SLC10A3 Is a Prognostic Biomarker and Involved in Immune Infiltration and Programmed Cell Death in Lower Grade Glioma
Weibo Ma and Pengying Mei
BACKGROUND: The association between SLC10A3 (so- lute carrier family 10 member 3) and lower grade glioma (LGG) remains unclear.
METHODS: We used public databases and bioinformatics analysis to analyze SLC10A3. These included The Cancer Genome Atlas, Genotype-Tissue Expansion, Chinese Glioma Genome Atlas, Human Protein Atlas, GeneCards, cBioPortal, Search Tool for the Retrieval of Interacting Genes/Proteins, Gene Expression Profiling Interactive Analysis, Tumor Im- mune Estimation Resource, Tumor-Immune System Interac- tion Database, receiver operating characteristic curve analysis, Kaplan-Meier analysis, Cox analysis, nomograms, calibration plots, gene ontology/Kyoto Encyclopedia of Genes and Genomes enrichment analysis, gene set enrich- ment analysis, single-sample gene set enrichment analysis, and Spearman’s correlation analysis.
RESULTS: SLC10A3 was upregulated in adrenocortical carcinoma, glioblastoma, and LGG and was associated with good overall survival (OS) in adrenocortical carci- noma and poor OS in LGG and glioblastoma. SLC10A3 was increased with increased World Health Organization grade, upregulated in isocitrate dehydrogenase-wild type, 1p/19q (chromosome arms 1p and 19q) non-co-deleted, and higher in astrocytoma. Patients with LGG were grouped by the occurrence of the clinical outcome endpoints (i.e., OS, disease-specific survival [DSS], and progression-free in- terval events). Genetic alterations in SLC10A3 were asso- ciated with poor progression-free survival in LGG. Most of clinical characteristics were associated with the SLC10A3 expression level. SLC10A3 with diagnostic and prognostic value (OS, DSS, and progression-free interval) was an in- dependent prognostic factor in LGG. Moreover, Nomograms (WHO grade, 1p/19q codeletion, age and SLC10A3) had
Key words
Biomarker
Immune infiltration
Lower grade glioma
Programmed cell death
SLC10A3
Abbreviations and Acronyms
1p/19q: Chromosome arms 1p and 19q
ACC: Adrenocortical carcinoma
aDC: Activated dendritic cell
AUC: Area under the curve
C-index: Concordance index
CI: Confidence interval
TIL: Tumor-infiltrating lymphocyte
TIMER: Tumor immune estimation resource
TISIDB: Tumor-immune system interaction database
GC: Guanine-cytosine
GEPIA2: Gene Expression Profiling Interactive Analysis
GO: Gene Ontology
GSEA: Gene set enrichment analysis
GTEx: Genotype-Tissue Expression
HR: Hazard ratio
iDC: Immature dendritic cell
IDH: Isocitrate dehydrogenase
KEGG: Kyoto encyclopedia of genes and genomes
LGG: Lower grade glioma
Mut: Mutant
NES: Normalized enrichment score
NK: Natural killer
OS: Overall survival
PD: Progressive disease
pDC: Plasmacytoid dendritic cell
PFI: Progression-free interval
PPI: Protein-protein interaction
ROC: Receiver operating characteristic
SLC: Solute carrier
SLC10: Solute carrier family 10
SLC10A3: Solute carrier family 10 member 3
SSGSEA: Single-sample gene set enrichment analysis
STRING: Search tool for the retrieval of interacting genes/proteins
TAM: Tumor-associated macrophage
TCGA: The cancer genome atlas
Th: T helper
DSS: Disease-specific survival
GBM: Glioblastoma multiforme
TME: Tumor microenvironment
WHO: World health organization
WT: Wild type
Fujian Provincial Key Laboratory of Plant Functional Biology, College of Life Sciences, Fujian Agriculture and Forestry University, Fuzhou, China
Citation: World Neurosurg. (2023) 178:e595-e640.
https://doi.org/10.1016/j.wneu.2023.07.134
Journal homepage: www.journals.elsevier.com/world-neurosurgery
Available online: www.sciencedirect.com
1878-8750/$ - see front matter @ 2023 Elsevier Inc. All rights reserved.
moderately accurate predictive for OS and DSS. Functional analysis showed that SLC10A3 might participate in the transport of multiple substances, neurogenic signaling, immune response, and programmed cell death in LGG. SLC10A3 correlated with immune infiltration in LGG and moderately correlated with the gene signature of pyropto- sis, lysosome-dependent cell death, necroptosis, apoptosis, ferroptosis, alkaliptosis, and autophagy-dependent cell death.
CONCLUSIONS: SLC10A3 is a potential diagnostic and prognostic biomarker for LGG and might be associated with substance transport, neurogenic signaling, immune infil- tration, and programmed cell death in LGG.
INTRODUCTION
G lioma originates from the glial cells and is a prevalent tumor type in the central nervous system. Lower grade gliomas (LGGs) comprise World Health Organization (WHO) grades II and III,’ are less often malignant, and have superior survival outcomes compared with glioblastoma multiforme (GBM; WHO grade IV). However, >70% of LGGs can dedifferentiate and progress to GBM2-4; thus, the challenge remains for doctors to increase the cure rate for patients with LGG.5 The molecular markers of glioma play an important role in improving the accuracy of diagnosis and treatment. The isocitrate dehydrogenase (IDH) mutation and co-deletion of chromosome arms Ip and 199 (Ip/19q co-deletion) have been integrated into the WHO classification to illustrate the histological characteristics and guide therapeutic strategies.1,6-8 Therefore, identifying new and effective molecular markers with the potential to serve as diag- nostic, prognostic, and potential therapeutic targets for LGG is essential to guide comprehensive treatment strategies and improve outcomes.
The solute carrier (SLC) family 10 (SLC10) contains 7 members (SLCIOAI-SLC10A7) and includes influx transporters of bile acids, steroidal hormones, specific drugs, and a variety of other substrates.9 Of the SCL10 members, SLC10A1, SLC10A2, and SLC10A6 have been functionally characterized, although the other members, including SLC10A3, are still orphan carriers.10-12 Current research suggests that some SLC10 family members could be promising therapeutic targets for many diseases. SLC10A1 has become a valuable target for drug development strategies for hepatitis B virus/hepatitis D virus,13 and SLC10A2 has become a promising target for the treatment of liver, gallbladder, intestinal, and metabolic diseases.14 The SLC10A3 gene maps to a GC (guanine-cytosine)-rich region of the X chromosome and is ubiquitously expressed in the placenta, small intestine, pancreas, cervix, kidney, uterus, and brain neuroblastoma. Recently, RNA sequencing of polyploid cancer cells showed SLC10A3 might be a drug-resistant gene,15 and SLC10A3 exhibited a significant relationship with immune cells and correlated with poor overall survival (OS) for those with liver cancer.16 However, the biological function and substrate
specificity of SLC10A3 in LGG remains unclear. Thus, we analyzed the mRNA and protein expression levels of SLC10A3 and genetic alterations of SLC10A3 in LGG. We also assessed the progression-free survival (PFS) in SLC10A3 with and without genetic alterations and the diagnosis and prognostic value of SLC10A3 mRNA expression. In addition, we analyzed the SLC10A3-related protein-protein interaction (PPI) network using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING). We obtained SLC10A3-correlated genes via Gene Expression Profiling Interactive Analysis (GEPIA2). We performed gene ontology (GO)/Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene set enrichment analysis (GSEA) to investigate the underlying biological function of SLC10A3. We also performed single-sample gene set enrichment analysis (ssGSEA), Tumor Immune Estimation Resource (TIMER) analysis, and Tumor-Immune System Interaction Database (TISIDB) analysis to identify the degree of correlation of SLC10A3 mRNA expression with immune infiltration in patients with LGG. Finally, we per- formed a correlation analysis of SLC10A3 and programmed cell death-related genes using R, version 3.6.3 (R Foundation for Statistical Computing, Vienna, Austria) and GEPIA2.
METHODS
Data Resources
The RNAseq data in transcripts per million reads format for 33 cancers from The Cancer Genome Atlas (TCGA) and corre- sponding normal tissues data from the Genotype-Tissue Expan- sion (GTEx) Program, processed uniformly using the Toil process,17 were downloaded from University of California Santa Cruz Xena (available at: https://xenabrowser.net/datapages/) for expression difference analysis of SLC10A3 in tumor and normal tissues. The clinical information and RNAseq data were obtained from the TCGA LGG Project (available at: https:// portal.gdc.cancer.gov/). The clinical information for WHO grade, IDH status, and Ip/19q co-deletion were obtained from the study by Ceccarelli et al.18 The formatted RNAseq data were converted into transcripts per million reads format for subsequent analysis.
Analysis of SLC10A3 Using Online Analysis Tools
The GEPIA2 (available at: http://gepia2.cancer-pku.cn/#index) was used to analyze the effect of SLC10A3 expression on OS in pan-cancers. The Chinese Glioma Genome Atlas (available at: http://www.cgga.org.cn) was used to analyze the expression of SLC10A3 and the effect of SLC10A3 expression on survival. The Human Protein Atlas (available at: http://www.proteinatlas.org/) was used to obtain immunohistochemical images of SLC10A3 protein. The 3-dimensional structure from AlphaFold (predicted) for the SLC10A3 gene was obtained from GeneCards (available at: https://www.genecards.org/). The cBioPortal database (available at: http://www.cbioportal.org/) was used to analyze gene muta- tions of SLC10A3 and the effect of SLC10A3 mutations on PFS in brain LGG (TCGA, PanCancer Atlas).
PPI and Enrichment Analyses
The STRING website (available at: https://string-db.org/) was used to construct the PPI network. The minimum required interaction
score was set to low confidence (0.150), and the maximum number of interactors was set to no >50 interactors in first shell. The similar gene detection module of GEPIA2 was used to obtain the top 100 SLC10A3-correlated targeting genes from the datasets of LGG tumor tissue. Next, these 2 sets of data were used to perform GO/KEGG analysis via the clusterProfiler package.19 The biological pathway differences between the high SLC10A3 expression group and low SLC10A3 expression group, which were grouped by the median expression of SLC10A3, was determined using GSEA,20 and c2.cp.v7.2. symbols.gmt [Curated] was used as a reference gene set. Pathways with a false discovery rate of < 0.25 and adjusted P value of < 0.05 were generally considered to represent significant enrichment.
Immune Infiltration Analysis
The correlations between the expression level of SLC10A3 and the infiltration level of 24 different types of immune cells in the tumor microenvironment (TME) of LGG were quantified using the GSVA (gene set variation analysis) package with the ssGSEA algo- rithm.21,22 The correlations between the expression of SLC10A3 and enrichment of macrophages, neutrophils, eosinophils, aDCs, cytotoxic cells, and NK cells were visualized by scatter plots. The enrichment scores of 24 types of immune cells in the 2 groups, grouped by the median expression of SLC10A3, were also analyzed by ssGSEA. The correlation of SLC10A3 expression with the abundance of 6 types of infiltrating immune cells (i.e., B cells, CD8+ T cells, CD4+ T cells, macrophages, neutrophils, and dendritic cells) and the correlation between the marker genes of tumor immune infiltrating cells acquired from previous studies and SLC10A3 were analyzed in the TIMER database (available at: http://cistrome.org/TIMER/).23-25 The correlation between SLC10A3 expression and tumor-infiltrating lymphocytes (TILs), SLC10A3 expression and immunomodulators, and SLC10A3 expression and chemokines (or receptors) were analyzed in the TISIDB (available at: cis.hku.hk/TISIDB/index.php).
Correlation Analysis of SLC10A3 and Programmed Cell Death
The programmed cell death-related genes were collected from review articles and previous research studies,26-37 including apoptosis, necroptosis, pyroptosis, ferroptosis, cuproptosis, par- thanatos, entotic cell death, netotic cell death, lysosome-dependent cell death, autophagy-dependent cell death, alkaliptosis, and oxeiptosis. The correlation between marker genes of programmed cell death and SLC10A3 was analyzed in R, version 3.6.3 (R Foundation for Statistical Computing). The cor- relation between the gene signature of programmed cell death (Supplementary Table 1) and SLC10A3 was analyzed in GEPIA2.
Statistical Analysis
The statistical analysis was performed using R, version 3.6.3 (R Foundation for Statistical Computing). The expression of SLC10A3 in the different groups was analyzed using the Mann-Whitney U test and Kruskal-Wallis test. The correlations of SLC10A3 mRNA expression and different clinical characteristics were evaluated using the x2 test and the Fisher exact test. The diagnostic value of SLC10A3 mRNA expression was evaluated using receiver operating characteristic (ROC) curves. Survival analyses of the data were first performed using Kaplan-Meier analysis via the log-rank test and
further evaluated using univariate and multivariate Cox analyses of LGG patients. The independent prognostic factors obtained from multivariate Cox regression analysis were used to establish no- mograms to predict the survival probability for 1-, 3-, and 5-year OS. The RMS package and survival package were used to generate nomograms that included significant clinical character- istics and calibration plots. A concordance index (C-index) was used to determine the discrimination of the nomograms. The correlation between the infiltration abundance of immune cells and expression level of SLC10A3 was examined using the Spearman correlation test. The correlation between the marker genes (immune cells and programmed cell death) and SLC10A3 was also analyzed using the Spearman test. The enrichment scores of immune cells in the 2 groups were examined using the Mann- Whitney U test, with P < 0.05 considered statistically significant for all analyses.
RESULTS
Expression and Prognosis Analysis of SLC10A3 in Pan-Cancers
mRNA expression analysis of SLC10A3 in pan-cancers based on the TCGA and GTEx databases showed that SLC10A3 was mark- edly upregulated in multiple types of cancers, including adreno- cortical carcinoma (ACC), GBM, and brain LGG (Figure 1A). Next, we used GEPIA2 to analyze the prognostic value of SLC10A3 with significant differential expression in pan-cancers (Figure 1B-D and Supplementary Figure 1). We found the expression of SLC10A3 had merely a prognostic role in ACC, GBM, and LGG. The results showed that high SLC10A3 expression predicted for good OS for ACC patients (Figure 1B) and poor OS for GBM and LGG patients, especially LGG patients (Figure 1C and D).
Expression, Structure, and Genetic Alterations Analysis of SLC10A3
With increases in the WHO grade, mRNA expression of SLC10A3 was significantly increased (Figure 2A). mRNA expression of SLC10A3 was higher in IDH-wild type (IDH-WT) than IDH- mutant (IDH-Mut) at different WHO grades (Figure 2B). At different WHO grades, mRNA expression of SLC10A3 was significantly stronger for 1p/19q non-co-deleted than 1p/19q co- deleted cancer (Figure 2C). Furthermore, SLC10A3 protein expression was higher in low- and high-grade glioma compared with that in normal tissue (Figure 2D). Structure prediction from the AlphaFold project of SLC10A3 was obtained from GeneCards (Figure 2E). The genetic alterations rate of SLC10A3 was 7% in LGG (Figure 2H). The alteration frequency of 3 categories (cancer type detailed) are shown based on filtering, and the alteration frequency of astrocytoma was the highest (Figure 2F). Finally, genetic alterations of SLC10A3 were associated with poor PFS (Figure 2G). These results suggest that SLC10A3 could have important clinical implications in LGG; thus, we used TCGA and GTEx data sets to further analyze SLC10A3.
mRNA Expression of SLC10A3 in LGG
mRNA expression of SLC10A3 in LGG tissues was significantly higher than that in normal tissues (P = 3.3e-80; Figure 3A). mRNA expression of SLC10A3 was also observed in different subgroups stratified by distinct clinical characteristics of LGG patients.
Same as the results from the online analysis tools, mRNA expression of SLC10A3 was higher for WHO G3 than for WHO G2 (P = I.Ie-10), higher for IDH-WT than for IDH-Mut (P = 9.8e-23), and stronger for 1p/19q non-co-deleted than for 1p/19q co-deleted (P = 2e-16; Figure 3B-D). The patients were grouped according to the occurrence of the following clinical outcome endpoints: OS, disease-specific survival (DSS), and progression- free interval (PFI) events. mRNA expression of SLC10A3 was higher in the patients who had died than in the patients who were alive when stratified by OS events (P = 3.4e-11; Figure 3F). mRNA expression of SLC10A3 was higher for patients with the clinical outcome endpoints of DSS (disease-specific death) and PFI (deterioration or death from tumor) than for the patients without these clinical outcome endpoints (P = 1.4e-10 and P = 1.1e-08, respectively; Figure 3G and H). Regarding the primary therapy outcome, mRNA expression of SLC10A3 in patients with progressive disease (PD) was higher than that in patients with stable disease and those with a complete response (P = 4.8e-04 and P = 1.9e-07, respectively; Figure 3E). Furthermore, when
stratified by histological type, we found that mRNA expression of SLC10A3 only in oligoastrocytoma and oligodendroglioma was significantly lower than that in astrocytoma (P = 6.5e-04 and P = 1.20-07, respectively; Figure 31). However, no statistically significant differences were found in SLC10A3 mRNA expression in patients stratified by laterality, age, gender, and race (Figure 3J-M).
Correlations Between mRNA Expression of SLC10A3 and LGG Clinical Characteristics
The Fisher exact test was used to evaluate whether mRNA expression of SLC10A3 correlated with race or laterality in those with LGG. Correlations between the other clinical characteristics and SLC10A3 expression in LGG were evaluated using the x2 test. The LGG patients were divided into 2 groups according to the median expression of SLC10A3. The results showed that mRNA expression of SLC10A3 was significantly associated with the WHO grade (P < 0.001), IDH status (P < 0.001), 1p/19q co-deletion (P < 0.001), primary therapy outcome (P < 0.001), race (P = 0.022),
A
ns
ns
ns
ns
ns
The expression of SLC10A3 Log2 (TPM+1)
8
6
M
₿ Normal
I
E
Tumor
4
2
2
ACC
BLCA
BRCA
CESC
CHOL
COAD
ESCA
GBM
HNSC
KICH
KIRC
KIRP
LAML
LGG
LIHC
LUAD
LUSC
MESO
OV
PAAD
PCPG
PRAD
READ
SARC
SKCM
STAD
TGCT
THCA
THYM
UCEC
UCS
UVM
[
B
ACC Overall Survival
C
GBM Overall Survival
D
LGG Overall Survival
1.0
Low SLC10A3 Group
1.0
High SLC10A3 Group
Low SLC10A3 Group
1.0
Low SLC10A3 Group
Logrank p=0.04
High SLC10A3 Group
High SLC10A3 Group
HR(high)=0.44
Logrank p=0.011
Logrank p=1e-07
0.8
p(HR)=0.045
0.8
HR(high)=1.6
n(high)=38
p(HR)=0.011
0.8
HR(high)=2.8
n(high)=81
p(HR)=3.3e-07
Percent survival
Percent survival
n(low)=81
Percent survival
n(high)=256
0.6
n(low}=38
0.6
0.6
n(low)=252
0.4
0.4
0.4
0.2
0.2
0.2
0.0
0.0
0.0
0
50
100
150
0
20
40
60
80
0
50
100
150
200
Months
Months
Months
A
B
Gene expression of SLC10A3
Anova, p=4.6e-24
Gene expression of SLC10A3
WHO II
WHO III
WHO IV
T-test, p=0.025
T-test, p=0.034
T-test, p=1.6e-10
3.
3.
2.
2
1.
WHO II
WHO III Grade
WHO IV
1.
Mutant
Wildtype
Mutant Wildtype IDH mutation status
Mutant
Wildtype
C
D
Gene expression of SLC10A3
WHO II
WHO III
WHO IV
Cerebral cortex
Low grade glioma
High grade glioma
T-test, p=2.5e-06
T-test, p=8.8e-11
T-test, p=0.00028
3.
SLC10A3
2.
1.
