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Neurological Research
Neurological Research A Journal of Progress in Neurosurgery, Neurology and Neurosciences
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APOBEC3C is a novel target for the immune treatment of lower-grade gliomas
Shufa Zhao, Yuntao Li, Jie Xu & Liang Shen
To cite this article: Shufa Zhao, Yuntao Li, Jie Xu & Liang Shen (2024) APOBEC3C is a novel target for the immune treatment of lower-grade gliomas, Neurological Research, 46:3, 227-242, DOI: 10.1080/01616412.2023.2287340
To link to this article: https://doi.org/10.1080/01616412.2023.2287340
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APOBEC3C is a novel target for the immune treatment of lower-grade gliomas
Shufa Zhaoª*, Yuntao Lia*, Jie Xua and Liang Shen (D b
ªDepartment of Neurosurgery, Huzhou Cent Hospital, Affiliated Cent Hospital Huzhou University, Huzhou, Zhejiang, China; bDepartment of Neurosurgery, The affiliated Changzhou Second People’s Hospital of Nanjing Medical University, Changzhou, Jiangsu, China
ABSTRACT
Background: Apolipoprotein B mRNA editing catalytic polypeptide-like (APOBEC) type 3C (A3C) has been identified as a cancer molecular biomarker in the past decade. However, the practical role of A3C in lower-grade gliomas (LGGs) in improving the clinical outcome remains unclear. This study aims to discuss the function of A3C in immunotherapy in LGGs.
Methods: The RNA-Sequencing (RNA-seq) and corresponding clinical data were extracted from UCSC Xena and the results were verified in the Chinese Glioma Genome Atlas (CGGA). Weighted gene co-expression network analysis (WGCNA) was used for screening A3C-related genes. Comprehensive bioinformation analyses were performed and multiple levels of expres- sion, survival rate, and biological functions were assessed to explore the functions of A3C. Results: A3C expression was significantly higher in LGGs than in normal tissues but lower than in glioblastoma (GBM), indicating its role as an independent prognosis predictor for LGGs. Twenty-eight A3C-related genes were found with WGCNA for unsupervised clustering analysis and three modification patterns with different outcomes and immune cell infiltration were identified. A3C and the A3C score were also correlated with immune cell infiltration and the expression of immune checkpoints. In addition, the A3C score was correlated with increased sensitivity to chemotherapy. Single-cell RNA (scRNA) analysis indicated that A3C most probably expresses on immune cells, such as T cells, B cells and macrophage.
Conclusions: A3C is an immune-related prognostic biomarker in LGGs. Developing drugs to block A3C could enhance the efficiency of immunotherapy and improve disease survival.
Abbreviation: A3C: Apolipoprotein B mRNA editing catalytic polypeptide-like (APOBEC) type 3C; LGGs: lower-grade gliomas; CGGA: Chinese Glioma Genome Atlas; WGCNA: Weighted gene co- expression network analysis; scRNA: Single-cell RNA; HGG: higher-grade glioma; OS: overall survival; TME: tumor microenvironment; KM: Kaplan-Meier; PFI: progression-free interval; IDH: isocitrate dehydrogenase; ROC: receiver operating characteristic; GS: gene significance; MM: module member- ship; TIMER: Tumor IMmune Estimation Resource; GSVA: gene set variation analysis; ssGSEA: single- sample gene-set enrichment analysis; PCA: principal component analysis; AUC: area under ROC curve; HAVCR2: hepatitis A virus cellular receptor 2; PDCD1: programmed cell death 1; PDCD1LG2: PDCD1 ligand 2; PTPRC: protein tyrosine phosphatase receptor type C; ACC: Adrenocortical carci- noma; BLCA: Bladder Urothelial Carcinoma;BRCA: Breast invasive carcinoma; CESC: Cervical squa- mous cell carcinoma and endocervical adenocarcinoma; CHOLCholangiocarcinoma; COADColon adenocarcinoma; DLBC: Lymphoid Neoplasm Diffuse Large B-cell Lymphoma; ESCA: Esophageal carcinoma; GBM: Glioblastoma multiforme; HNSC: Head and Neck squamous cell carcinoma; KICH: Kidney Chromophobe; KIRC: Kidney renal clear cell carcinoma; KIRP: Kidney renal papillary cell carcinoma; LAML: Acute Myeloid Leukemia; LGG: Brain Lower Grade Glioma; LIHC: Liver hepatocel- lular carcinoma; LUAD: Lung adenocarcinoma; LUSC: Lung squamous cell carcinoma; MESO: Mesothelioma; OV: Ovarian serous cystadenocarcinoma; PAAD: Pancreatic adenocarcinoma; PCPG: Pheochromocytoma and Paraganglioma; PRAD: Prostate adenocarcinoma; READ: Rectum adenocarcinoma; SARC: Sarcoma; SKCM: Skin Cutaneous Melanoma; STAD: Stomach adenocarci- noma; TGCT: Testicular Germ Cell Tumors; THCA: Thyroid carcinoma; THYM: Thymoma; UCEC: Uterine Corpus Endometrial Carcinoma; UCS: Uterine Carcinosarcoma; UVM: Uveal Melanoma
ARTICLE HISTORY Received 27 July 2023 Accepted 21 November 2023
KEYWORDS
Glioma; APOBEC3C; immune treatment; weighted gene co-expression network analysis (WGCNA); single-cell RNAS
1 Background
Glioma is a malignant tumor that seriously affects the lives of patients. The prognosis of patients with lower- grade gliomas (LGGs), also known as diffuse low- grade and intermediated-grade gliomas (WHO grade II and III), is slightly better than that of higher-grade gliomas (HGG) [1]. Glioblastoma, one type of HGG,
shows more aggressive biological characteristics, with a median survival time of fewer than 15 months [2]. Additionally, the overall survival (OS) of LGG patients ranges between 5 to 10 years, and most LGGs will progress to HGG within 10 years [1].