Codel
Non-codel
Codel
Non-codel
Codel
Non-codel
1p/19q co-deletion status
Staining: Not detected
Medium
Medium
Antibody HPA021656
E
F
G
Model Confidence:
Alteration Frequency
10%
· Mutation
100%
Very high (pLDDT > 90)
· Amplification
Confident (90 > pLDDT > 70)
8%
· mRNA High
· mRNA Low
90%
Low (70 > pLDDT > 50)
80%
Very low (pLDDT < 50)
6%
Progression Free
70%
4%
60%
2%
50%
Structural variant data Mutation data CNA data mRNA data Protein data
40%
30%
20%
Progression Free
Altered group
10%
Unaltered group
Astrocytoma
Oligoastrocytoma Oligodendroglioma
0%
Logrank Test P-Value: 5.155e-3
0
10 20
30
40
50
60
70
80
90 10011
101 1201 13 30 140
15016 160
170
Progress Free Survival (Months)
H
Profiled in Protein expression z-scores (RPPA)
Profiled in Putative copy-number alterations from GISTIC
SLC10A3
7%
Missense Mutation (unknown significance)
Amplification
mRNA High
mRNA Low
No alterations
Genetic Alteration
Profiled in Protein expression z-scores (RPPA)
Yes - No
Profiled in Putative copy-number alterations from GISTIC
Yes
No
high-grade glioma using the Human Protein Atlas. (E) Three-dimensional structure from AlphaFold (predicted) for SLC10A3 gene in GeneCards. (F) Alteration frequency of cancer type detailed in cBioPortal. (G) Kaplan-Meier curve of progression-free survival based on genetic alterations of SLC10A3 in cBioPortal. (H) Analysis of genetic alterations in SLC10A3 gene in lower grade glioma by cBioPortal.
A
B
C
D
The expression of SLC10A3 Log2 (TPM+1)
5
The expression of SLC10A3 Log2 (TPM+1)
9
The expression of SLC10A3 Log2 (TPM+1)
0
The expression of SLC10A3 Log2 (TPM+1)
6
4
0
en
5
3
A
4
4
2
1
3
3
3
0
G2
G3
Mut
WT
codel
non-codel
Normal
Tumor
WHO grade
IDH status
1p/19q codeletion
E
F
G
H
The expression of SLC10A3 Log2 (TPM+1)
8
The expression of SLC10A3 Log2 (TPM+1)
6
The expression of SLC10A3 Log2 (TPM+1)
6
The expression of SLC10A3 Log2 (TPM+1)
6
7
IS
6
5
5
5
5
4
A
4
4
3
3
3
3
2
SD
PR
CR
PD
Alive
Dead
NO
Yes
NO
Yes
Primary therapy outcome
OS event
DSS event
PFI event
J
K
L
The expression of SLC10A3 Log2 (TPM+1)
7
The expression of SLC10A3 Log2 (TPM+1)
7
ns
The expression of SLC10A3 Log2 (TPM+1)
6
ns
The expression of SLC10A3 Log2 (TPM+1)
ns
6
6
6
TS
9
5
5
5
4
4
4
4
3
3
3
3
2
2
Oligoastrocytoma
Oligodendroglioma Astrocytoma Histological type
Left
Midline
Right
⇐ 40
>40
Male
Female
Laterality
Age
Gender
M
The expression of SLC10A3 Log2 (TPM+1)
6
ns
5
4
3
Asian
Black or African American Race
| Table 1. Relationship Between mRNA Expression of SLC10A3 and Different Clinical Characteristics in LGG | |||
|---|---|---|---|
| Characteristic | SLC10A3 Expression | P Value | |
| Low | High | ||
| Samples (n) | 264 | 264 | |
| WHO grade | < 0.001* | ||
| G2 | 145 (31) | 79 (16.9) | |
| G3 | 91 (19.5) | 152 (32.5) | |
| IDH status | < 0.001* | ||
| WT | 14 (2.7) | 83 (15.8) | |
| Mutant | 248 (47.2) | 180 (34.3) | |
| 1p/19q co-deletion | < 0.001* | ||
| Yes | 125 (23.7) | 46 (8.7) | |
| No | 139 (26.3) | 218 (41.3) | |
| Primary therapy outcome | < 0.001* | ||
| PD | 40 (8.7) | 70 (15.3) | |
| SD | 71 (15.5) | 75 (16.4) | |
| PR | 31 (6.8) | 33 (7.2) | |
| CR | 88 (19.2) | 50 (10.9) | |
| Gender | 0.727 | ||
| Female | 117 (22.2) | 122 (23.1) | |
| Male | 147 (27.8) | 142 (26.9) | |
| Race | 0.022* | ||
| Asian | 5 (1) | 3 (0.6) | |
| Black or African-American | 5 (1) | 17 (3.3) | |
| White | 249 (48.2) | 238 (46) | |
| Age (years) | 0.433 | ||
| ≤40 | 137 (25.9) | 127 (24.1) | |
| >40 | 127 (24.1) | 137 (25.9) | |
| Histological type | < 0.001* | ||
| Astrocytoma | 67 (12.7) | 128 (24.2) | |
| Oligoastrocytoma | 69 (13.1) | 65 (12.3) | |
| Oligodendroglioma | 128 (24.2) | 71 (13.4) | |
| Laterality | 0.474 | ||
| Left | 122 (23.3) | 134 (25.6) | |
| Midline | 2 (0.4) | 4 (0.8) | |
| Right | 135 (25.8) | 126 (24.1) | |
| OS event | < 0.001* | ||
Data presented as n (%).
SLC10A3, solute carrier family 10 member 3; LGG, lower grade glioma; WHO, world health organization; G, grade; WT, wild type; Mut, mutant; PD, progressive disease; SD, stable disease; PR, partial response; CR, complete response; OS, overall survival; PFI, progressive-free interval. *Statistically significant. Continues
| Table 1. Continued | |||
|---|---|---|---|
| Characteristic | SLC10A3 Expression | P Value | |
| Low | High | ||
| Alive | 224 (42.4) | 168 (31.8) | |
| Dead | 40 (7.6) | 96 (18.2) | |
| DSS event | < 0.001* | ||
| No | 226 (43.5) | 171 (32.9) | |
| Yes | 36 (6.9) | 87 (16.7) | |
| PFI event | < 0.001* | ||
| No | 183 (34.7) | 135 (25.6) | |
| Yes | 81 (15.3) | 129 (24.4) | |
Data presented as n (%).
SLC10A3, solute carrier family 10 member 3; LGG, lower grade glioma; WHO, world health organization; G, grade; WT, wild type; Mut, mutant; PD, progressive disease; SD, stable disease; PR, partial response; CR, complete response; OS, overall survival; PFI, progressive-free interval.
*Statistically significant.
histological type (P < 0.001), OS event (P <0.001), DSS event (P < 0.001), and PFI event (P <0.001; Table 1). However, the results showed that correlations with gender, age, and laterality were not significantly different between the high and low expression groups (Table 1).
Diagnostic and Prognostic Value of mRNA Expression of SLC10A3 in LGG
The ROC curves showed that mRNA expression of SLC10A3 has moderate diagnostic value to distinguish between normal and cancerous tissues (area under the curve [AUC], 0.789), IDH-Mut and IDH-WT (AUC, 0.819), and 1p/19q non-co-deleted and 1p/ 19q co-deleted (AUC, 0.721; Figure 4A-C). The results of the Kaplan-Meier analysis showed that high mRNA expression of SLC10A3 correlated with poor OS, DSS, and PFI for those with LGG (P < 0.001; Figure 4D-F). Univariate Cox regression analysis revealed that the expression of SLC10A3 was associated with poor OS (hazard ratio [HR], 3.017; 95% confidence interval [CI], 2.075- 4.386; P < 0.001), DSS (HR, 3.115; 95% CI, 2.100-4.622; P < 0.001), and PFI (HR, 2.125; 95% CI, 1.603-2.817; P < 0.001) in LGG (Table 2). Multivariate Cox regression analysis was performed with WHO grade, 1p/19q co-deletion, age, and histo- logical type and showed that SLC10A3 expression was still an in- dependent factor that correlated with poor OS (HR, 1.898; 95% CI, I.214-2.965; P = 0.005) and poor DSS (HR, 1.868; 95% CI, 1.159- 3.012; P = 0.010; Table 2). Multivariate Cox regression analysis performed with WHO grade, 1p/19q co-deletion, age, histologi- cal type, and laterality showed that SLC10A3 expression was still an independent factor that correlated with a short PFI (HR, 1.664; 95% CI, 1.200-2.306; P = 0.002; Table 2). These results indicated the mRNA expression of SLC10A3 plays a crucial role in the diagnostic and prognostic assessments of LGG patients.
Construction and Validation of mRNA Expression of SLC10A3 in LGG-Based Nomograms
The independent clinical risk factors (WHO grade, 1p/19q co- deletion, age, and SLC10A3 expression) from the multivariate Cox regression analysis were used to construct a prognostic nomogram, and a calibration curve was drawn to test the effi- ciency of the nomogram. The nomograms illuminated that the C- index of the OS model was 0.780 (95% CI, 0.758-0.802; Figure 5A). The C-index of the DSS model was 0.786 (95% CI, 0.763-0.809; Figure 5C), and the C-index of the PFI model was 0.693 (95% CI, 0.673-0.713; Figure 5E), suggesting that the prediction efficiency of the OS and DSS models are moderately accurate and the prediction efficiency of the PFI model has low accuracy. The calibration plot showed that the model calibration line was close to the ideal calibration line (45° line; Figure 5B, D and F), which showed a fine agreement between the prediction and the observation. These results suggest that the nomograms can well predict the short- and long-term survival of LGG patients.
Functional Analysis of SLC10A3
The 50 proteins that may interact with SLC10A3 are shown in Figure 6A. The top 100 SLC10A3-correlated targeting genes based on the datasets of LGG tumor tissues were obtained from GEPIA2. The GO/KEGG enrichment analysis of these 2 data sets showed that these genes are mainly related to substance transport, im- munity, and apoptosis (Figure 6B-E). The results of the GO/ KEGG enrichment analysis related to substance transport are shown in Supplementary Table 2. These results with adjusted P values < 0.05 are also presented in Supplementary Figure 2. GSEA was used to analyze the SLC10A3-related signaling path- ways in LGG. We selected the most significantly enriched signaling pathways according to their normalized enrichment score (NES; Figure 7A and B). The top 16 NESs showed that most are immune signaling pathways (Figure 7A), such as innate
A
Tumor vs Normal
B
Mut vs WT
C
non-codel vs codel
1.0
1.0
1.0
0.8
0.8
0.8
Sensitivity (TPR)
Sensitivity (TPR)
Sensitivity (TPR)
0.6
0.6
0.6
0.4
0.4
0.4
0.2
SLC10A3
0.2
SLC10A3
0.2
SLC10A3
AUC: 0.789
AUC: 0.819
AUC: 0.721
0.0
CI: 0.767-0.810
0.0
CI: 0.768-0.870
0.0
CI: 0.677-0.765
0.0
0.2
0.4
0.6
0.8
1.0
0.0
0.2
0.4
0.6
0.8
1.0
0.0
0.2
0.4
0.6
0.8
1.0
1-Specificity (FPR)
1-Specificity (FPR)
1-Specificity (FPR)
D
E
F
1.0
SLC10A3
1.0
SLC10A3
1.0
SLC10A3
Low
Low
Low
High
High
High
Survival probability
0.8
Survival probability
0.8
Survival probability
0.8
0.6
0.6
0.6
0.4
0.4
0.4
0.2
Overall Survival
0.2
Disease Specific Survival
0.2
Progress Free Interval
HR = 2.92 (2.09-4.10)
HR = 3.01 (2.11-4.30)
HR = 2.09 (1.59-2.74)
0.0
Log-rank P < 0.001
0.0
Log-rank P < 0.001
0.0
Log-rank P < 0.001
0
50
100
150
200
0
50
100
150
200
0
50
100
150
Time (months)
Time (months)
Time (months)
immune system-related pathways (including creation of C4 and C2 activators, Fc gamma receptor activation, initial triggering of complement, complement cascade role of phospholipids in phagocytosis, role of LAT2/NTAL/LAB on calcium mobilization, FCERI-mediated mitogen-activated protein kinase activation, and FCERI-mediated Ca2+ mobilization) and adaptive immune system-related pathways (including immunoregulatory in- teractions between lymphoid and nonlymphoid cells, CD22- mediated B-cell receptor regulation, and antigen activated B-cell receptors leading to generation of second messengers). Further- more, scavenging of heme from plasma and binding and uptake of ligands by scavenger receptors belong to vesicle-mediated trans- port-related pathways and were differentially enriched in the SLC10A3 high-expression phenotype. The bottom 16 NESs showed that most are neuronal system-related pathways (Figure 7B), such as PPIs at synapse-related pathways (including PPIs at synapses, neurexins, and neuroligins), potassium channel-related pathways (including voltage-gated potassium channels, and potassium channels), and transmission across chemical synapse-related
pathways (including glutamate neurotransmitter release cycle, transmission across chemical synapses, dopamine neurotrans- mitter release cycle, serotonin neurotransmitter release cycle, long-term potentiation, neurotransmitter release cycle; trafficking of AMPA receptors, GABA synthesis, GABA release, GABA reup- take, GABA degradation, unblocking of NMDA receptors, and glutamate binding and activation). Moreover, the synaptic vesicle pathway was also differentially enriched in the SLC10A3 high- expression phenotype. These results indicate that highly expressed SLC10A3 might be significantly involved in the immune response, neurogenic signaling, and vesicle-mediated transport in LGG.
Correlation Between SLC10A3 Expression and Tumor Immune Infiltrating Cells
Because the GO/KEGG and GSEA analysis results indicated that SLC10A3 is involved in regulating immune-related pathways, we further analyzed the correlation between SLC10A3 expression and immune infiltration using ssGSEA and TIMER. The ssGSEA
| Table 2. Cox Regression Analysis for Clinical Outcomes in LGG Patients | ||||||
|---|---|---|---|---|---|---|
| HR for OS (95% CI) | HR for DSS (95% CI) | HR for PFI (95% CI) | ||||
| Characteristic | Univariate | Multivariate | Univariate | Multivariate | Univariate | Multivariate |
| WHO grade (G3 vs. G2) | 3.059* | 2.495* | 3.145* | 2.530* | 1.630+ | 1.306 |
| 1p/19q co-deletion (no vs. yes) | 2.493* | 2.483+ | 2.861* | 2.760+ | 2.313* | 2.417* |
| Gender (male vs. female) | 1.124 | e | 1.084 | e | 0.887 | e |
| Race (white vs. Asian and black) | 0.849 | e | 0.796 | e | 0.899 | e |
| Age (>40 vs. ≤40 years) | 2.889* | 3.554* | 2.991* | 3.762* | 1.889* | 2.091* |
| Histological type (oligoastrocytoma vs. astrocytoma) | 0.661 | 1.253 | 0.606± | 1.159 | 0.574+ | 0.818 |
| Histological type (oligodendroglioma vs. astrocytoma) | 0.577+ | 1.238 | 0.541+ | 1.209 | 0.633+ | 1.269 |
| Laterality (right vs. left and midline) | 0.770 | e | 0.815 | e | 0.791 | 0.822 |
| SLC10A3 (high vs. low) | 3.017* | 1.898+ | 3.115* | 1.868± | 2.125* | 1.664+ |
LGG, lower grade glioma; HR, hazard ratio; OS, overall survival; CI, confidence interval; DSS, disease-specific survival; PFI, progression-free interval; WHO, world health organization; G, grade; SLC10A3, solute carrier family 10 member 3.
*P < 0.001.
+P < 0.01.
įP < 0.05.
showed the enrichment scores of macrophages, neutrophils, eo- sinophils, aDCs, cytotoxic cells, NK cells, T helper (Th)17 cells, T cells, immature DCs (iDCs), NK CD56dim cells, Th cells, and Th2 cells were significantly higher in the high SLC10A3 expression group than in the low SLC10A3 expression group. The enrichment scores of plasmacytoid DCs (pDCs), NK CD56bright cells, and regulatory T cells were significantly lower in the high SLC10A3 expression group than in the low SLC10A3 expression group (Figure 8A). In agreement, the lollipop diagram of ssGSEA showed that SLC10A3 expression correlated positively with the abundance of macrophages, neutrophils, eosinophils, aDCs, cytotoxic cells, NK cells, Th17 cells, T cells, iDCs, NK CD56dim cells, Th cells, and Th2 cells and correlated negatively with the abundance of PDCs, NK CD56bright cells, and regulatory T cells (Figure 8B). Scatter plots of the ssGSEA results showed the top 6 correlations for the absolute value between SLC10A3 expression and macrophages, neutrophils, eosinophils, aDCs, cytotoxic cells, and NK cells, respectively (Figure 8C-H). The results from TIMER database showed that the expression of SLC10A3 correlated positively with the infiltrating levels of B cells (r = 0.456; P = 6.28e-26), CD8+ T cells (r = 0.250; P =3.08e-08), CD4+ T cells (r = 0.547; P = 1.700-38), macrophages (r = 0.562; P = 9.36e-41), neutrophils (r = 0.606; P = 6.19e-49), and dendritic cells (r = 0.593; P = 1.67e-46; Figure 9). Furthermore, mRNA expression of SLC10A3 correlated with expression of most of the marker genes for various immune cells after correlation adjusted by tumor purity (Supplementary Table 3). The results from TISIDB also suggest that the expression of SLC10A3 was associated positively with most of the TILs, immunomodulators, chemokines, and receptors in human cancers, in particular, in LGG and GBM (Figure 10). These results indicate that SLC10A3 might participate in regulating the TME of LGG patients.
Correlation Between SLC10A3 Expression and Programmed Cell Death
Because the GO/KEGG analysis results indicated that SLC10A3 is involved in regulating apoptosis, we further found cell death- related pathways from the GO/KEGG analysis and GSEA, with enriched pathways with adjusted P values < 0.05 shown in Figure 11. We found that most are programmed cell death- associated pathways, such as apoptosis, apoptosis modulation, apoptosis signaling, caspase pathway, caspase activation via death receptors in the presence of ligand, RIPK1-mediated regulated necrosis, programmed cell death, caspase activation via extrinsic apoptotic signaling pathways, and so forth. Thus, we further analyzed co-expression between SLC10A3 expression and pro- grammed cell death-related genes. mRNA expression of SLC10A3 correlated with the expression of most of the marker genes of apoptosis, necroptosis, pyroptosis, ferroptosis, cuproptosis, par- thanatos, entotic cell death, netotic cell death, lysosome- dependent cell death, autophagy-dependent cell death, alka- liptosis, and oxeiptosis (Figure 12). The correlation analysis of the gene signature of programmed cell death and SLC10A3 from GEPIA2 showed SLC10A3 correlated positively with the gene signature of programmed cell death and moderately with the gene signature of pyroptosis, lysosome-dependent cell death, necroptosis, apoptosis, ferroptosis, alkaliptosis, and autophagy- dependent cell death (Figure 13). These results indicate that SLC10A3 expression could play an important role in regulating programmed cell death in LGG.
DISCUSSION
SLC10A3 is a member of SLC10 family. At present, the clinical diagnosis and prognosis and potential function of SLC10A3 in LGG remain unclear. We analyzed the expression and prognostic value of SLC10A3 in pan-cancers and found SLC10A3 was
A
B
Points
0
20
40
60
80
100
Observed fraction survival probability
1.0
WHO grade
G3
0.8
1p/19q codeletion
G2
non-codel
Age
codel
>40
0.6
SLC10A3
⇐ 40
High
Total Points
Low
0.4
Linear Predictor
0
100
200
300
1-year Survival Probability
2
-1
0
1
2
0.2-
1-Year
3-Year
3-year Survival Probability
0.95
0.9 0.850.8
5-Year
Ideal line
5-year Survival Probability
0.9
0.8
0.7 0.60.50.40.3
0.0
0.8
0.6
0.4
0.2
0.0
0.2
0.4
0.6
0.8
1.0
Nomogram predicted survival probability
C
D
100
1.0
Observed fraction survival probability
0.8
>40
0.6
0.4
0.2
1-Year
3-Year
5-Year
0.0
Ideal line
0.0
0.2
0.4
0.6
0.8
1.0
Nomogram predicted survival probability
E
F
Points
0
20
40
60
80
100
Observed fraction survival probability
1.0
WHO grade
G3
0.8
1p/19q codeletion
G2
non-codel
Age
codel
>40
0.6
SLC10A3
⇐ 40
High
Total Points
Low
0.4
Linear Predictor
0
100
200
300
-1.4
-1
-0.6
-0.2
0.2
0.6
1
0.2
1-year Survival Probability
1-Year
3-Year
3-year Survival Probability
0.9
0.8
0.7
0.6
5-Year
5-year Survival Probability
0.8
0.6
0.4
0.2
0.0
Ideal line
0.8
0.6
0.4
0.2
0.0
0.2
0.4
0.6
0.8
1.0
Nomogram predicted survival probability
Figure 5. Construction and validation of nomograms based on solute carrier family 10 member 3 (SLC10A3) mRNA expression. Nomograms constructed to establish SLC10A3 expression-based risk scoring
models for 1-, 3-, and 5-year overall survival (OS; A), disease-specific survival (DSS; C), and progression-free interval (PFI; E). Calibration plots validating efficiency of nomograms for OS (B), DSS (D), and PFI (F).
| 0 20 | 40 | 60 | 80 | |
|---|---|---|---|---|
| Points WHO grade | G3 | |||
| 1p/19q codeletion | G2 codel | non-codel | ||
| Age | <= 40 | High | ||
| SLC10A3 | ||||
| Low | ||||
| Total Points | ||||
| 0 | 100 | 200 | 300 | |||||
|---|---|---|---|---|---|---|---|---|
| Linear Predictor -2.5 | -1.5 | -0.5 | 0.5 | 1.5 | ||||
| 1-year Survival Probability | 0.95 | 0.9 0.850.8 | ||||||
| 3-year Survival Probability | 0.9 | 0.8 0.7 0.60.50.40.3 | ||||||
| 5-year Survival Probability | 0.8 | 0.6 0.4 0.2 | ||||||
significantly upregulated in ACC, GBM and LGG. Moreover, high expression of SLC10A3 predicted for good OS in ACC and poor OS in GBM and LGG, especially LGG. Thus, we investigated the potential role of SLC10A3 in LGG. We analyzed the structure of SLC10A3, the expression of SLC10A3 in glioma, and the genetic alteration of SLC10A3 in LGG. Next, we further analyzed the expression, function, and clinical value of SLC10A3 in LGG. First, we analyzed the mRNA expression of SLC10A3 and the relation- ship between SLC10A3 expression and different clinical charac- teristics. Second, we analyzed the diagnostic and prognostic value of SLC10A3 expression in LGG. Finally, we analyzed the potential function of SLC10A3 expression and the effect of SLC10A3
expression on immune infiltration and programmed cell death in LGG.
In the present study, we found mRNA expression of SLC10A3 increased with increasing WHO grade and was significantly upregulated in IDH-WT and 1p/19q non-co-deleted glioma. Moreover, the protein expression of SLC10A3 was higher in glioma than in normal tissue. Also, the incidence of genetic alterations in SLC10A3 was 7% in LGG and was associated with poor PFS. Therefore, SLC10A3 expression could have important clinical im- plications. We further analyzed SLC10A3 in LGG. SLC10A3 expression in LGG was higher in tumor tissue, WHO G2, IDH- WT, 1p/19q non-co-deleted, PD, and when stratified by clinical
A
TMEM199
7
HLATLE
GOLPH3
9
RGAGA
y
1GJ
FAM1278
1
PSMD10
A
A
MFSDI
ZC4H2
MORE 4L2
ABOC2
MOSPDI
SLC 502
&
SLC1041
MisLa
SLC3502
SLCOSAS
A
HTATSFI
SLCREMA
icina
MESOT
SLCS:
$
UBL4A
LC17A5
TMEM148
=
TTC18
LC25A25
C11A2
SLC1543
SLC4A10
TEX261
KHI
TMBEST20
=
İLCEZAL
BPIFRI
SUC12A2
GPCPDI
3
SC
MICU
SPOIL
KGNJ18
WINK3
SLC12A3
0
B
C
Pathogenic Escherichia coli infection
organic anion transport
Salmonella infection
Apoptosis
neutrophil mediated immunity
Lysosome
p.adjust
0.08
neutrophil activation
Tuberculosis
p.adjust
0.06
Tight junction
0.04
neutrophil activation involved in immune response
7.5e-05
5.0e-05
TNF signaling pathway -
0.02
neutrophil degranulation
2.5e-05
NOD-like receptor signaling pathway-
Counts
Bile secretion -
4
lipid transport
Counts ☐
6
15
C-type lectin receptor signaling pathway
8
carboxylic acid transport
☐ 20
Toll-like receptor signaling pathway-
10
NF-kappa B signaling pathway-
organic acid transport
PD-L1 expression and PD-1 checkpoint pathway in cancer
anion transmembrane transport
Leishmaniasis
Antifolate resistance
sodium ion transport
D
0.040.060.080.100.120.140.160.18 GeneRatio
0.080.100.120.140.160.18 GeneRatio
E
anion transmembrane transporter activity
vacuolar membrane-
active transmembrane transporter activity
apical part of cell
organic anion transmembrane transporter activity
lytic vacuole membrane
secondary active transmembrane transporter activity
p.adjust
lysosomal membrane
p.adjust
symporter activity
1.5e-05
vesicle lumen
0.0020
0.0015
solute:cation symporter activity
1.0e-05
cytoplasmic vesicle lumen
0.0010
carboxylic acid transmembrane transporter activity
5.0e-06
secretory granule lumen
0.0005
organic acid transmembrane transporter activity
Counts
apical plasma membrane
Counts
solute:sodium symporter activity
5
☐ 10
basolateral plasma membrane
6
☐
8
carbohydrate derivative transmembrane transporter activity
☐
15
integral component of organelle membrane
☐
10
monocarboxylic acid transmembrane transporter activity
☐
20
intrinsic component of Golgi membrane
☐
12
organic hydroxy compound transmembrane transporter activity
integral component of Golgi membrane
nucleobase-containing compound transmembrane transporter activity
pigment granule
bile acid transmembrane transporter activity
melanosome
solute:proton symporter activity
brush border
0.040.060.080.100.120.140.160.18
0.040.050.060.070.080.090.10 GeneRatio
GeneRatio
A
B
REACTOME PROTEIN PROTEIN INTERACTIONS AT SYNAPSES
NES =- 2.594; p.adj = 0.037; FDR = 0.025
REACTOME SCAVENGING OF HEME FROM PLASMA
NES = 2.518; p.adj = 0.010; FDR = 0.006
REACTOME NEUREXINS AND NEUROLIGINS
NES =- 2.584; p.adj = 0.025; FDR = 0.017
REACTOME CREATION OF C4 AND C2
ACTIVATORS
NES = 2.495; p.adj = 0.010; FDR = 0.006
REACTOME VOLTAGE GATED POTASSIUM CHANNELS
NES =- 2.566; p.adj = 0.022; FDR = 0.015
REACTOME FCGR ACTIVATION
NES = 2.484; p.adj = 0.010; FDR = 0.006
REACTOME BINDING AND UPTAKE OF LIGANDS BY SCAVENGER RECEPTORS
REACTOME GLUTAMATE NEUROTRANSMITTER RELEASE CYCLE
NES =- 2.558; p.adj = 0.020; FDR = 0.013
NES = 2.472; p.adj = 0.010; FDR = 0.006
REACTOME INITIAL TRIGGERING OF COMPLEMENT
REACTOME TRANSMISSION ACROSS CHEMICAL SYNAPSES
NES = 2.469; p.adj = 0.010; FDR = 0.006
NES =- 2.497; p.adj = 0.133; FDR = 0.090
REACTOME IMMUNOREGULATORY
INTERACTIONS BETWEEN A
NES = 2.457; p.adj = 0.010; FDR = 0.006
WP SYNAPTIC VESICLE PATHWAY -
NES =- 2.426; p.adj = 0.024; FDR = 0.016
LYMPHOID AND A NON LYMPHOID
CELL
REACTOME CD22 MEDIATED BCR REGULATION
REACTOME DOPAMINE NEUROTRANSMITTER RELEASE CYCLE
NES = 2.452; p.adj = 0.010; FDR = 0.006
NES = - 2.424; p.adj = 0.020; FDR = 0.013
REACTOME SEROTONIN NEUROTRANSMITTER RELEASE CYCLE
NES =- 2.420; p.adj = 0.018; FDR = 0.012
REACTOME COMPLEMENT CASCADE
NES = 2.451; p.adj = 0.010; FDR = 0.006
REACTOME ROLE OF PHOSPHOLIPIDS IN PHAGOCYTOSIS
NES = 2.434; p.adj = 0.010; FDR = 0.006
REACTOME POTASSIUM CHANNELS
NES =- 2.417; p.adj = 0.042; FDR = 0.028
REACTOME ROLE OF LAT2 NTAL LAB ON CALCIUM MOBILIZATION
NES = 2.432; p.adj = 0.010; FDR = 0.006
REACTOME LONG TERM POTENTIATION
NES =- 2.366; p.adj = 0.020; FDR = 0.013
REACTOME FCGR3A MEDIATED IL 10 SYNTHESIS
NES = 2.407; p.adj = 0.010; FDR = 0.006
REACTOME NEUROTRANSMITTER RELEASE CYCLE
NES =- 2.356; p.adj = 0.024; FDR = 0.016
REACTOME FCERI MEDIATED MAPK
REACTOME TRAFFICKING OF AMPA
RECEPTORS
NES =- 2.344; p.adj = 0.021; FDR = 0.014
ACTIVATION
NES = 2.398; p.adj = 0.010; FDR = 0.006
REACTOME ANTIGEN ACTIVATES
B CELL RECEPTOR BCR LEADING
REACTOME GABA SYNTHESIS
TO GENERATION OF SECOND
NES = 2.385; p.adj = 0.010; FDR = 0.006
RELEASE REUPTAKE AND
NES =- 2.337; p.adj = 0.018; FDR = 0.012
MESSENGERS
DEGRADATION
REACTOME FCERI MEDIATED CA 2
NES = 2.384; p.adj = 0.010; FDR = 0.006
REACTOME EUKARYOTIC TRANSLATION ELONGATION
NES = - 2.333; p.adj = 0.039; FDR = 0.026
MOBILIZATION
REACTOME CELL SURFACE INTERACTIONS AT THE VASCULAR
NES =2.376; p.adj = 0.010; FDR = 0.006
REACTOME UNBLOCKING OF NMDA RECEPTORS GLUTAMATE BINDING
NES =- 2.330; p.adj = 0.018; FDR = 0.012
WALL
AND ACTIVATION
REACTOME PARASITE INFECTION
NES = 2.360; p.adj = 0.010; FDR = 0.006
KEGG RIBOSOME
NES = - 2.329; p.adj = 0.036; FDR = 0.024
0
1
2
3
4
5
-2.5
-2.0
-1.5
-1.0
-0.5
0.0
Figure 7. Significantly enriched pathways found by gene set enrichment analysis (GSEA). (A) Top 16 normalized enrichment scores (NESs) from GSEA, including scavenging of heme from plasma, creation of C4 and C2 activators, FCGR activation binding, and uptake of ligands by scavenger receptors, initial triggering of complement, immunoregulatory interactions between lymphoid and nonlymphoid cells, CD22-mediated B-cell receptor (BCR) regulation, complement cascade, role of phospholipids in phagocytosis, role of LAT2/NTAL/LAB on calcium mobilization, FCGR3A-mediated interleukin-10 (IL10) synthesis, FCERI-mediated mitogen-activated protein kinase (MAPK) activation, antigen activated BCR leading to generation of second messengers, FCERI-mediated Ca2+
mobilization, cell surface interactions at the vascular wall, and parasite infection. (B) Bottom 16 NESs from GSEA, including protein-protein interactions at synapses, neurexins, neuroligins, voltage-gated potassium channels, glutamate neurotransmitter release cycle, transmission across chemical synapses, synaptic vesicle pathway, dopamine neurotransmitter release cycle, serotonin neurotransmitter release cycle, potassium channels, long-term potentiation, neurotransmitter release cycle, trafficking of AMPA receptors, GABA synthesis, GABA release, GABA reuptake, GABA degradation, eukaryotic translation elongation, unblocking of NMDA receptors, glutamate binding and activation, and ribosome.
outcome endpoints of OS, DSS, and PFI events, SLC10A3 expression was higher in patients with clinical outcome endpoints occur in OS, DSS and PFI events. High mRNA expression of SLC10A3 was associated with WHO grade, IDH status, 1p/19q co- deletion, primary therapy outcome, race, histological type, OS event, DSS event, and PFI event in LGG patients. The ROC curves showed SLC10A3 expression has moderate diagnostic value for distinguishing between normal and cancerous tissue, IDH-Mut and IDH-WT, and 1p/19q non-co-deleted and 1p/19q co-deleted. The Kaplan-Meier curves revealed higher SLC10A3 expression correlated with poor OS, DSS, and PFI in LGG patients. Cox regression analysis indicated that SLC10A3 expression is an in- dependent prognostic indicator for LGG patients. The C-indexes and calibration plots of the nomograms based on multivariate analysis revealed a moderately accurate predictive performance for OS and DSS in LGG. These results have demonstrated that SLC10A3 is highly expressed in LGG and has clinical significance
for the diagnosis and prognosis of LGG patients. Based on these results, we would like to further explore its function in LGG. The GO/KEGG analysis and GSEA were used to further investigate the functions of SLC10A3 in LGG. The GO/KEGG enrichment analysis of genes from SLC10A3-related PPI network and SLC10A3- correlated genes in LGG showed these genes are mainly related to substance transport and involved in immunotherapy-related pathways and apoptosis, such as organic anion transport, organic acid transport, carboxylic acid transport, anion trans- membrane transport, lipid transport, sodium ion transport, anion transmembrane transporter activity, organic anion trans- membrane transporter activity, active transmembrane transporter activity, programmed cell death ligand 1 expression and pro- grammed cell death 1 checkpoint pathway in cancer, neutrophil- mediated immunity, apoptosis, activation of cysteine-type endo- peptidase activity involved in the apoptotic process, and so forth. The GSEA showed that many immune system-related pathways,
A
B
Macrophages
..
Macrophages
Neutrophils
Neutrophils
Eosinophils
Eosinophils
aDC
aDC
Cytotoxic cells
Cytotoxic cells
NK cells
NK cells
P value
Th17 cells
Th17 cells
0.75
T cells
T cells
0.50
iDC
NK CD56dim cells
SLC10A3
iDC
0.25
キ
Low
NK CD56dim cells
T helper cells
0.00
High
T helper cells
Th2 cells
Correlation
Th2 cells
0.1
B cells
*
B cells
0.2
CD8 T cells
*
ns
CD8 T cells
0.3
Th1 cells
ns
Th1 cells
0.4
Tgd
ns
Tgd
0.5
Tem
ns
Tem
DC
ns
DC
Mast cells
ns
Mast cells
TFH
ns
TFH
Tcm
ns
Tcm
TReg
*
NK CD56bright cells
TReg
**
NK CD56bright cells
pDC
**
pDC
-0.2
0.0
0.2
0.4
0.6
0.8
Enrichment scores
-0.2
0.0
0.2
0.4
0.6
Correlation
C
D
E
0.45
Enrichment of Macrophages
0.60
Enrichment of Neutrophils
0.4
Enrichment of Eosinophils
0.55
0.40
0.50
0.3
0.45
0.40
0.2
0.35
0.35
Spearman
0.1
Spearman
0.30
Spearman
0.30
r = 0.552
r = 0.518
r = 0.516
P < 0.001
P < 0.001
P < 0.001
3
4
5
6
3
4
5
6
3
4
5
6
The expression of SLC10A3 Log2 (TPM+1)
The expression of SLC10A3 Log2 (TPM+1)
The expression of SLC10A3 Log2 (TPM+1)
F
G
H
0.4
0.48
0.4
Enrichment of aDC
Enrichment of Cytotoxic cells
Enrichment of NK cells
0.46
0.3
0.44
0.2
0.3
0.42
0.1
0.2
0.40
Spearman
Spearman
Spearman
0.0
r = 0.477
r = 0.408
0.38
r = 0.387
-0.1
P < 0.001
P < 0.001
P < 0.001
0.1
0.36
3
4
5
6
3
4
5
6
3
4
5
6
The expression of SLC10A3 Log2 (TPM+1)
The expression of SLC10A3 Log2 (TPM+1)
The expression of SLC10A3 Log2 (TPM+1)
(continued)
SLC10A3 Expression Level (log2 TPM)
Purity
B Cell
CD8+T Cell
CD4+ T Cel
Macrophage
Neutrophil
Dendritic Cell
cof = - 0.256
p = 1.27e-08
partial.cor = 0.456
p = 6.28e-26
partial.cor = 0.25
p = 3.08e-08
· partial.cor = 0.547
p = 1.70e-38
partial.cor = 0.562
p = 9.36e-41
partial.cor = 0.606
partialstor = 0.593
5
p = 6.19e-49
p = 1.67e-46
4 -
LGG
3.
2
0.25
0.50
0.75
1.00 0.0
0.1
0.2
0.3
0.1
0.2
0.3
0.4
0.5
0.5
0.1
0.2
0.3
0.4
0.5
0.0
0.1
0.2
0.3
0.4
0.1
0.2
0.3
0.3
0.6
0.9
TIMER [Tumor Immune Estimation Resource]).
transmission across chemical synapse-related pathways, PPIs at synapse-related pathways, potassium channel-related pathways, and vesicle-mediated transport-related pathways were differen- tially enriched in the high SLC10A3 expression phenotype, indi- cating that SLC10A3 might participate in regulating the immune response, neurogenic signaling, and vesicle-mediated transport in LGG. Thus, we preliminarily speculate that SLC10A3 might regulate neuronal system-related pathways (e.g., voltage gated potassium channels) via substance transport (e.g., anion trans- membrane transport, sodium ion transport).
Immunotherapy for cancer has been made significant progress in recent years.38 The GO/KEGG analysis and GSEA indicated that SLC10A3 might be related to immune regulation in LGG. Thus, we further analyzed the correlation between SLC10A3 expression and immune infiltration in LGG. The results showed that the SLC10A3 expression correlated positively with infiltrating levels of many immune cells, including innate immune cells of macrophages, neutrophils, eosinophils, aDCs, NK cells, iDCs, NK CD56dim cells, and adaptive immune cells of Th17 cells, T cells, Th cells, Th2 cells, and cytotoxic cells, and correlated negatively with infiltrating levels of pDCs, NK CD56bright cells, and regulatory T cells. The enrichment scores of macrophages, neutrophils, eosinophils, aDCs, cytotoxic cells, NK cells, Th cells, and Th2 cells were also significantly higher in the high SLC10A3 expression group. However, the enrichment score of the Thi cells had no significant changes at different SLC10A3 expression levels. Dendritic cells can be activated to aDCs and undergo a series of phenotypic and functional changes and then become mature dendritic cells.39 Cytotoxic cells, including cytotoxic lymphocytes, cytotoxic T cells, and NK cells, are ultimately responsible for killing the cancer cells and eliminating the tumor.4° NK cells are divided into NK CD56bright cells and NK CD56dim cells. NK CD56dim cells account for >90% of NK cells and mainly have cytotoxic effects to kill tumor cells. NK CD56bright cells mainly have immunoregulatory functions by producing a large amount of cytokines.41 Neutrophils play a dual role of promoting tumor and suppressing tumor in the TME.42
Eosinophils have antitumor and pro-tumor capacities in different cancers.43 Nevertheless, high SLC10A3 expression significantly promoted infiltration of macrophages in LGG tissues, which can promote solid tumors and negatively affect survival of cancer patients.44,45 Tumor-associated macrophages (TAMs) promote glioma expansion and progress by enhancing tumor growth, tumor invasion, tumor migration, angiogenesis, metabolism, and immunosuppression.40 M2 macrophages can cause a poor prognosis for glioma patients by activating STAT3 to stimulate the proliferation of tumor cells.47 Thi and Th2 cells secrete different cytokines to promote their own proliferation and inhibit the proliferation of the other.48,49 Therefore, Thi and The cells are in a relatively balanced state in normal conditions; however, tumors can lead to an imbalance of Thi and Th2 cells. A shift toward Th2 cells leads to immunosuppression in tumors.23,5° Moreover, analysis results from the TIMER database and TISIDB further demonstrated that SLC10A3 can increase LGG tumor-infiltrating macrophages. In addition, analysis results from the TIMER database showed the marker genes of TAMs, macrophages, M1 macrophages, M2 macrophages, Thi cells, Th2 cells, and T cell exhaustion corre- lated with expression of SLC10A3. The correlation between SLC10A3 and the marker genes of T-cell exhaustion indicated SLC10A3 might be involved in mediating T-cell depletion during the LGG immune response.51,52 These results suggest that SLC10A3 might participate in regulating the polarization of TAMs and the induction of T-cell exhaustion to induce an immunosuppressive microenvironment in LGG, leading to the poor prognosis of LGG patients.
Programmed cell death plays a dual role of promoting tumor and suppressing tumor in cancer and is involved in fine tuning the antitumor immunity in the TME. Moreover, brain cancer is the cancer most prone to programmed cell death.37 We found many cell death-related pathways from the GO/KEGG analysis and GSEA, in particular, programmed cell death-associated pathways. Furthermore, we found SLC10A3 correlated positively with the gene signature of programmed cell death and moderately with the
Figure 8. Correlation between solute carrier family 10 member 3 (SLC10A3) and tumor immune infiltrating cells. (A) Correlation diagram showing difference in enrichment scores of 24 immune cells in high and low SLC10A3 expression groups. (B) Lollipop diagram showing correlation between SLC10A3 expression and relative abundances of 24 immune cells. (C-H) Scatter plot showing correlation between infiltration of 6 immune
cells and SLC10A3 expression (P <0.05). * P < 0.05; ** P < 0.01; *** P < 0. 001. aDC, activated dendritic cells; DC, dendritic cells; iDC, immature dendritic cell; NK, natural killer; ns, not significant; pDC, plasmacytoid dendritic cells; Tcm, T central memory; Tem, T effector memory; Tgd, T gamma delta; TFH, T follicular helper; Th, T helper; TPM, transcripts per million reads; Treg, T regulatory cells.
(continued)
A
B
Act CD8
ADORA2A
Tem CD8
Tem CD8
BTLA
Act CD4
CD160
Tom CD4
CD244
Tem CD4
CD274
Tih
CD96
Tgd
CSF1R
Th1
Th17
CTLA4
Th2
HAVCR2
Treg
IDO1
Act B
IL10
Imm B
ILTORB
Mem B
KDR
NK
CD56bright
KIR2DL1
CD56dim
KIR2DL3
MDSC
1
LAG3
NKT
LGALS9
Act DC
PDCD1
PDC
PDCD1LG2
IDC
Macrophage
PVRL2
Eosinophil
TGFB1
Mast
TGFBR1
Monocyte
TIGIT
Neutrophil
VTCN1
C
D
C10orf54
SP271
B2M
CD28 CD40-
HLA-A
CD40LG
CD48 CD70
HLA-B
CD80 CD86
HLA-C
CXCL12”
HLA-DMA
CXCR4
EDLPD HHLA2
HLA-DMB
HLA-DOA
ICOSLG
IL2RA
1 |
HLA-DOB
IL6R-
HLA-DPA1
KLRC1 J
LTA ] MICB.
HLA-DPB1
NTSE
HLA-DQA1
RAETTE-
HLA-DQA2
TMEM173-
HLA-DQB1
TNFRS: 136 -
HLA-DRA
-1
INFRSF14
-1
HLA-DRB1
TNFRSF35 INFRSES
HLA-E
INERSTE
HLA-F
INFRSFO
HLA-G
TAP1
TAP2
TNFSF9
ULBP1
TAPBP
E
F
CCL1 ]
CCL2
CCL3-
CCR1
CCL4-
CCR2
CCL5
CCL7]
CCR3
COLE
CCLTIJ
CCL13
CCR4
CCL14
CCL157
CCR5
CCL161
CCL17
CCL18
CCR6
CCL19”
-
1
CCR7
1
CCL21- ce133- CCLZZ CCL23 CCL24 ]
CCR8
CCR9
CCL25
CCL26
0
CCL27 ]
CCR10
CCL28-
CXCR1
-0
CX3CL1-
CXCL1- -
-1
CXCR2
Ce-
-1
CYCLE
CALLS
CXCR3
CXCL6
CXCL8
CXCR4
CXCL9”
CXCL10”
CXCL11”
CXCR5
CXCL12”
CXCL137
CxCLIA-
CXCR6
CXCL16-
- CXCL17
XCR1
XCL1
XCL2
CX3CR1
Figure 10. Correlation of solute carrier family 10 member 3 (SLC10A3) expression with tumor-infiltrating lymphocytes (TILs), immunomodulators, chemokines, and receptors (TISIDB [Tumor-Immune System Interaction Database]). (A) Spearman correlations between expression of SLC10A3 and TILs across human cancers. (B) Spearman correlations between expression of SLC10A3 and immune inhibitors across human cancers. (C) Spearman correlations between
expression of SLC10A3 and immune stimulators across human cancers. (D) Spearman correlations between expression of SLC10A3 and major histocompatabilities across human cancers. (E) Spearman correlations between expression of SLC10A3 and chemokines across human cancers. (F) Spearman correlations between expression of SLC10A3 and receptors across human cancers.
A
B
activation of cysteine-type endopeptidase activity
8
WP APOPTOSIS MODULATION AND SIGNALING
NES = 1.710; p.adj = 0.010; FDR = 0.006
involved in apoptotic process
WP APOPTOSIS
NES = 1.804; p.adj = 0.010; FDR = 0.006
p.adjust
REACTOME TP53 REGULATES TRANSCRIPTION OF
NES = 1.710; p.adj = 0.010; FDR = 0.006
0.020
CELL DEATH GENES
cysteine-type endopeptidase activity involved in
0.015
BIOCARTA CASPASE PATHWAY -
NES = 1.832; p.adj = 0.010; FDR = 0.006
apoptotic process
0.010
REACTOME CASPASE ACTIVATION VIA DEATH
NES = 1.698; p.adj = 0.010; FDR = 0.006
0.005
RECEPTORS IN THE PRESENCE OF LIGAND
REACTOME RIPK1 MEDIATED REGULATED NECROSIS
NES = 1.674; p.adj = 0.016; FDR = 0.011
death receptor binding
E
Counts
REACTOME PROGRAMMED CELL DEATH
NES = 1.403; p.adj = 0.028; FDR = 0.019
2
4
WP APOPTOSIS MODULATION BY HSP70
NES = 1.595; p.adj = 0.030; FDR = 0.020
death domain binding
6
8
BIOCARTA DEATH PATHWAY
NES = 1.578;p.adj = 0.034; FDR = 0.023
WP NANOMATERIAL INDUCED APOPTOSIS
NES = 1.579, p.adj = 0.043; FDR = 0.029
WP NANOPARTICLE TRIGGERED REGULATED
NES = 1.595; p.adj = 0.046; FDR = 0.031
Apoptosis
KEGG
NECROSIS
REACTOME CASPASE ACTIVATION VIA
EXTRINSIC APOPTOTIC SIGNALLING PATHWAY
NES = 1.555; p.adj = 0.050; FDR = 0.034
REACTOME TP53 REGULATES TRANSCRIPTION OF CASPASE ACTIVATORS AND CASPASES
64880240
NES = 1.584; p.adj = 0.050; FDR = 0.034
00000000.
GeneRatio
0
1
2
3
4
Figure 11. Cell death-related pathways from gene otology (GO)/Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis and GSEA. (A) Cell death-related pathways from GO/KEGG analysis. (B) Cell death-related pathways from GSEA, including apoptosis modulation and signaling, apoptosis, TP53-regulated transcription of cell death genes, caspase pathway, caspase activation via death
receptors in the presence of ligand, RIPK1-mediated regulated necrosis, programmed cell death, apoptosis modulation by HSP70, death pathway, nanomaterial-induced apoptosis, nanoparticle-triggered regulated necrosis, caspase activation via extrinsic apoptotic signaling pathway, TP53-regulated transcription of caspase activators and caspases.
gene signature of pyroptosis, lysosome-dependent cell death, necroptosis, apoptosis, ferroptosis, alkaliptosis, and autophagy- dependent cell death, indicating that SLC10A3 might participate in regulating programmed cell death in LGG. Pyroptosis is closely associated with the inflammatory response and has double func- tion in regulating antitumor immunity in TMEs.53,54 However, the relationship between pyroptosis and antitumor immunity is not fully understood.55 A study constructed a scoring scheme (PSscore) to quantify pyroptosis regulation patterns and found a high PSscore demonstrated high expression of pyroptosis-related genes, higher immune cell infiltration, and a poor prognosis for glioma patients.5º Bioinformatics analysis showed pyroptosis of macrophages plays a critical role in maintaining immunosuppression in glioma.57 The main feature of lysosome- dependent cell death is lysosomal membrane permeabilization.58 Thus, drugs that can cause lysosome-dependent cell death by sensitizing lysosomes and promoting lysosomal membrane per- meabilization could have useful antitumor effects in antiapoptotic cells.59,60 However, therapies that protect lysosomal structure and restore lysosomal function are required to treat neurodegenerative diseases.ºI Necroptosis, known as inflammatory cell death,62 has both pro-tumor and antitumor roles in the TME.63 In LGG, higher expression of necroptosis pathway-associated genes (including RIPKI, RIPK3 and MLKL) were related to poor OS and DFS.64 Necroptosis can induce a chronic inflammatory microenvironment to promote glioma growth by boosting inflammatory activity and attracting immunosuppressive cell infiltration.65 Apoptosis is one of the earliest well-recognized
new therapy field in glioma.67 Nevertheless, apoptosis of cancer cells is usually attenuated in the TME, and apoptosis of immune cells (e.g., cytotoxic T cells) can directly weaken antitumor immunity in the TME.68-7º Ferroptosis is characterized by
iron-dependent lipid peroxidation.71 A study found the enriched ferroptosis in GBM patients correlated with progressive malignancy, poor outcomes, and aggravated immunosuppression.72 Furthermore, they found ferroptosis-mediated immunosuppression was associated with TAMs that could be recruited and polarized into M2-like pheno- type by ferroptosis.72 Early necrosis in GBM tissue promotes neutrophil infiltration through ferroptosis, causing more necrosis; thus, necrosis and neutrophil infiltration could form a positive feedback loop to promote GBM progression by maximizing GBM necrosis formation.73 Alkaliptosis as a new treatment strategy for multiple tumor types is a pH-dependent form of regulated cell death.74 Autophagy-dependent cell death has a strict requirement of autophagy.75 Although autophagy-dependent cell death is an integral component of tu- mor suppressive mechanisms,76 autophagy is also considered to play a key role in establishing resistance to cancer treatment.77 Moreover, a study showed that inhibition autophagy of cancer cells could promote cancer cell clearance in the TME.78 These results suggest that SLC10A3 might upregulate programmed cell death (e.g., pyroptosis, necroptosis, ferroptosis) to promote LGG progression, leading to a poor prognosis for LGG patients.
From our analyses, we speculate that SLC10A3 might participate in regulating apoptosis of immune cells (e.g., cytotoxic T cells) and mediating immunosuppression in the TME via pyroptosis, necroptosis, and ferroptosis to promote LGG progression. More and more evidence has shown that individual SLC transporters can be expressed in various types of immune cells. Research has found that SLC can modulate different metabolic pathways and balance the levels of different metabolites to regulate lymphocyte signaling and control the differentiation, function, and fate of lympho- cytes.79 The cellular uptake of lactate can generally inhibit the activity of effector T cells and promote polarization of TAMs
A
Apoptosis
B
Necroptosis
6
1
6
1
SLC10A3
Log2 (TPM+1)
9
X SLC10A3 Log2 (TPM+1)
5
4
Low
4
3
3
Low
2
High
O
2
High
1
-
1
0
0
BCL2L10
ITPK1
BCL2L2
MCL1
ADAM17
BCL2L1
PELI1
TNFAIP3
BCL2
PGAM5
TP53
PARP1
BID
CDC37
PMAIP1
UHRF1
AXL
BBC3
BRAF
BCL2L11
BRD4
SP1
BOK
TICAM1
BAX
BIRC3
BAK1
BIRC2
CASP7
BP1
CASP6
TNFRSF1A
TNF
CASP3
TLR3
CASP10
RIPK3
RIPK1
CASP9
MLKL
CASP8
FASLE
CASP2
FAS
FADD
Z-score
-5.0-2.5 0.0 2.5 5.0
Z-score
-5
0
5
C
D
Pyroptosis
Ferroptosis
SLC10A3 Log2 (TPM+1)
6
1
=
N 5
0 SLC10A3 Log2 (TPM+1)
6 -
5
Low
4
3
Low
High
High
2
2 -
1
1 -
0
0
TP53
OTUB1
ITGB4
-
FANCD2
TGA6
NFS1
HSPA5
HSPB1
NFE2L2
GPX4
-
SLC7A11
TGFBR1
ACVR1B
-
RAB7A
VDAC3
VDAC2
PEBP1
BECN1
BAP1
NCOA4
DPP4
GLS2
ALOX15
LPCAT3
AIM2
ACSL4
TFRC
Z-score
Z-score
-4
0
4
8
-5
0
5
10
E
F
Cuproptosis
Parthanatos
SLC10A3 Log2 (TPM+1)
6
1
6
1
5
Log2 (TPM+1)
5
4
SLC10A3
3
Low
4
3
Low
2
High
2
High
1
1
1
GCSH
0
0
FDX1
RNF146
DBT
DLST
ATP7A
ADPRS
SLC31A1
ATP7B
**
OGG1
CDKN2A
.
GLS
MTF1
AIFM1
PDHB
PDHA1
DLAT
MIF
DLD
LIPT1
PARP1
LIAS
Z-score
-5.0 -2.5 0.0 2.5
Z-score
-5.0-2.50.0 2.5 5.0 7.5
Figure 12. Spearman correlation analysis between solute carrier family 10 member 3 (SLC10A3) and marker gene of programmed cell death. (A) Spearman correlation analysis of SLC10A3 expression and marker genes of apoptosis. (B) Spearman correlation analysis of SLC10A3 expression and marker genes of necroptosis. (C) Spearman correlation analysis of SLC10A3 expression and marker genes of pyroptosis. (D) Spearman correlation analysis of SLC10A3 expression and marker genes of ferroptosis. (E) Spearman correlation analysis of SLC10A3 expression and marker genes of cuproptosis. (F) Spearman correlation analysis of SLC10A3 expression and marker genes of parthanatos. (G) Spearman correlation
analysis of SLC10A3 expression and marker genes of entotic cell death. (H) Spearman correlation analysis of SLC10A3 expression and marker genes of netotic cell death. (I) Spearman correlation analysis of SLC10A3 expression and marker genes of lysosome-dependent cell death. (J) Spearman correlation analysis of SLC10A3 expression and marker genes of autophagy-dependent cell death. (K) Spearman correlation analysis of SLC10A3 expression and marker genes of alkaliptosis. (L) Spearman correlation analysis of SLC10A3 expression and marker genes of oxeiptosis. * P < 0.05; ** P < 0.01; *** P < 0.001.
(Continues)
G
H
SLC10A3
Log2 (TPM+1)
ONWAGO
Entotic cell death
Netotic cell death
6 -
SLC10A3 Log2 (TPM+1)
6
1
A
3 .
/
Low
3
Low
2 -
High
2
High
0
J
0
1
UVRAG
RUBCN
MIA
ROCK2
ROCK1
RNF146
PADI4
RHOA
MYH14
**
CAMP
CYBB
CTNNA1
CDH1
MPO
CDC42
BECN1
MMP1
ATG7
ATG5
ELANE
PRKAA1
Z-score
-8
-4
0
4
Z-score
0
4
8
12
J
Lysosome-dependent cell death
Autophagy-dependent cell death
SLC10A3 Log2 (TPM+1)
6
1
5
SLC10A3 Log2 (TPM+1)
=
4
4
3
-
Low
1 -
.
Low
2
-
High
2
-
High
1
0
1.
J
1
-
MCOLN1
0
J
MTOR
NFKB1
TP53
PRKAG3
STAT3
PRKAG2
CTSZ
PRKAG1
CTSW
PRKAB2
CTSV
PRKAB1
CTSS
CTSO
PRKAA2
CTSL
PRKAA1
CTSK
FXYD1
CTSH
ATP1B3
…
CTSG
ATP1B2
**
CTSF
CTSE
ATP1B1
ATP1A4
CTSD
CTSC
ATP1A3
CTSB
ATP1A2
*
CTSA
ATP1A1
Z-score
0
4
8
12
Z-score
-5
0
5
10
K
L
Alkaliptosis
Oxeiptosis
SLC10A3 Log2 (TPM+1)
6
1
SLC10A3 Log2 (TPM+1)
6
y
5
4
-
3
Low
4
-
3
Low
2
-
High
2
-
High
1
-
1
-
0
0
RELA
AIRE
IKBKG
NFE2L2
CHUK
AIFM1
CA9
NFKB1
KEAP1
IKBKB
PGAM5
Z-score
-3
0
3
6
Z-score
-2.5 0.0 2.5 5.0
Figure 12. (continued).
toward the M2-like phenotype.80,81 Zinc transporter-mediated zinc homeostasis plays an important role in the immune response.82 Thus, we speculated that SLC10A3 could be involved in regulating immune cells (e.g., effector T cell dysfunction, M2 polarization) via metabolism-related substance transport (e.g., carbohydrate derivative transport, bile acid and bile salt transport,
organic acid transport, carboxylic acid transport, lipid transport, zinc ion transport) to induce an immunosuppressive microenvi- ronment in LGG. Nervous system-cancer crosstalk can occur through nervous system regulation of different cell types within the TME.83 Neuroimmune communication has elicited a great amount of interest in recent years.84 Research found dopamine
A
Apoptosis
B
Necroptosis
C
Pyroptosis
A
p-value = 1.8e-67
0
R = 0.67
p-value = 1.8e-74
R = 0.69
p-value = 4.3e-82
R = 0.71
log2(578 Signatures TPM)
2.4
log2(96 Signatures TPM)
2.5
log2(51 Signatures TPM)
0
2.3
2.4
2.2
2.3
2.1
2.2
2.2
2.0
-
5
1.9
0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
log2(SLC10A3 TPM)
log2(SLC10A3 TPM)
log2(SLC10A3 TPM)
D
Ferroptosis
E
Cuproptosis
F
2.90
Parthanatos
2
p-value = 4.7e-49
R = 0.59
p-value = 8.9e-08
p-value = 5.3e-29
R = 0.23
R = 0.46
log2(87 Signatures TPM)
2.85
2.6
log2(14 Signatures TPM)
2
log2(8 Signatures TPM)
2.80
2.5
2.2
2.75
2.70
2.4
2.0
2.65
73
2.60
00
2.55
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
log2(SLC10A3 TPM)
log2(SLC10A3 TPM)
log2(SLC10A3 TPM)
G
Entotic cell death
H
Netotic cell death
Lysosome-dependent cell death
0
p-value = 3.4e-24
R = 0.43
1
p-value = 1.2e-12
p-value = 8.8e-79
R = 0.31
0
R = 0.7
log2(21 Signatures TPM)
3
log2(7 Signatures TPM)
log2(220 Signatures TPM)
2.4
2
2.5
2.3
1.0
2
0.8
2.4
2
0.6
2
2
0.4
2
0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
log2(SLC10A3 TPM)
log2(SLC10A3 TPM)
log2(SLC10A3 TPM)
J
Autophagy-dependent cell death
K
Alkaliptosis
L
Oxeiptosis
0
p-value = 1.1e-33
R = 0.5
p-value = 9.1e-43
0
R = 0.55
3
p-value = 4.5e-30’
R = 0.47
log2(365 Signatures TPM)
2.5
log2(6 Signatures TPM)
log2(5 Signatures TPM)
2.4
2.4
2.4
2.2
3
2
2
2.0
2
-
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
log2(SLC10A3 TPM)
log2(SLC10A3 TPM)
log2(SLC10A3 TPM)
can inhibit TLR2-induced NF-KB activation and inflammation via the dopamine D5 receptor in macrophages.85 B-cell-derived GABA promotes the differentiation of monocytes into anti- inflammatory macrophages that secrete interleukin-10 and inhibit the killer function of CD8+ T cells.86 Therefore, we speculated that SLC10A3 might also regulate neurogenic signaling to modulate the TME of LGG by substance transport. Moreover, the correlations of SLC10A3 expression with TILs, immunomodulators, chemokines, and receptors are significantly higher in glioma (LGG and GBM) than in other cancers and are highest in LGG (TISIDB). These factors could lead to SLC10A3 as a prognostic tool especially for LGG compared with other types of tumors.
Our study revealed that SLC10A3 is upregulated in glioma and associated with poor OS in LGG and GBM, especially LGG. Further analysis found that SLC10A3 is correlated with poor OS, DSS, and PFI in LGG and has diagnostic value for distinguishing normal and cancerous tissue, IDH-Mut and IDH-WT, and 1p/19q non-co-deleted and 1p/19q co-deleted. From the analysis results, we found SLC10A3 is involved in regulating substance transport, neurogenic signaling, immune infiltration, and programmed cell death. Combined with the results from previous studies, we speculated that SLC10A3 might mediate neuroimmune commu- nication and metabolism-related substance transport in immune cells to induce immunosuppression in the TME of LGG by sub- stance transport. Furthermore, SLC10A3 might also upregulate pyroptosis, necroptosis, and ferroptosis in the TME to induce immunosuppression of LGG. Therefore, our results have provided the foundation for determining the molecular mechanisms of SLC10A3 underlying LGG pathogenesis in the future. mRNA expression of SLC10A3 in patients with PD was higher than that in patients with stable disease or a complete response and was significantly associated with the primary therapy outcome. Furthermore, SLC10A3 is involved in immune regulation. Thus,
SLC10A3 mRNA expression could provide guidance for timely intervention and effective treatment of LGG, especially for immunotherapy. However, the research has several limitations. First, the diagnostic and prognostic value of SLC10A3 in clinical practice requires further study. Second, although the results of the GO/KEGG analysis and GSEA provide some clues for functional study of SLC10A3 in LGG, further experimental investigation and analysis are needed to reveal the substrate specificity of SLC10A3. Additional research is required to detail the mechanisms of the correlation between SLC10A3 and immune infiltration, between SLC10A3 and neurogenic signaling, and between SLC10A3 and programmed cell death in LGG tissue.
CONCLUSIONS
First, we confirmed that SLC10A3 is upregulated in LGG and that high expression of SLC10A3 is related to a worse prognosis for patients with LGG. Furthermore, SLC10A3 could play important roles in substance transport, neurogenic signaling, immunomo- dulation, and programmed cell death in LGG, suggesting SLC10A3 as a possible therapeutic target for LGG.
CRediT AUTHORSHIP CONTRIBUTION STATEMENT
Weibo Ma: Data curation, Conceptualization. Pengying Mei: Conceptualization, Data curation, Methodology, Software, Visu- alization, Investigation, Writing - original draft.
ACKNOWLEDGMENTS
The data that support the findings of the present study are openly available at The Cancer Genome Atlas (available at: https://portal. gdc.cancer.gov/) and Genotype-Tissue Expansion (available at: https://commonfund.nih.gov/GTEx).
REFERENCES
1. Louis DN, Perry A, Wesseling P, et al. The 2021 WHO classification of tumors of the central ner- vous system: a summary. Neuro Oncol. 2021;23: 1231-1251.
2. Olson JD, Riedel E, DeAngelis LM. Long-term outcome of low-grade oligodendroglioma and mixed glioma. Neurology. 2000;54:1442-1448.
3. Mirow C, Pietsch T, Berkefeld S, et al. Children <1 year show an inferior outcome when treated according to the traditional LGG treatment strat- egy: a report from the German multicenter trial HIT-LGG 1996 for children with low grade glioma (LGG). Pediatr Blood Cancer. 2014;61:457-463.
4. Diwanji TP, Engelman A, Snider JW, Mohindra P. Epidemiology, diagnosis, and optimal manage- ment of glioma in adolescents and young adults. Adolesc Health Med Ther. 2017;8:99-113.
5. Claus EB, Walsh KM, Wiencke JK, et al. Survival and low-grade glioma: the emergence of genetic information. Neurosurg Focus. 2015;38:E6.
6. Hartmann C, Hentschel B, Wick W, et al. Patients with IDH1 wild type anaplastic astrocytomas
exhibit worse prognosis than IDH1-mutated glio- blastomas, and IDH1 mutation status accounts for the unfavorable prognostic effect of higher age: implications for classification of gliomas. Acta Neuropathol. 2010;120:707-718.
7. Chamberlain MC. Prognostic or predictive value of MGMT promoter methylation in gliomas depends on IDH1 mutation. Neurology. 2014;82:2147-2148.
8. Hainfellner J, Louis DN, Perry A, Wesseling P. Letter in response to David N. Louis et al, Inter- national Society of Neuropathology-Haarlem Consensus Guidelines for Nervous System Tu- mor Classification and Grading. Brain Pathol. 2014; 24:671-672.
9. Claro da Silva T, Polli JE, Swaan PW. The solute carrier family 10 (SLC10): beyond bile acid trans- port. Mol Aspects Med. 2013;34:252-269.
10. Fernandes CF, Godoy JR, Doring B, et al. The novel putative bile acid transporter SLC10A5 is highly expressed in liver and kidney. Biochem Bio- phys Res Commun. 2007;361:26-32.
11. Godoy JR, Fernandes C, Doring B, Beuerlein K, Petzinger E, Geyer J. Molecular and phylogenetic characterization of a novel putative membrane
transporter (SLC10A7), conserved in vertebrates and bacteria. Eur J Cell Biol. 2007;86:445-460.
12. Karakus E, Wannowius M, Muller SF, et al. The orphan solute carrier SLC10A7 is a novel negative regulator of intracellular calcium signaling. Sci Rep. 2020;10:7248.
13. Zakrzewicz D, Geyer J. Multitasking Na(+)/Taur- ocholate cotransporting polypeptide (NTCP) as a drug target for HBV infection: from protein engi- neering to drug discovery. Biomedicines. 2022;10: 196.
14. Yang N, Dong YQ, Jia GX, et al. ASBT(SLC10A2): a promising target for treatment of diseases and drug discovery. Biomed Pharmacother. 2020;132: 110835.
15. Wang Q, Lu F, Lan R. RNA-sequencing dissects the transcriptome of polyploid cancer cells that are resistant to combined treatments of cisplatin with paclitaxel and docetaxel. Mol Biosyst. 2017;13: 2125-2134.
16. Tian S, Li J, Xiang J, Peng P. The clinical relevance and immune correlation of SLC10 family genes in liver cancer. J Hepatocell Carcinoma. 2022;9: 1415-1431.
17. Vivian J, Rao AA, Nothaft FA, et al. Toil enables reproducible, open source, big biomedical data analyses. Nat Biotechnol. 2017;35:314-316.
18. Ceccarelli M, Barthel FP, Malta TM, et al. Molec- ular profiling reveals biologically discrete subsets and pathways of progression in diffuse glioma. Cell. 2016;164:550-563.
19. Yu G, Wang LG, Han Y, He QY. ClusterProfiler: an R package for comparing biological themes among gene clusters. OMICS. 2012;16:284-287.
20. Subramanian A, Tamayo P, Mootha VK, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expres- sion profiles. Proc Natl Acad Sci U S A. 2005;102: 15545-15550.
21. Bindea G, Mlecnik B, Tosolini M, et al. Spatio- temporal dynamics of intratumoral immune cells reveal the immune landscape in human cancer. Immunity. 2013;39:782-795.
22. Hanzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinf. 2013;14:7.
23. Wang SS, Liu W, Ly D, Xu H, Qu L, Zhang L. Tumor-infiltrating B cells: their role and applica- tion in anti-tumor immunity in lung cancer. Cell Mol Immunol. 2019;16:6-18.
24. Danaher P, Warren S, Dennis L, et al. Gene expression markers of tumor infiltrating leuko- cytes. J Immunother Cancer. 2017;5:18.
25. Cui X, Zhang X, Liu M, et al. A pan-cancer anal- ysis of the oncogenic role of staphylococcal nuclease domain-containing protein 1 (SND1) in human tumors. Genomics. 2020;112:3958-3967.
26. Tang D, Kang R, Berghe TV, Vandenabeele P, Kroemer G. The molecular machinery of regulated cell death. Cell Res. 2019;29:347-364.
27. Hanson B. Necroptosis: a new way of dying? Cancer Biol Ther. 2016;17:899-910.
28. Chen J, Kos R, Garssen J, Redegeld F. Molecular insights into the mechanism of necroptosis: the necrosome as a potential therapeutic target. Cells. 2019;8:1486.
29. Molnar T, Mazlo A, Tslaf V, Szollosi AG, Emri G, Koncz G. Current translational potential and un- derlying molecular mechanisms of necroptosis. Cell Death Dis. 2019;10:860.
30. Liu L, Tang Z, Zeng Y, et al. Role of necroptosis in infection-related, immune-mediated, and auto- immune skin diseases. J Dermatol. 2021;48: 1129-1138.
31. Karki R, Kanneganti TD. Diverging inflamma- some signals in tumorigenesis and potential tar- geting. Nat Rev Cancer. 2019;19:197-214.
32. Xia X, Wang X, Cheng Z, et al. The role of pyroptosis in cancer: pro-cancer or pro-”host”? Cell Death Dis. 2019;10:650.
33. Wang B, Yin Q. AIM2 inflammasome activation and regulation: a structural perspective. J Struct Biol. 2017;200:279-282.
34. Man SM, Kanneganti TD. Regulation of inflam- masome activation. Immunol Rev. 2015;265:6-21.
35. Tsvetkov P, Coy S, Petrova B, et al. Copper induces cell death by targeting lipoylated TCA cycle pro- teins. Science. 2022;375:1254-1261.
36. Chen L, Min J, Wang F. Copper homeostasis and cuproptosis in health and disease. Signal Transduct Target Ther. 2022;7:378.
37. Zou Y, Xie J, Zheng S, et al. Leveraging diverse cell-death patterns to predict the prognosis and drug sensitivity of triple-negative breast cancer patients after surgery. Int J Surg. 2022;107:106936.
38. van den Bulk J, Verdegaal EM, de Miranda NF. Cancer immunotherapy: broadening the scope of targetable tumours. Open Biol. 2018;8:180037.
39. Steinman RM. Decisions about dendritic cells: past, present, and future. Annu Rev Immunol. 2012; 30:1-22.
40. Martinez-Lostao L, Anel A, Pardo J. How do cytotoxic lymphocytes kill cancer cells? Clin Cancer Res. 2015;21:5047-5056.
41. Morvan MG, Lanier LL. NK cells and cancer: you can teach innate cells new tricks. Nat Rev Cancer. 2016;16:7-19.
42. Xiong S, Dong L, Cheng L. Neutrophils in cancer carcinogenesis and metastasis. J Hematol Oncol. 2021;14:173.
43. Varricchi G, Galdiero MR, Loffredo S, et al. Eo- sinophils: the unsung heroes in cancer? Oncoim- munology. 2018;7:e1393134.
44. Cassetta L, Pollard JW. Targeting macrophages: therapeutic approaches in cancer. Nat Rev Drug Discov. 2018;17:887-904.
45. Shu Y, Cheng P. Targeting tumor-associated macrophages for cancer immunotherapy. Biochim Biophys Acta Rev Cancer. 2020;1874:188434.
46. Tong N, He Z, Ma Y, et al. Tumor associated macrophages, as the dominant immune cells, are an indispensable target for immunologically cold tumor-glioma therapy? Front Cell Dev Biol. 2021;9: 706286.
47. Komohara Y, Horlad H, Ohnishi K, et al. Impor- tance of direct macrophage-tumor cell interaction on progression of human glioma. Cancer Sci. 2012; 103:2165-2172.
48. Zhu JT. T Helper cell differentiation, heteroge- neity, and plasticity. Cold Spring Harbor Perspect Biol. 2018;10:2030338.
49. Morel PA. Differential T-cell receptor signals for T helper cell programming. Immunology. 2018;155: 63-71.
50. Li J, Zeng Z, Wu Q, et al. Immunological modu- lation of the Thi/Th2 shift by ionizing radiation in tumors (Review). Int J Oncol. 2021;59:50.
51. Mohme M, Neidert MC. Tumor-specific T cell activation in malignant brain tumors. Front Immu- nol. 2020;11:205.
52. Karachi A, Dastmalchi F, Nazarian S, et al. Opti- mizing T cell-based therapy for glioblastoma. Front Immunol. 2021;12:705580.
53. Liu J, Hong M, Li Y, Chen D, Wu Y, Hu Y. Pro- grammed cell death tunes tumor immunity. Front Immunol. 2022;13:847345.
54. Hsu SK, Li CY, Lin IL, et al. Inflammation-related pyroptosis, a novel programmed cell death pathway, and its crosstalk with immune therapy in cancer treatment. Theranostics. 2021;11:8813-8835.
55. Wei X, Xie F, Zhou X, et al. Role of pyroptosis in inflammation and cancer. Cell Mol Immunol. 2022; 19:971-992.
56. Fan T, Wan Y, Niu D, et al. Comprehensive analysis of pyroptosis regulation patterns and their influence on tumor immune microenviron- ment and patient prognosis in glioma. Discou Oncol. 2022;13:13.
57. Li L, Wu L, Yin X, Li C, Hua Z. Bulk and single- cell transcriptome analyses revealed that the pyroptosis of glioma-associated macrophages participates in tumor progression and immuno- suppression. Oxid Med Cell Longev. 2022;2022: 1803544.
58. Wang F, Gomez-Sintes R, Boya P. Lysosomal membrane permeabilization and cell death. Traffic. 2018;19:918-931.
59. Serrano-Puebla A, Boya P. Lysosomal membrane permeabilization as a cell death mechanism in cancer cells. Biochem Soc Trans. 2018;46:207-215.
60. Piao S, Amaravadi RK. Targeting the lysosome in cancer. Ann N Y Acad Sci. 2016;1371:45-54.
61. Decressac M, Mattsson B, Weikop P, Lundblad M, Jakobsson J, Bjorklund A. TFEB-mediated auto- phagy rescues midbrain dopamine neurons from alpha-synuclein toxicity. Proc Natl Acad Sci U S A. 2013; 110:E1817-E1826.
62. Li X, Yao X, Zhu Y, et al. The caspase inhibitor Z- VAD-FMK alleviates endotoxic shock via inducing macrophages necroptosis and promoting MDSCs- mediated inhibition of macrophages activation. Front Immunol. 2019;10:1824.
63. Qin X, Ma D, Tan YX, Wang HY, Cai Z. The role of necroptosis in cancer: a double-edged sword? Biochim Biophys Acta Rev Cancer. 2019;1871:259-266.
64. Dong Y, Sun Y, Huang Y, Dwarakanath B, Kong L, Lu JJ. Upregulated necroptosis-pathway- associated genes are unfavorable prognostic markers in low-grade glioma and glioblastoma multiforme. Transl Cancer Res. 2019;8:821-827.
65. Zhou Z, Xu J, Huang N, Tang J, Ma P, Cheng Y. Clinical and biological significance of a necroptosis-related gene signature in glioma. Front Oncol. 2022;12:855434.
66. Kerr JF, Wyllie AH, Currie AR. Apoptosis: a basic biological phenomenon with wide-ranging im- plications in tissue kinetics. Br J Cancer. 1972;26: 239-257.
67. Bogler O, Mikkelsen T. Angiogenesis and apoptosis in glioma: two arenas for promising new therapies. J Cell Biochem. 2005;96:16-24.
68. Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell. 2011;144:646-674.
69. Jarosz-Biej M, Smolarczyk R, Cichon T, Kulach N. Tumor microenvironment as A “game changer” in cancer radiotherapy. Int J Mol Sci. 2019;20:3212.
70. Horton BL, Williams JB, Cabanov A, Spranger S, Gajewski TF. Intratumoral CD8(+) T-cell apoptosis is a major component of T-cell dysfunction and impedes antitumor immunity. Cancer Immunol Res. 2018;6:14-24.
71. Shi J, Yang N, Han M, Qiu C. Emerging roles of ferroptosis in glioma. Front Oncol. 2022;12:993316.
72. Liu T, Zhu C, Chen X, et al. Ferroptosis, as the most enriched programmed cell death process in glioma, induces immunosuppression and immu- notherapy resistance. Neuro Oncol. 2022;24: 1113-1125.
73. Yee PP, Wei Y, Kim SY, et al. Neutrophil-induced ferroptosis promotes tumor necrosis in glioblas- toma progression. Nat Commun. 2020;11:5424.
74. Liu J, Kuang F, Kang R, Tang D. Alkaliptosis: a new weapon for cancer therapy. Cancer Gene Ther. 2020;27:267-269.
75. Denton D, Kumar S. Autophagy-dependent cell death. Cell Death Differ. 2019;26:605-616.
76. Nassour J, Radford R, Correia A, et al. Autophagic cell death restricts chromosomal instability during replicative crisis. Nature. 2019;565:659-663.
77. Amaravadi RK, Kimmelman AC, Debnath J. Tar- geting autophagy in cancer: recent advances and future directions. Cancer Discov. 2019;9:1167-1181.
78. Pellegrini P, Strambi A, Zipoli C, et al. Acidic extracellular pH neutralizes the autophagy- inhibiting activity of chloroquine: implications for cancer therapies. Autophagy. 2014;10:562-571.
79. Song W, Li D, Tao L, Luo Q, Chen L. Solute carrier transporters: the metabolic gatekeepers of immune cells. Acta Pharm Sin B. 2020;10:61-78.
80. Fischer K, Hoffmann P, Voelkl S, et al. Inhibitory effect of tumor cell-derived lactic acid on human T cells. Blood. 2007;109:3812-3819.
81. Colegio OR, Chu NQ, Szabo AL, et al. Functional polarization of tumour-associated macrophages by tumour-derived lactic acid. Nature. 2014;513: 559-563.
82. Wessels I, Fischer HJ, Rink L. Dietary and phys- iological effects of zinc on the immune system. Annu Rev Nutr. 2021;41:133-175.
83. Monje M, Borniger JC, D’Silva NJ, et al. Roadmap for the emerging field of cancer neuroscience. Cell. 2020;181:219-222.
84. Chavan SS, Pavlov VA, Tracey KJ. Mechanisms and therapeutic relevance of neuro-immune communication. Immunity. 2017;46:927-942.
85. Wu Y, Hu Y, Wang B, et al. Dopamine uses the DRD5-ARRB2-PP2A signaling Axis to block the TRAF6-mediated NF-kappaB pathway and sup- press systemic inflammation. Molecular cell. 2020; 78:42-56.6.
86. Zhang B, Vogelzang A, Miyajima M, et al. B cell- derived GABA elicits IL-10(+) macrophages to limit anti-tumour immunity. Nature. 2021;599: 471-476.
Conflict of interest statement: The authors declare that the article content was composed in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Received 8 April 2023; accepted 28 July 2023 Citation: World Neurosurg. (2023) 178:e595-e640. https://doi.org/10.1016/j.wneu.2023.07.134
Journal homepage: www.journals.elsevier.com/world- neurosurgery Available online: www.sciencedirect.com 1878-8750/$ - see front matter @ 2023 Elsevier Inc. All rights reserved.
SUPPLEMENTARY DATA
A
BRCA Overall Survival
B
CESC Overall Survival
C
CHOL Overall Survival
0
Low SLC10A3 Group
0
High SLC19A3 Group
Low SLC10A3 Group
0
Logrank p=0.068
High SLC10A3 Group
Low SLC10A3 Group
High SLC10A3 Group
HR(high)=1.3
Logrank p=0.21
Logrank p=0.35
0.8
p(HR)=0.069
0.8
HR(high)=0.75
R
HR(high)=1.6
p(HR)=0.36
: n(high)=535
p(HR)=0.22
Percent survival
n(low)=535
Percent survival
n(high)=146
Percent survival
n(high)=18
0.6
0.6
n(low)=146
0.6
n(low)=18
0.4
0.4
0.4
0.2
0.2
0.2
0.0
0.0
0.0
0
50
100
150
200
250
0
50
100
150
200
0
10
20
30
40
50
60
Months
Months
Months
D
E
F
COAD Overall Survival
DLBC Overall Survival
ESCA Overall Survival
0
Low SLC10A3 Group
9
High SLC10A3 Group
LOW SLC10A3 Group
1.0
Low SLC10A3
High SLC10A3 Group
High SLC10A3 Group
Logrank p=0.083
Logrank p=0.96
Logrank p=0.38
0.8
HR(high)=1.5
p(HR)=0.086
0.8
HR(high)=1
p(HR)=0.96
0.8
HR(high)=1.2
p(HR)=0.39
Percent survival
h[high)=135
Percent survival
n(high)=91
0.6
n(low)=135
Percent survival
n(high)=23
0.6
n(low)=23
0.6
n(low)=91
0.4
0.4
0.4
0.2
0.2
0.2
0.0
0.0
0.0
0
50
100
150
0
50
100
150
200
0
20
40
60
80
100
120
Months
Months
Months
G
HNSC Overall Survival
H
KIRC Overall Survival
LAML Overall Survival
0
Low SLC10A3 Group
1.0
High SLC10A3 Group
Low SLC10A3 Group
1.0
High SLC10A3 Group
Low SLC10A3
High SLC10A3 Group
Logrank p=0.57
Logrank p=0.3
0.8
HR(high)=1.1
HR(high)=1.6
p(HR)=0.58
0.8
HR(high)=0.85
Logrank p=0.087
p(HR)=0.3
0.8
p(HR)=0.088
Percent survival
n(high)=259
n(low)=259
Percent survival
n(high)=258
n(high)=53
0.6
n(low)=258
Percent survival
nflow)=53
0.6
0.6
0.4
0.4
0.4
0.2
0.2
0.2
0.0
0.0
0.0
0
50
100
150
200
0
50
100
150
0
20
40
60
80
Months
Months
Months
J
LIHC Overall Survival
K
LUSC Overall Survival
L
O
OV Overall Survival
Low SLC10A3 Group
a
High SLC10A3 Group
Low SLC10A3 Group
9
Logrank p=0.34
High SLC10A3 Group
Low SLC10A3 Group
Logrank p=0.33
High SLC10A3 Group
HR(high)=1.2
HR(high)=1.1
Logrank p=0.4
0.8
p(HR)=0.35
0.8
p(HR)=0.33
0.8
HR(high)=1.1
p(HR)=0.4
Percent survival
n(high)=182
0.6
n(low)=182
Percent survival
n(high)=241
n(high)=212
0.6
n(low)=241
Percent survival
0.6
n(low)=212
0.4
0.4
0.4
0.2
0.2
0.2
00
0.0
0.0
0
20
40
60
80
100
120
0
50
100
150
0
50
100
150
Months
Months
Months
M
PAAD Overall Survival
N
PRAD Overall Survival
O
READ Overall Survival
0
Low SLC10A3 Group
1.0
0
Low SLC10A3 Group
High SLC10A3 Group
SICH LOW SLC10A3 Group
WHY HE Moli SLC1043 Group
High SLC10A3 Group
Logrank p=0.16
Logrank p=0.81
Logrank p=0.68
0.8
HR(high)=1.3
HR(high)=0.85
HR(high)=1.2
p(HR)=0.16
0.8
p(HR)=0.81
0.8
p(HR)=0.68
Percent survival
n(high)=89
n(high)=246
n(high)=46
n(low)=89
Percent survival
0.6
0.6
n(low)=246
Percent survival
n(low)=46
0.6
0.4
0.4
0.4
0.2
0.2
0.2
0.0
0.0
0.0
0
20
40
60
80
0
50
100
150
0
20
40
60
80
100
120
Months
Months
Months
P
SKCM Overall Survival
Q
STAD Overall Survival
R
TGCT Overall Survival
9
Low SLC10A3 Group
1.0
High SLC10A3 Group
Low SLC10A3 Group
0
High SLC10A3 Group
“LOM SLC1043 GROUP
High SLC10AS G
bup
Logrank p=0.88
.99
HR(high)=0.98
Logrank p=0.91
HR(high)=0.98
Logrank pm
0.8
0.8
0.8
HR(high)=
.99
p(HR)=0.89
n(high)=229
p(HR)=0.91
n(high)=192
p(HR)=
.99
Percent survival
Percent survival
Percent survival
n(high)
=68
0.6
n(low)=229
0.6
n(low)=192
0.6
n(low)
=68
0.4
0.4
0.4
0.2
0.2
0.2
0.0
0.0
0.0
0
100
200
300
0
20
40
60
80
100
120
0
50
100
150
200
250
Months
Months
Months
S
T
U
THCA Overall Survival
THYM Overall Survival
UCEC Overall Survival
0
Low SLC10A3:Group
0
“Low SLC10A3 Group
1.0
Low SLC1043 Group
Lpgrank p=0. 12
gh SLC10A3 Group
High SLC10A3 Group
Logrank p=0.51
Logrank p=0.58
0.8
HR(high)=2.3
0.8
IR(high)=1.6
0.8
.HR(high)=1.2
P(HR)=0.13
n(high)=255
p(HR)=0.51
P(HR)=0.58
Percent survival
n(low)=254
Percent survival
n(high)=59
Percent survival
n(high)=86
0.6
0.6
n(low)=59
0.6
n(low)=86
0.4
0.4
0.4
0.2
0.2
0.2
0.0
0.0
0.0
0
50
100
150
0
50
100
150
0
20
40
60
80
100
120
140
Months
Months
Months
V
UCS Overall Survival
0
Low SLC10A3 Group
High SLC10A3
Logrank p=0.62
0.8
HR(high)=1.2
p(HR)=0.62
Percent survival
n(high)=28
0.6
n(low)=28
0.4
0.2
0.0
0
20
40
60
80
100
120
140
Supplementary Figure 1. (continued).
organic anion transport
lipid transport -
carboxylic acid transport -
organic acid transport -
anion transmembrane transport
organic hydroxy compound transport -
divalent inorganic cation transport -
sodium ion transport -
carbohydrate derivative transport
nucleobase-containing compound transport -
monocarboxylic acid transport -
positive regulation of intracellular transport -
bile acid and bile salt transport
proton transmembrane transport -
0
transition metal ion transport -
p.adjust
0.04
chloride transport -
0.03
organophosphate ester transport
0.02
nucleotide transport -
0.01
purine nucleotide transport -
purine ribonucleotide transport -
Counts
☐
5
adenine nucleotide transport
☐
10
nucleotide-sugar transmembrane transport -
☐
15
pyrimidine nucleotide-sugar transmembrane transport -
☐
20
zinc ion transport -
intracellular cholesterol transport -
intracellular sterol transport -
anion transmembrane transporter activity
active transmembrane transporter activity
organic anion transmembrane transporter activity
secondary active transmembrane transporter activity
monovalent inorganic cation transmembrane transporter
carboxylic acid transmembrane transporter activity -
activity
organic acid transmembrane transporter activity
MF
carbohydrate derivative transmembrane transporter
monocarboxylic acid transmembrane transporter
organic hydroxy compound transmembrane transporter
nucleobase-containing compound transmembrane
activity
transporter activity
bile acid transmembrane transporter activity -
secondary active monocarboxylate transmembrane
transporter activity
8
GeneRatio
Supplementary Figure 2. Visualization of results with adjusted P values < 0. 05 related to substance transport from gene ontology/Kyoto Encyclopedia of Genes and Genomes enrichment analysis.
| Supplementary Table 1. Gene Signature of Programmed Cell Death | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Apoptosis | Necroptosis | Pyroptosis | Ferroptosis | Cuproptosis | Parthanatos | Entotic Cell Death | Netotic Cell Death | Lysosome- Dependent Cell Death | Autophagy- Dependent Cell Death | Alkaliptosis | Oxeiptosis |
| AATF | GLUD1 | BAK1 | ABCC1 | FDX1 | ARTD1 | PRKAA1 | ELANE | ABCA2 | ABL1 | IKBKB | PGAM5 |
| ABL1 | GLUD2 | BAX | ACACA | LIAS | MIF | PRKAA2 | MMP1 | ABCB9 | ABL2 | NFKB1 | KEAP1 |
| ACAA2 | ALOX15 | CASP1 | ACO1 | LIPT1 | AIFM1 | PRKAB1 | MPO | ACP2 | ACER2 | CA9 | AIFM1 |
| ACKR3 | FTH1 | CASP3 | ACSF2 | DLD | HSP70 | PRKAB2 | CAMP | ACP5 | ADRA1A | CHUK | HEBP1 |
| ACVR1 | CAPN1 | CASP4 | ACSL1 | DLAT | ARH3 | PRKAG1 | PADI4 | ADGRE2 | ADRB2 | IKBKG | AIRE |
| ACVR1B | CASP1 | CASP5 | ACSL3 | PDHA1 | RNF146 | PRKAG2 | NCX1 | AGA | AKT1 | RELA | |
| ADORA1 | BAX | CASP6 | ACSL4 | PDHB | ADPRHL2 | PRKAG3 | MIA | AP1B1 | AMBRA1 | ||
| AEN | BCL2 | CASP8 | ACSL5 | MTF1 | OGG1 | ATG5 | AP1G1 | ATF6 | |||
| AGT | FADD | CASP9 | ACSL6 | GLS | ATG7 | AP1M1 | ATG101 | ||||
| AGTR2 | RIPK1 | CHMP2A | AIFM2 | CDKN2A | BECN1 | AP1M2 | ATG13 | ||||
| AIFM1 | TNF | CHMP2B | AKR1C1 | GCSH | CDC42 | AP1S1 | ATG14 | ||||
| AKT1 | TNFRSF1A | CHMP3 | AKR1C2 | ΑΤΡΊΑ | CDH1 | AP1S2 | ATG2A | ||||
| ANXA6 | TRADD | CHMP4A | AKR1C3 | ATP7B | CTNNA1 | AP1S3 | ATG2B | ||||
| APAF1 | TRAF2 | CHMP4B | ALOX12 | SLC31A1 | CYBB | AP3B1 | ATG5 | ||||
| APPL1 | PPIA | CHMP4C | ALOX15 | MYH14 | AP3B2 | ATG7 | |||||
| AR | CAPN2 | CHMP6 | ALOX5 | RHOA | AP3D1 | ATM | |||||
| ARHGEF2 | HSP90A | CHMP7 | ATG5 | RNF146 | AP3M1 | ATP13A2 | |||||
| ARL6IP5 | IL1A | CYCS | ATG7 | ROCK1 | AP3M2 | ATP6V0A1 | |||||
| ARMC10 | TNFSF6 | ELANE | P3 | ROCK2 | AP3S1 | ATP6V0A2 | |||||
| ARRB2 | TNFRSF6 | GPX4 | BACH1 | SCAR15 | AP3S2 | ATP6V0B | |||||
| ASAH2 | CASP8 | GSDMB | CARS | UVRAG | AP4B1 | ATP6V0C | |||||
| ATF3 | JNK | GSDMC | CBS | AP4E1 | ATP6V0D1 | ||||||
| ATF4 | JAK2 | GSDMD | CD44 | AP4M1 | ATP6V0D2 | ||||||
| ATM | CAMK2 | DFNA5 | CHAC1 | AP4S1 | ATP6V0E1 | ||||||
| ATP2A1 | IL1B | GZMB | CISD1 | ARF1 | ATP6V0E2 | ||||||
| ATP2A3 | IFNG | HMGB1 | CP | ARL8B | ATP6V1A | ||||||
| ATPI | STAT3 | IL18 | CRYAB | ARSA | ATP6V1B1 | ||||||
| AVP | IRF9 | IL1A | CS | ARSB | ATP6V1B2 | ||||||
| BAD | TNFSF10 | IL1B | CYBB | ARSG | ATP6V1C1 | ||||||
| BAG3 | TNFRSF10A | IRF1 | DPP4 | ASAH1 | ATP6V1C2 | ||||||
| BAG5 | TNFRSF10B | IRF2 | EMC2 | ATP10B | ATP6V1D | ||||||
| BAG6 | CFLAR | NLRC4 | FADS2 | ATP13A2 | ATP6V1E1 | ||||||
| BAK1 | XIAP | NLRP1 | FANCD2 | ATP6AP1 | ATP6V1E2 | ||||||
| BAX | BID | NLRP2 | FDFT1 | ATP6V0A1 | ATP6V1G1 | ||||||
| BBC3 | AIFM1 | NLRP3 | FTH1 | ATP6V0A2 | ATP6V1G2 | ||||||
| BCAP31 | TRPM7 | NLRP6 | FTL | ATP6V0A4 | ATP6V1H | ||||||
| BCL10 | IFNAR1 | NLRP7 | FTMT | ATP6V0B | AUP1 | ||||||
| BCL2 | IFNAR2 | NOD1 | G6PD | ATP6V0C | BAD | ||||||
| Continues | |||||||||||
| Supplementary Table 1. Continued | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Apoptosis | Necroptosis | Pyroptosis | Ferroptosis | Cuproptosis | Parthanatos | Entotic Cell Death | Netotic Cell Death | Lysosome- Dependent Cell Death | Autophagy- Dependent Cell Death | Alkaliptosis | Oxeiptosis |
| BCL2A1 | IFNGR1 | PLCG1 | GCLC | ATP6V0D1 | BAG3 | ||||||
| BCL2L1 | IFNGR2 | PJVK | GCLM | ATP6V0D2 | BCL2 | ||||||
| BCL2L10 | TLR3 | PRKACA | GLS2 | ATP6V1H | BCL2L11 | ||||||
| BCL2L11 | TIRP | PYCARD | GOT1 | BLK | BECN1 | ||||||
| BCL2L12 | IFNB | SCAF11 | GPX4 | BLOC1S1 | BMF | ||||||
| BCL2L14 | TICAM1 | TNF | GSS | BLOC1S2 | BNIP3 | ||||||
| BCL2L2 | VDAC1 | TP53 | HMGCR | LOH12CR1 | BNIP3L | ||||||
| BCL3 | PPID | TP63 | HMOX1 | C17orf59 | BOK | ||||||
| BCLAF1 | CYLD | AIM2 | HSBP1 | BTK | C9orf72 | ||||||
| BDKRB2 | RIPK3 | GSDMA | HSPB1 | C12orf4 | CALCOCO2 | ||||||
| BDNF | MLKL | IL6 | IREB2 | CBL | CAMKK2 | ||||||
| BECN1 | TRAF5 | NOD2 | KEAP1 | CD164 | CAPN1 | ||||||
| BID | TLR4 | TIRAP | LPCAT3 | CD300A | CAPNS1 | ||||||
| BIK | RBCK1 | MAP1LC3A | CD63 | CASP1 | |||||||
| BIRC6 | HMGB1 | MAP1LC3B | CD68 | CASP3 | |||||||
| BLOC1S2 | JAK1 | MAP1LC3C | CD84 | CDC37 | |||||||
| BMF | JAK3 | MT1G | CHGA | CDK5 | |||||||
| BMP4 | TYK2 | NCOA4 | CLN3 | CDK5R1 | |||||||
| BMP5 | STAT1 | NFE2L2 | CLN5 | CHMP4A | |||||||
| BMPR1B | STAT2 | NFS1 | CLNK | CHMP4B | |||||||
| BNIP3 | STAT4 | NOX1 | CLTA | CISD2 | |||||||
| BNIP3L | STAT5A | NQO1 | CLTB | CLEC16A | |||||||
| BOK | STAT5B | OTUB1 | CLTC | CLN3 | |||||||
| BRCA1 | STAT6 | PCBP1 | CLTCL1 | CLU | |||||||
| BRCA2 | H2AFQ | PCBP2 | CLU | CPTP | |||||||
| BRSK2 | TNFAIP3 | PEBP1 | CPLX2 | CSNK2A2 | |||||||
| BTK | RNF31 | PGD | CTNS | CTSA | |||||||
| CAAP1 | CHMP2A | PHKG2 | CTSA | CTTN | |||||||
| CASP1 | CHMP2B | PRNP | CTSB | DAP | |||||||
| CASP10 | CHMP4A | PROM2 | CTSC | DAPK1 | |||||||
| CASP12 | CHMP4B | PTGS2 | CTSD | DAPK2 | |||||||
| CASP2 | CHMP6 | RPL8 | CTSE | DAPK3 | |||||||
| CASP3 | VPS4 | SAT1 | CTSF | DAPL1 | |||||||
| CASP4 | CHMP1 | SAT2 | CTSG | DCN | |||||||
| CASP5 | CHMP5 | SLC11A2 | CTSH | DDIT3 | |||||||
| CASP8 | SMPD1 | SLC1A5 | CTSK | DDRGK1 | |||||||
| CASP8AP2 | PYCARD | SLC39A14 | CTSL | DEPDC5 | |||||||
| CASP9 | NLRP3 | SLC39A8 | CTSO | DEPP | |||||||
| Continues | |||||||||||
| Supplementary Table 1. Continued | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Apoptosis | Necroptosis | Pyroptosis | Ferroptosis | Cuproptosis | Parthanatos | Entotic Cell Death | Netotic Cell Death | Lysosome- Dependent Cell Death | Autophagy- Dependent Cell Death | Alkaliptosis | Oxeiptosis |
| CAV1 | ZBP1 | SLC3A2 | CTSS | DHRSX | |||||||
| CCAR2 | IL33 | SLC40A1 | CTSV | DNM1L | |||||||
| CCK | FTL | SLC7A11 | CTSW | DRAM1 | |||||||
| CD14 | SQSTM1 | SOLE | CTSZ | DRAM2 | |||||||
| CD24 | VDAC2 | STEAP3 | DEF8 | EEF1A1 | |||||||
| CD27 | VDAC3 | TF | DNASE2 | EEF1A2 | |||||||
| CD28 | CHMP7 | TFRC | DNASE2B | EIF2AK4 | |||||||
| CD38 | PGAM5 | TP53 | ENTPD4 | EIF4G1 | |||||||
| CD3E | BIRC2 | VDAC2 | FAM98A | EIF4G2 | |||||||
| CD44 | BIRC3 | VDAC3 | FER | KIAA1324 | |||||||
| CD5 | EIF2AK2 | ZEB1 | FES | EP300 | |||||||
| CD70 | PLA2G4 | FGR | EPM2A | ||||||||
| CD74 | DNM1L | FLCN | ERCC4 | ||||||||
| CDIP1 | SPATA2 | FOXF1 | ERN1 | ||||||||
| CDKN1A | FAF1 | FTH1 | EXOC1 | ||||||||
| CDKN2D | SHARPIN | FTL | EXOC4 | ||||||||
| CEBPB | NOX2 | FUCA1 | EXOC7 | ||||||||
| CFLAR | USP21 | GAA | EXOC8 | ||||||||
| CHAC1 | PARP1 | GAB2 | FBXL2 | ||||||||
| CHCHD10 | CHMP4C | GALC | FBXO7 | ||||||||
| CHEK2 | GALNS | FBXW7 | |||||||||
| CIB1 | GATA2 | FEZ1 | |||||||||
| CIDEB | GBA | FEZ2 | |||||||||
| CLU | GCC2 | FLCN | |||||||||
| APOPT1 | GGA1 | FOXK1 | |||||||||
| COL2A1 | GGA2 | FOXK2 | |||||||||
| CRADD | GGA3 | FOXO1 | |||||||||
| CREB3 | GLA | FOXO3 | |||||||||
| CREB3L1 | GLB1 | FTH1 | |||||||||
| CRH | GM2A | FTL | |||||||||
| CRIP1 | GNPTAB | FYCO1 | |||||||||
| CSF2 | GNPTG | FZD5 | |||||||||
| CSNK2A1 | GNS | GAPDH | |||||||||
| CSNK2A2 | GUSB | GATA4 | |||||||||
| CTH | HDAC6 | GBA | |||||||||
| CTNNA1 | HEXA | GFAP | |||||||||
| CTSC | HEXB | GNAI3 | |||||||||
| CTTN | HGS | GOLGA2 | |||||||||
Continues
| Supplementary Table 1. Continued | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Apoptosis | Necroptosis | Pyroptosis | Ferroptosis | Cuproptosis | Parthanatos | Entotic Cell Death | Netotic Cell Death | Lysosome- Dependent Cell Death | Autophagy- Dependent Cell Death | Alkaliptosis | Oxeiptosis |
| CUL1 | HGSNAT | GPR137 | |||||||||
| CUL2 | HMOX1 | GPR137B | |||||||||
| CUL3 | HPS6 | GPSM1 | |||||||||
| CUL4A | HSPA8 | GSK3A | |||||||||
| CUL5 | HYAL1 | GSK3B | |||||||||
| CX3CL1 | IDS | HAX1 | |||||||||
| CX3CR1 | IDUA | HDAC6 | |||||||||
| CXCL12 | IGF2R | HERC1 | |||||||||
| CYLD | IL13 | HGF | |||||||||
| CYP1B1 | IL13RA2 | HIF1A | |||||||||
| DAB2IP | IL4 | HMGB1 | |||||||||
| DAP | IL4R | HMOX1 | |||||||||
| DAP3 | KIF1B | HSP90AA1 | |||||||||
| DAPK1 | KIT | HSPA8 | |||||||||
| DAPK2 | KXD1 | HSPB1 | |||||||||
| DAPK3 | LAMP1 | HSPB8 | |||||||||
| DAPL1 | LAMP2 | HTR2B | |||||||||
| DAXX | LAMP3 | HTRA2 | |||||||||
| DBH | LAMTOR1 | HTT | |||||||||
| DCC | LAPTM4A | HUWE1 | |||||||||
| DDIAS | LAPTM4B | IFI16 | |||||||||
| DDIT3 | LAPTM5 | IFNG | |||||||||
| DDIT4 | LAT | IKBKG | |||||||||
| DDX3X | LAT2 | IL10 | |||||||||
| DDX47 | LGALS9 | IL10RA | |||||||||
| DDX5 | LGMN | IL4 | |||||||||
| DEDD | LIPA | IRGM | |||||||||
| DEDD2 | LRRK2 | ITPR1 | |||||||||
| KIAA0141 | LYN | KAT5 | |||||||||
| DEPTOR | M6PR | KAT8 | |||||||||
| DIABLO | MAN2B1 | KDM4A | |||||||||
| DIDO1 | MANBA | KDR | |||||||||
| DNAJA1 | MAP1LC3A | KEAP1 | |||||||||
| DNAJC10 | MAP6 | KIF25 | |||||||||
| DNM1L | MCOLN1 | KLHL22 | |||||||||
| DPF2 | MFSD8 | KLHL3 | |||||||||
| DYRK2 | MILR1 | LACRT | |||||||||
| E2F1 | MRGPRX2 | LAMP1 | |||||||||
Continues
| Supplementary Table 1. Continued | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Apoptosis | Necroptosis | Pyroptosis | Ferroptosis | Cuproptosis | Parthanatos | Entotic Cell Death | Netotic Cell Death | Lysosome- Dependent Cell Death | Autophagy- Dependent Cell Death | Alkaliptosis | Oxeiptosis |
| E2F2 | MT3 | LAMP2 | |||||||||
| EDA2R | MYH9 | LAMP3 | |||||||||
| EIF2AK3 | NAGA | LAMTOR1 | |||||||||
| ELL3 | NAGLU | LAMTOR2 | |||||||||
| ENO1 | NAGPA | LAMTOR3 | |||||||||
| EP300 | NAPSA | LAMTOR4 | |||||||||
| EPHA2 | NCOA4 | LAMTOR5 | |||||||||
| EPO | NDEL1 | LARP1 | |||||||||
| ERBB3 | NEDD4 | LEP | |||||||||
| ERCC6 | NEU1 | LEPR | |||||||||
| ERN1 | NPC1 | LGALS8 | |||||||||
| ERN2 | NPC2 | LRRK2 | |||||||||
| ERO1L | NR4A3 | LRSAM1 | |||||||||
| ERP29 | PDPK1 | LZTS1 | |||||||||
| EYA1 | PIK3C3 | MAP1LC3A | |||||||||
| EYA2 | PIK3CD | MAP1LC3B | |||||||||
| EYA3 | PIK3CG | MAP1LC3C | |||||||||
| EYA4 | PIP4K2A | MAP3K7 | |||||||||
| FADD | PIP4K2B | MAPK15 | |||||||||
| FAF1 | TMEM55B | MAPK3 | |||||||||
| FAIM | PLA2G15 | MAPK8 | |||||||||
| FAIM2 | PLA2G3 | MAPT | |||||||||
| FAM162A | PLEKHM1 | MCL1 | |||||||||
| FAS | PLEKHM2 | MEFV | |||||||||
| FASLG | PPT1 | MET | |||||||||
| FASTK | PPT2 | MFN2 | |||||||||
| FBH1 | PSAP | MFSD8 | |||||||||
| FBXW7 | PSAPL1 | MID2 | |||||||||
| FEM1B | PTGDR | MIR199A1 | |||||||||
| FGA | PTGDS | MIRLET7B | |||||||||
| FGB | RAB34 | MLST8 | |||||||||
| FGF10 | RAB3A | MT3 | |||||||||
| FGFR1 | RAB7A | MTCL1 | |||||||||
| FGFR3 | RAC2 | MTDH | |||||||||
| FGG | C13orf18 | MTM1 | |||||||||
| FHIT | S100A13 | MTMR3 | |||||||||
| FIGNL1 | SCARB2 | MTMR4 | |||||||||
| FIS1 | SGSH | MTMR8 | |||||||||
Continues
| Supplementary Table 1. Continued | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Apoptosis | Necroptosis | Pyroptosis | Ferroptosis | Cuproptosis | Parthanatos | Entotic Cell Death | Netotic Cell Death | Lysosome- Dependent Cell Death | Autophagy- Dependent Cell Death | Alkaliptosis | Oxeiptosis |
| FNIP2 | SLC11A1 | MTMR9 | |||||||||
| FXN | SLC11A2 | MTOR | |||||||||
| FYN | SLC17A5 | NCOA4 | |||||||||
| FZD9 | SMPD1 | NEDD4 | |||||||||
| G0S2 | SNAP23 | NLRP6 | |||||||||
| GABARAP | SNAPIN | NOD1 | |||||||||
| GATA1 | SNX16 | NOD2 | |||||||||
| GATA4 | SNX4 | NPC1 | |||||||||
| GCLM | SORL1 | NPRL2 | |||||||||
| GDNF | SORT1 | NRBP2 | |||||||||
| GFRAL | SPAG9 | NUPR1 | |||||||||
| GGCT | SPHK2 | OPTN | |||||||||
| GHITM | SQSTM1 | ORMDL3 | |||||||||
| GNAI2 | STXBP1 | OSBPL7 | |||||||||
| GNAI3 | STXBP2 | PAFAH1B2 | |||||||||
| GPER1 | SUMF1 | PARK7 | |||||||||
| GPX1 | SYK | PHB2 | |||||||||
| GRINA | SYTL4 | PHF23 | |||||||||
| DFNA5 | TCIRG1 | PIK3C2A | |||||||||
| GSK3A | TFEB | PIK3C3 | |||||||||
| GSK3B | TMEM106B | PIK3CA | |||||||||
| GSKIP | TPP1 | PIK3CB | |||||||||
| GSTP1 | UNC13D | PIK3R2 | |||||||||
| GZMB | VAMP7 | PIM2 | |||||||||
| HDAC1 | VAMP8 | PINK1 | |||||||||
| HERPUD1 | VPS33A | PIP4K2A | |||||||||
| HGF | VPS33B | PIP4K2B | |||||||||
| HIC1 | VPS4A | PIP4K2C | |||||||||
| HIF1A | WASH3P | PJVK | |||||||||
| HINT1 | ZFYVE16 | PLEKHF1 | |||||||||
| HIP1 | PLK2 | ||||||||||
| HIP1R | PLK3 | ||||||||||
| HIPK1 | POLDIP2 | ||||||||||
| HIPK2 | PRKAA1 | ||||||||||
| HMGB2 | PRKAA2 | ||||||||||
| HMOX1 | PRKAB1 | ||||||||||
| HNRNPK | PRKAB2 | ||||||||||
| HRAS | PRKACA | ||||||||||
Continues
| Supplementary Table 1. Continued | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Apoptosis | Necroptosis | Pyroptosis | Ferroptosis | Cuproptosis | Parthanatos | Entotic Cell Death | Netotic Cell Death | Lysosome- Dependent Cell Death | Autophagy- Dependent Cell Death | Alkaliptosis | Oxeiptosis |
| HRK | PRKAG1 | ||||||||||
| HSPA1A | PRKAG2 | ||||||||||
| HSPA1B | PRKAG3 | ||||||||||
| HSPB1 | PRKD1 | ||||||||||
| HTRA2 | PRKN | ||||||||||
| HTT | PSAP | ||||||||||
| HYAL2 | PTPN22 | ||||||||||
| HYOU1 | PYCARD | ||||||||||
| ICAM1 | QSOX1 | ||||||||||
| IFI16 | RAB39B | ||||||||||
| IFI27 | RAB3GAP1 | ||||||||||
| IFI27L1 | RAB3GAP2 | ||||||||||
| IFI27L2 | RAB7A | ||||||||||
| IFI6 | RAB8A | ||||||||||
| IFNB1 | RALB | ||||||||||
| IFNG | RASIP1 | ||||||||||
| IGF1 | RB1CC1 | ||||||||||
| IKBKE | JK1 | ||||||||||
| IL12A | FAM134C | ||||||||||
| IL19 | RHEB | ||||||||||
| IL1A | RIPK2 | ||||||||||
| IL1B | HsT2591 | ||||||||||
| IL2 | RNF152 | ||||||||||
| IL20RA | RNF41 | ||||||||||
| IL33 | RNF5 | ||||||||||
| IL4 | ROCK1 | ||||||||||
| IL6R | RPTOR | ||||||||||
| IL7 | RRAGA | ||||||||||
| INCA1 | RRAGB | ||||||||||
| ING2 | RRAGC | ||||||||||
| ING5 | RRAGD | ||||||||||
| INHBA | KIAA0226 | ||||||||||
| INHBB | RUFY4 | ||||||||||
| INS | SCFD1 | ||||||||||
| ITGA6 | SCOC | ||||||||||
| ITGAM | SEC22B | ||||||||||
| ITGAV | SESN1 | ||||||||||
| ITM2C | SESN2 | ||||||||||
Continues
| Supplementary Table 1. Continued | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Apoptosis | Necroptosis | Pyroptosis | Ferroptosis | Cuproptosis | Parthanatos | Entotic Cell Death | Netotic Cell Death | Lysosome- Dependent Cell Death | Autophagy- Dependent Cell Death | Alkaliptosis | Oxeiptosis |
| ITPR1 | SESN3 | ||||||||||
| ITPRIP | SH3BP4 | ||||||||||
| IVNS1ABP | SH3GLB1 | ||||||||||
| JAK2 | SIRT1 | ||||||||||
| JMY | SIRT2 | ||||||||||
| JUN | SLC38A9 | ||||||||||
| KDM1A | SMCR8 | ||||||||||
| KITLG | SMG1 | ||||||||||
| KRT18 | SNCA | ||||||||||
| KRT8 | SNRNP70 | ||||||||||
| LCK | SNX32 | ||||||||||
| LGALS12 | SNX5 | ||||||||||
| LGALS3 | SNX6 | ||||||||||
| LRRK2 | SOGA1 | ||||||||||
| LTBR | SOGA3 | ||||||||||
| LY96 | SPTLC1 | ||||||||||
| MADD | SPTLC2 | ||||||||||
| MAEL | SQSTM1 | ||||||||||
| MAGEA3 | SREBF1 | ||||||||||
| MAP2K5 | SREBF2 | ||||||||||
| MAP3K5 | STAT3 | ||||||||||
| MAPK7 | STING | ||||||||||
| MAPK8 | STK11 | ||||||||||
| MAPK8IP1 | STUB1 | ||||||||||
| MAPK8IP2 | SUPT5H | ||||||||||
| MAPK9 | SVIP | ||||||||||
| MARCH7 | SYNPO2 | ||||||||||
| MAZ | TAB2 | ||||||||||
| MCL1 | TAB3 | ||||||||||
| MDM2 | TBC1D14 | ||||||||||
| MELK | TBC1D25 | ||||||||||
| MFF | TBK1 | ||||||||||
| MIF | TEX264 | ||||||||||
| MIR132 | TFEB | ||||||||||
| MIR15A | TICAM1 | ||||||||||
| MIR16-1 | TIGAR | ||||||||||
| MIR17 | TLK2 | ||||||||||
| MIR21 | TMEM150A | ||||||||||
Continues
| Supplementary Table 1. Continued | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Apoptosis | Necroptosis | Pyroptosis | Ferroptosis | Cuproptosis | Parthanatos | Entotic Cell Death | Netotic Cell Death | Lysosome- Dependent Cell Death | Autophagy- Dependent Cell Death | Alkaliptosis | Oxeiptosis |
| MIR210 | TMEM150B | ||||||||||
| MIR221 | TMEM150C | ||||||||||
| MIR222 | TMEM39A | ||||||||||
| MIR26B | TMEM39B | ||||||||||
| MIR27B | TMEM59 | ||||||||||
| MIR449A | TOMM7 | ||||||||||
| MKNK2 | TP53 | ||||||||||
| MLH1 | TP53INP1 | ||||||||||
| MLLT11 | TP53INP2 | ||||||||||
| MMP9 | TPCN1 | ||||||||||
| MNT | TPCN2 | ||||||||||
| MOAP1 | TREM2 | ||||||||||
| MPV17L | TRIB3 | ||||||||||
| MSH2 | TRIM13 | ||||||||||
| MSH6 | TRIM14 | ||||||||||
| MSX1 | TRIM21 | ||||||||||
| MUC1 | TRIM22 | ||||||||||
| MUL1 | TRIM27 | ||||||||||
| MYBBP1A | TRIM34 | ||||||||||
| NACC2 | TRIM38 | ||||||||||
| NANOS3 | TRIM5 | ||||||||||
| NBN | TRIM6 | ||||||||||
| NCK1 | TRIM65 | ||||||||||
| NCK2 | TRIM68 | ||||||||||
| NDUFA13 | TRIM8 | ||||||||||
| NDUFS3 | TRIML1 | ||||||||||
| NFATC4 | TRIML2 | ||||||||||
| NFE2L2 | TSC1 | ||||||||||
| NGF | TSC2 | ||||||||||
| NGFR | TSPO | ||||||||||
| NKX3-1 | UBA5 | ||||||||||
| NLE1 | UBQLN1 | ||||||||||
| NME5 | UBQLN2 | ||||||||||
| NMT1 | UBQLN4 | ||||||||||
| NOC2L | UCHL1 | ||||||||||
| NOG | UFC1 | ||||||||||
| NOL3 | UFL1 | ||||||||||
| NONO | UFM1 | ||||||||||
Continues
| Supplementary Table 1. Continued | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Apoptosis | Necroptosis | Pyroptosis | Ferroptosis | Cuproptosis | Parthanatos | Entotic Cell Death | Netotic Cell Death | Lysosome- Dependent Cell Death | Autophagy- Dependent Cell Death | Alkaliptosis | Oxeiptosis |
| NOS3 | ULK1 | ||||||||||
| NOX1 | USP10 | ||||||||||
| NR4A2 | USP13 | ||||||||||
| NUPR1 | USP30 | ||||||||||
| OPA1 | USP33 | ||||||||||
| P2RX4 | USP36 | ||||||||||
| P2RX7 | UVRAG | ||||||||||
| P4HB | VDAC1 | ||||||||||
| PAK2 | VPS13C | ||||||||||
| PAK7 | VPS13D | ||||||||||
| PARK7 | VPS26A | ||||||||||
| PARP1 | VPS26B | ||||||||||
| PARP2 | VPS35 | ||||||||||
| PAWR | WAC | ||||||||||
| PCGF2 | WDFY3 | ||||||||||
| PDCD10 | WDR24 | ||||||||||
| PDCD5 | WDR41 | ||||||||||
| PDCD6 | WDR6 | ||||||||||
| PDIA3 | WDR81 | ||||||||||
| PDK1 | WIPI2 | ||||||||||
| PDK2 | ZC3H12A | ||||||||||
| PDPK1 | ZKSCAN3 | ||||||||||
| PDX1 | ZMPSTE24 | ||||||||||
| PEA15 | |||||||||||
| PELI3 | |||||||||||
| PERP | |||||||||||
| PF4 | |||||||||||
| PHIP | |||||||||||
| PHLDA3 | |||||||||||
| PIAS4 | |||||||||||
| PIDD1 | |||||||||||
| PIH1D1 | |||||||||||
| PIK3R1 | |||||||||||
| PINK1 | |||||||||||
| PLAGL2 | |||||||||||
| PLAUR | |||||||||||
| PLEKHF1 | |||||||||||
| PMAIP1 | |||||||||||
Continues
| Supplementary Table 1. Continued | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Apoptosis | Necroptosis | Pyroptosis | Ferroptosis | Cuproptosis | Parthanatos | Entotic Cell Death | Netotic Cell Death | Lysosome- Dependent Cell Death | Autophagy- Dependent Cell Death | Alkaliptosis | Oxeiptosis |
| PML | |||||||||||
| POLB | |||||||||||
| POU4F1 | |||||||||||
| POU4F2 | |||||||||||
| PPARD | |||||||||||
| PPIA | |||||||||||
| PPIF | |||||||||||
| PPM1F | |||||||||||
| PPP1CA | |||||||||||
| PPP1R13B | |||||||||||
| PPP1R15A | |||||||||||
| PPP2R1B | |||||||||||
| PPP3CC | |||||||||||
| PPP3R1 | |||||||||||
| PRDX2 | |||||||||||
| PRELID1 | |||||||||||
| PRKCA | |||||||||||
| PRKCD | |||||||||||
| PRKDC | |||||||||||
| PRKN | |||||||||||
| PRKRA | |||||||||||
| PRODH | |||||||||||
| PSEN1 | |||||||||||
| PSMD10 | |||||||||||
| PSME3 | |||||||||||
| PTEN | |||||||||||
| PTGIS | |||||||||||
| PTH | |||||||||||
| PTPMT1 | |||||||||||
| PTPN1 | |||||||||||
| PTPN2 | |||||||||||
| PTPRC | |||||||||||
| PTTG1IP | |||||||||||
| PYCARD | |||||||||||
| PRO2195 | |||||||||||
| RACK1 | |||||||||||
| RAF1 | |||||||||||
| RB1 | |||||||||||
Continues
| Supplementary Table 1. Continued | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Apoptosis | Necroptosis | Pyroptosis | Ferroptosis | Cuproptosis | Parthanatos | Entotic Cell Death | Netotic Cell Death | Lysosome- Dependent Cell Death | Autophagy- Dependent Cell Death | Alkaliptosis | Oxeiptosis |
| RB1CC1 | |||||||||||
| RBCK1 | |||||||||||
| RELA | |||||||||||
| RET | |||||||||||
| RFFL | |||||||||||
| RHOT1 | |||||||||||
| RHOT2 | |||||||||||
| RIPK1 | |||||||||||
| RIPK3 | |||||||||||
| RNF183 | |||||||||||
| RNF186 | |||||||||||
| RNF34 | |||||||||||
| RNF41 | |||||||||||
| RPL11 | |||||||||||
| RPL26 | |||||||||||
| RPS27L | |||||||||||
| RPS3 | |||||||||||
| RPS6KB1 | |||||||||||
| RPS7 | |||||||||||
| RRP8 | |||||||||||
| RTKN2 | |||||||||||
| C22orf29 | |||||||||||
| S100A8 | |||||||||||
| S100A9 | |||||||||||
| SCG2 | |||||||||||
| SCN2A | |||||||||||
| SCRT2 | |||||||||||
| SELK | |||||||||||
| VIMP | |||||||||||
| SENP1 | |||||||||||
| C17orf47 | |||||||||||
| SERINC3 | |||||||||||
| SERPINE1 | |||||||||||
| SFN | |||||||||||
| SFPQ | |||||||||||
| SFRP1 | |||||||||||
| SFRP2 | |||||||||||
| SGMS1 | |||||||||||
Continues
| Supplementary Table 1. Continued | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Apoptosis | Necroptosis | Pyroptosis | Ferroptosis | Cuproptosis | Parthanatos | Entotic Cell Death | Netotic Cell Death | Lysosome- Dependent Cell Death | Autophagy- Dependent Cell Death | Alkaliptosis | Oxeiptosis |
| SGPL1 | |||||||||||
| SGPP1 | |||||||||||
| SH3RF1 | |||||||||||
| SHH | |||||||||||
| SHISA5 | |||||||||||
| SIAH1 | |||||||||||
| SIAH2 | |||||||||||
| SIRT1 | |||||||||||
| SIVA1 | |||||||||||
| SKIL | |||||||||||
| SLC25A5 | |||||||||||
| SLC35F6 | |||||||||||
| SLC9A3R1 | |||||||||||
| SMAD3 | |||||||||||
| SNAI1 | |||||||||||
| SNAI2 | |||||||||||
| SNW1 | |||||||||||
| SOD1 | |||||||||||
| SOD2 | |||||||||||
| SORT1 | |||||||||||
| SP100 | |||||||||||
| SRC | |||||||||||
| SRPX | |||||||||||
| SST | |||||||||||
| SSTR3 | |||||||||||
| ST20 | |||||||||||
| STK11 | |||||||||||
| STK24 | |||||||||||
| STK25 | |||||||||||
| STK3 | |||||||||||
| STK4 | |||||||||||
| STRADB | |||||||||||
| STX4 | |||||||||||
| STYXL1 | |||||||||||
| SYVN1 | |||||||||||
| TAF9 | |||||||||||
| TAF9B | |||||||||||
| TCF7L2 | |||||||||||
Continues
| Supplementary Table 1. Continued | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Apoptosis | Necroptosis | Pyroptosis | Ferroptosis | Cuproptosis | Parthanatos | Entotic Cell Death | Netotic Cell Death | Lysosome- Dependent Cell Death | Autophagy- Dependent Cell Death | Alkaliptosis | Oxeiptosis |
| TERT | |||||||||||
| TFDP1 | |||||||||||
| TFDP2 | |||||||||||
| TFPT | |||||||||||
| TGFB1 | |||||||||||
| TGFB2 | |||||||||||
| TGFBR1 | |||||||||||
| THBS1 | |||||||||||
| TICAM1 | |||||||||||
| TICAM2 | |||||||||||
| TIMM50 | |||||||||||
| TIMP3 | |||||||||||
| TLR3 | |||||||||||
| TLR4 | |||||||||||
| TM2D1 | |||||||||||
| TMBIM1 | |||||||||||
| TMBIM6 | |||||||||||
| TMC8 | |||||||||||
| TMEM102 | |||||||||||
| TMEM109 | |||||||||||
| TMEM117 | |||||||||||
| TMEM14A | |||||||||||
| TMEM161A | |||||||||||
| TNF | |||||||||||
| TNFAIP3 | |||||||||||
| TNFRSF10A | |||||||||||
| TNFRSF10B | |||||||||||
| TNFRSF10C | |||||||||||
| TNFRSF12A | |||||||||||
| TNFRSF1A | |||||||||||
| TNFRSF1B | |||||||||||
| TNFRSF25 | |||||||||||
| TNFSF10 | |||||||||||
| TNFSF12 | |||||||||||
| TOPORS | |||||||||||
| TP53 | |||||||||||
| TP53BP2 | |||||||||||
| TP63 | |||||||||||
Continues
| Supplementary Table 1. Continued | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Apoptosis | Necroptosis | Pyroptosis | Ferroptosis | Cuproptosis | Parthanatos | Entotic Cell Death | Netotic Cell Death | Lysosome- Dependent Cell Death | Autophagy- Dependent Cell Death | Alkaliptosis | Oxeiptosis |
| TP73 | |||||||||||
| TPD52L1 | |||||||||||
| TPT1 | |||||||||||
| TRADD | |||||||||||
| TRAF1 | |||||||||||
| TRAF2 | |||||||||||
| TRAF7 | |||||||||||
| TRAP1 | |||||||||||
| TRIAP1 | |||||||||||
| TRIB3 | |||||||||||
| TRIM32 | |||||||||||
| TRIM39 | |||||||||||
| TXNDC12 | |||||||||||
| TYROBP | |||||||||||
| UACA | |||||||||||
| UBB | |||||||||||
| UBE2K | |||||||||||
| UBE4B | |||||||||||
| UBQLN1 | |||||||||||
| UMOD | |||||||||||
| UNC5B | |||||||||||
| URI1 | |||||||||||
| USP28 | |||||||||||
| USP47 | |||||||||||
| VDAC2 | |||||||||||
| VNN1 | |||||||||||
| WDR35 | |||||||||||
| WNT4 | |||||||||||
| WWOX | |||||||||||
| XBP1 | |||||||||||
| YAP1 | |||||||||||
| YBX3 | |||||||||||
| YWHAB | |||||||||||
| YWHAE | |||||||||||
| YWHAG | |||||||||||
| YWHAH | |||||||||||
| YWHAQ | |||||||||||
| YWHAZ | |||||||||||
Continues
| Supplementary Table 1. Continued | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Apoptosis | Necroptosis | Pyroptosis | Ferroptosis | Cuproptosis | Parthanatos | Entotic Cell Death | Netotic Cell Death | Lysosome- Dependent Cell Death | Autophagy- Dependent Cell Death | Alkaliptosis | Oxeiptosis |
| ZC3HC1 | |||||||||||
| ZDHHC3 | |||||||||||
| ZMYND11 | |||||||||||
| ZNF205 | |||||||||||
| ZNF385A | |||||||||||
| ZNF385B | |||||||||||
| ZNF622 | |||||||||||
| ZSWIM2 | |||||||||||
| Supplementary Table 2. Results of GO/KEGG Enrichment Analysis Related to Substance Transport | ||||||
|---|---|---|---|---|---|---|
| Ontology | Identification No. | Description | GeneRatio | BgRatio | Adjusted P Value | Count |
| BP | GO: 0015711 | Organic anion transport | 23/134 | 482/18,670 | 1.24E-09 | 23 |
| BP | GO: 1901264 | Carbohydrate derivative transport | 11/134 | 76/18,670 | 7.48E-09 | 11 |
| BP | GO: 0015721 | Bile acid and bile salt transport | 8/134 | 27/18,670 | 8.50E-09 | 8 |
| BP | GO: 0015849 | Organic acid transport | 14/134 | 333/18,670 | 4.03E-05 | 14 |
| BP | GO: 0046942 | Carboxylic acid transport | 14/134 | 333/18,670 | 4.03E-05 | 14 |
| BP | GO: 0098656 | Anion transmembrane transport | 13/134 | 288/18,670 | 4.52E-05 | 13 |
| BP | GO: 0006869 | Lipid transport | 14/134 | 365/18,670 | 8.36E-05 | 14 |
| BP | GO: 0015850 | Organic hydroxy compound transport | 12/134 | 262/18,670 | 8.36E-05 | 12 |
| BP | GO: 0006814 | Sodium ion transport | 11/134 | 218/18,670 | 8.70E-05 | 11 |
| BP | GO: 0090481 | Pyrimidine nucleotide-sugar transmembrane transport | 4/134 | 13/18,670 | 0.000244 | 4 |
| BP | GO:0015780 | Nucleotide-sugar transmembrane transport | 4/134 | 14/18,670 | 0.000286 | 4 |
| BP | GO: 0015718 | Monocarboxylic acid transport | 9/134 | 162/18,670 | 0.000292 | 9 |
| BP | GO: 0051503 | Adenine nucleotide transport | 4/134 | 16/18,670 | 0.000464 | 4 |
| BP | GO: 0015868 | Purine ribonucleotide transport | 4/134 | 17/18,670 | 0.000577 | 4 |
| BP | GO: 0015865 | Purine nucleotide transport | 4/134 | 18/18,670 | 0.000706 | 4 |
| BP | GO: 0015931 | Nucleobase-containing compound transport | 10/134 | 241/18,670 | 0.000903 | 10 |
| BP | GO: 0006862 | Nucleotide transport | 4/134 | 24/18,670 | 0.00202 | 4 |
| BP | GO: 0032388 | Positive regulation of intracellular transport | 8/134 | 215/18,670 | 0.0106 | 8 |
| BP | GO: 1902600 | Proton transmembrane transport | 7/134 | 163/18,670 | 0.0111 | 7 |
| BP | GO: 0072511 | Divalent inorganic cation transport | 11/134 | 489/18,670 | 0.0314 | 11 |
| BP | GO: 0032366 | Intracellular sterol transport | 3/134 | 26/18,670 | 0.0314 | 3 |
| BP | GO: 0032367 | Intracellular cholesterol transport | 3/134 | 26/18,670 | 0.0314 | 3 |
| BP | GO: 0006829 | Zinc ion transport | 3/134 | 27/18,670 | 0.0334 | 3 |
| BP | GO: 0015748 | Organophosphate ester transport | 5/134 | 105/18,670 | 0.0339 | 5 |
| BP | GO: 0006821 | Chloride transport | 5/134 | 108/18,670 | 0.0373 | 5 |
| BP | GO: 0000041 | Transition metal ion transport | 5/134 | 112/18,670 | 0.0408 | 5 |
| BP | GO: 0035672 | Oligopeptide transmembrane transport | 2/134 | 10/18,670 | 0.0544 | 2 |
| BP | GO: 0070838 | Divalent metal ion transport | 10/134 | 483/18,670 | 0.0575 | 10 |
| BP | GO: 0006857 | Oligopeptide transport | 2/134 | 11/18,670 | 0.05747 | 2 |
| BP | GO: 0032377 | Regulation of intracellular lipid transport | 2/134 | 11/18,670 | 0.0575 | 2 |
| BP | GO: 0032380 | Regulation of intracellular sterol transport | 2/134 | 11/18,670 | 0.0575 | 2 |
| BP | GO: 0032383 | Regulation of intracellular cholesterol transport | 2/134 | 11/18,670 | 0.0575 | 2 |
| BP | GO: 0015867 | ATP transport | 2/134 | 12/18,670 | 0.0647 | 2 |
| BP | GO: 0035725 | Sodium ion transmembrane transport | 5/134 | 140/18,670 | 0.0663 | 5 |
| BP | GO: 0032386 | Regulation of intracellular transport | 9/134 | 423/18,670 | 0.0675 | 9 |
| BP | GO: 0032365 | Intracellular lipid transport | 3/134 | 43/18,670 | 0.0681 | 3 |
| BP | GO: 1902476 | Chloride transmembrane transport | 4/134 | 88/18,670 | 0.0690 | 4 |
| BP | GO: 0008643 | Carbohydrate transport | 5/134 | 148/18,670 | 0.0761 | 5 |
| BP | GO: 0090316 | Positive regulation of intracellular protein transport | 5/134 | 153/18,670 | 0.0821 | 5 |
GO, gene ontology; KEGG, kyoto encyclopedia of genes and genomes; BP, biological process; MF, molecular function.
Continues
| Supplementary Table 2. Continued | ||||||
|---|---|---|---|---|---|---|
| Ontology | Identification No. | Description | GeneRatio | BgRatio | Adjusted P Value | Count |
| BP | GO: 0030301 | Cholesterol transport | 4/134 | 100/18,670 | 0.0864 | 4 |
| MF | GO: 0008509 | Anion transmembrane transporter activity | 23/132 | 327/17,697 | 1.05E-13 | 23 |
| MF | GO: 0008514 | Organic anion transmembrane transporter activity | 19/132 | 211/17,697 | 2.72E-13 | 19 |
| MF | GO: 0015291 | Secondary active transmembrane transporter activity | 18/132 | 237/17,697 | 1.78E-11 | 18 |
| MF | GO: 0015125 | Bile acid transmembrane transporter activity | 7/132 | 17/17,697 | 1.15E-09 | 7 |
| MF | GO: 0022804 | Active transmembrane transporter activity | 19/132 | 362/17,697 | 1.25E-09 | 19 |
| MF | GO: 1901505 | Carbohydrate derivative transmembrane transporter activity | 8/132 | 44/17,697 | 4.72E-08 | 8 |
| MF | GO: 0005342 | Organic acid transmembrane transporter activity | 12/132 | 153/17,697 | 5.58E-08 | 12 |
| MF | GO: 0046943 | Carboxylic acid transmembrane transporter activity | 12/132 | 153/17,697 | 5.58E-08 | 12 |
| MF | GO: 0015932 | Nucleobase-containing compound transmembrane transporter activity | 7/132 | 43/17,697 | 8.85E-07 | 7 |
| MF | GO: 1901618 | Organic hydroxy compound transmembrane transporter activity | 7/132 | 44/17,697 | 9.58E-07 | 7 |
| MF | GO: 0008028 | Monocarboxylic acid transmembrane transporter activity | 7/132 | 50/17,697 | 2.06E-06 | 7 |
| MF | GO: 0015355 | Secondary active monocarboxylate transmembrane transporter activity | 4/132 | 11/17,697 | 2.02E-05 | 4 |
| MF | GO: 0015077 | Monovalent inorganic cation transmembrane transporter activity | 14/132 | 382/17,697 | 2.08E-05 | 14 |
| GO, gene ontology; KEGG, kyoto encyclopedia of genes and genomes; BP, biological process; MF, molecular function. | ||||||
| Supplementary Table 3. Correlation Analysis Between SLC10A3 Expression and Marker Genes of Immune Cells Using Data in TIMER Database | |||||
|---|---|---|---|---|---|
| Description | Gene Markers | LGG | |||
| None | Purity | ||||
| Cor | P Value | Cor | P Value | ||
| B cell | CD19 | 0.376 | < 0.001 | 0.344 | < 0.001 |
| KRT20 | 0.068 | NS | 0.092 | < 0.05 | |
| CD27 | 0.211 | < 0.001 | 0.233 | < 0.001 | |
| CD38 | 0.025 | NS | -0.030 | NS | |
| CD8+ T cell | CD8A | 0.317 | < 0.001 | 0.244 | < 0.001 |
| CD8B | 0.188 | < 0.001 | 0.120 | < 0.001 | |
| PTPRC | 0.567 | < 0.001 | 0.533 | < 0.001 | |
| Tfh | BCL6 | 0.141 | < 0.001 | 0.169 | < 0.001 |
| ICOS | 0.454 | < 0.001 | 0.425 | < 0.001 | |
| CXCR5 | 0.271 | < 0.001 | 0.256 | < 0.001 | |
| Th1 | TBX21 | 0.398 | < 0.001 | 0.416 | < 0.001 |
| STAT4 | -0.008 | NS | -0.046 | NS | |
| IL12RB2 | 0.005 | NS | -0.054 | NS | |
| IL27RA | 0.219 | < 0.001 | 0.231 | < 0.001 | |
| STAT1 | 0.422 | < 0.001 | 0.422 | < 0.001 | |
| IFNG | 0.288 | < 0.001 | 0.251 | < 0.001 | |
| TNF | 0.199 | < 0.001 | 0.165 | < 0.001 | |
| Th2 | GATA3 | 0.472 | < 0.001 | 0.445 | < 0.001 |
| CCR3 | 0.343 | < 0.001 | 0.318 | < 0.001 | |
| STAT6 | 0.500 | < 0.001 | 0.450 | < 0.001 | |
| STAT5A | 0.556 | < 0.001 | 0.506 | < 0.001 | |
| Th9 | TGFBR2 | 0.521 | < 0.001 | 0.496 | < 0.001 |
| IRF4 | 0.160 | < 0.001 | 0.159 | < 0.001 | |
| SPI1 | 0.606 | < 0.001 | 0.563 | < 0.001 | |
| Th17 | STAT3 | 0.522 | < 0.001 | 0.542 | < 0.001 |
| IL23R | 0.166 | < 0.001 | 0.157 | < 0.001 | |
| IL21R | 0.165 | < 0.001 | 0.202 | < 0.001 | |
| IL17A | 0.053 | NS | 0.043 | NS | |
| Th22 | CCR10 | 0.240 | < 0.001 | 0.254 | < 0.001 |
| AHR | 0.374 | < 0.001 | 0.338 | < 0.001 | |
| Treg | FOXP3 | 0.047 | NS | 0.076 | NS |
| IL2RA | 0.290 | < 0.001 | 0.322 | < 0.001 | |
| CCR8 | 0.212 | < 0.001 | 0.232 | < 0.001 | |
SLC10A3, solute carrier family 10 member 3; TIMER, tumor immune estimation resource; LGG, lower grade glioma; None, correlation without adjustment; Purity, correlation adjusted by tumor purity; Cor, Spearman’s correlation; NS, not statistically significant (P > 0.05); Tfh, follicular helper T cell; Th, T helper cell; Treg, regulatory T cell; TAM, tumor-associated macrophage; NK, natural killer.
Continues
| Supplementary Table 3. Continued | |||||
|---|---|---|---|---|---|
| Description | Gene Markers | LGG | |||
| None | Purity | ||||
| Cor | P Value | Cor | P Value | ||
| T cell exhaustion | PDCD1 | 0.568 | < 0.001 | 0.557 | < 0.001 |
| CTLA4 | 0.304 | < 0.001 | 0.258 | < 0.001 | |
| LAG3 | 0.348 | < 0.001 | 0.371 | < 0.001 | |
| HAVCR2 | 0.585 | < 0.001 | 0.540 | < 0.001 | |
| Macrophage | CD68 | 0.590 | < 0.001 | 0.558 | < 0.001 |
| ITGAM | 0.511 | < 0.001 | 0.452 | < 0.001 | |
| M1 | NOS2 | -0.012 | NS | -0.020 | NS |
| IRF5 | 0.563 | < 0.001 | 0.516 | < 0.001 | |
| PTGS2 | 0.194 | < 0.001 | 0.156 | < 0.001 | |
| CD80 | 0.468 | < 0.001 | 0.459 | < 0.001 | |
| CD86 | 0.558 | < 0.001 | 0.507 | < 0.001 | |
| M2 | FCGR3A | 0.562 | < 0.001 | 0.546 | < 0.001 |
| ARG1 | 0.132 | < 0.001 | 0.076 | NS | |
| MRC1 | -0.046 | NS | -0.091 | < 0.05 | |
| MS4A4A | 0.437 | < 0.001 | 0.429 | < 0.001 | |
| CLEC10A | 0.313 | < 0.001 | 0.337 | < 0.001 | |
| CD163 | 0.482 | < 0.001 | 0.491 | < 0.001 | |
| IL10 | 0.426 | < 0.001 | 0.389 | < 0.001 | |
| TAM | CCL2 | 0.475 | < 0.001 | 0.441 | < 0.001 |
| CD80 | 0.468 | < 0.001 | 0.459 | < 0.001 | |
| CD86 | 0.558 | < 0.001 | 0.507 | < 0.001 | |
| CCR5 | 0.600 | < 0.001 | 0.569 | < 0.001 | |
| Monocyte | CD14 | 0.513 | < 0.001 | 0.480 | < 0.001 |
| FCGR3B | 0.341 | < 0.001 | 0.300 | < 0.001 | |
| CSF1R | 0.426 | < 0.001 | 0.353 | < 0.001 | |
| Neutrophil | CEACAM8 | 0.060 | NS | 0.043 | NS |
| FUT4 | 0.479 | < 0.001 | 0.440 | < 0.001 | |
| ITGAM | 0.511 | < 0.001 | 0.452 | < 0.001 | |
| NK cell | XCL1 | 0.331 | < 0.001 | 0.284 | < 0.001 |
| CD7 | 0.563 | < 0.001 | 0.515 | < 0.001 | |
| KIR3DL1 | 0.076 | NS | 0.066 | NS | |
| Dendritic cell | CD1C | 0.302 | < 0.001 | 0.300 | < 0.001 |
| THBD | 0.365 | < 0.001 | 0.376 | < 0.001 | |
| ITGAX | 0.514 | < 0.001 | 0.449 | < 0.001 | |
SLC10A3, solute carrier family 10 member 3; TIMER, tumor immune estimation resource; LGG, lower grade glioma; None, correlation without adjustment; Purity, correlation adjusted by tumor purity; Cor, Spearman’s correlation; NS, not statistically significant (P > 0.05); Tfh, follicular helper T cell; Th, T helper cell; Treg, regulatory T cell; TAM, tumor-associated macrophage; NK, natural killer.