Despite the rising costs in glioma treatment and research, limited improvement in clinical prognosis has
CONTACT Liang Shen soochowneuro@163.com
Department of Neurosurgery, The affiliated Changzhou Second People’s Hospital of Nanjing Medical University, Changzhou, Jiangsu, China
*These authors have contributed equally to this work and share first authorship.
+ Supplemental data for this article can be accessed online at https://doi.org/10.1080/01616412.2023.2287340
been achieved. As a result, multiple novel approaches to glioma treatment have been proposed over the past sev- eral decades. For example, Chinese patent medicine ther- apy, electromagnetic field therapy, immunotherapy and other treatments have been studied and could potentially be supplemented to traditional treatment methods (including surgery, radiotherapy and chemotherapy) [3-6]. Immunotherapy is an available treatment method for some solid tumors, but diverse clinical studies have demonstrated that its therapeutic efficacy in glioma is limited [7,8]. The genesis of a poor response to immu- notherapy probably lurks in disorganized blood vessels in the glioma microenvironment. Therefore, immunother- apy in glioma should be modified to achieve reasonable therapeutic efficacy. Consequently, exploring new bio- markers with huge potential in affecting the prognosis of glioma for immunotherapy has sparked a heated controversy.
The apolipoprotein B mRNA editing catalytic poly- peptide-like (APOBEC) family of proteins contains 11 members that can be classified into four families, and play a significant role in innate antiviral immunity [9]. APOBEC3 (A3) is one of the four families and contains 7 types (A3A, A3B, A3C, A3DE, A3F, A3G and A3H). Its encoding genes are located on chromosome 22 [10,11]. A3C is a pervasive research target of HIV-1 and HBV according to the published studies [12-15]. In addition to its regulatory role in viral reverse transcription, A3C has also been found to be altered in malignant cancers [16,17]. A few studies have investigated the function of A3C in the development of glioma cells [18,19], but the function of A3C in glioma immunotherapy is rarely mentioned. In order to further explore the function of A3C, we first performed immunofluorescence in glioma tissue, and found that A3C located where CD31 was expressed. CD31 was identified as a cellmarker of T cell [20] or endothelial cell [21], which indicated that A3C may be involved in the immune response of glioma. This study attempts to uncover the mechanisms of A3C in affecting the prognosis of LGGs. We hypothesize that gene expression reprogramming following A3C expres- sion might modulate glioma cell growth.
2 Methods
2.1 Immunofluorescence
The LGG samples [22] were donated by the participants who underwent surgical tumor removal at the depart- ment of neurosurgery, the affiliated Changzhou Second People’s Hospital of Nanjing Medical University. Paraffin sections of glioma were dewaxed and tissue antigen repair was performed with citrate buffer (0.01 M citrate, 0.05% Tween 20, PH6.0) at 100℃ for 20 min. Three percent H2 O2 was used to eliminate endogenous peroxidase activity at room temperature for 10 min. The sections were blocked with blocking buffer (5%BSA,0.2% Triton
X-100 in PBS) for 1 hour at room temperature and incubated for 24 hours at 4℃ in a primary antibody diluted in blocking buffer. After that, sections were incu- bated in fluorescent secondary antibody diluted in block- ing buffer. Last, sections were mounted onto glass slides with an antifading mounting medium and DAPI (S2110, Solarbio, China). Antibodies used: Anti-A3C (ab221874, Abcam, U.S.A.), Anti-CD31 (ab9498, Abcam, U.S.A.), Goat Anti-Rabbit IgG (Alexa Fluor® 488) (ab150077, Abcam, U.S.A.), Goat Anti-Mouse IgG (Alexa Fluor® 647) (ab150115, Abcam, U.S.A.).
2.2 Prognostic effect of A3C in gliomas
For a preliminary understanding of the relationship between A3C and malignant cancers, the RNA-seq data of 33 types of cancers and the corresponding clinical information were downloaded from the UCSC Xena (https://xena.ucsc.edu/). A3C expres- sion in cancer tissues was primarily assessed by comparing it with normal tissues if the data was available. Then, A3C in gliomas was further ana- lyzed in The Cancer Genome Atlas (TCGA), which was identified as the internal group, while the data from CGGA was chosen as the external group. Only five normal brain samples were included in TCGA. For an accurate result, the 206 normal cortex tissue samples from Genotype-Tissue Expression (https://commonfund.nih.gov/GTEx/) were added to the differential gene expression ana- lysis. Finally, 211 normal samples, 529 LGGs sam- ples and 168 GBM samples were included in the boxplot to compare the differential expressions. Kaplan-Meier (KM) analysis was employed to eval- uate the relationship between A3C expression, overall survival (OS) and progression-free interval (PFI) in TCGA. Next, the predictive power was verified according to the prognosis prediction in glioma subtypes, the independence from prognostic factors (age, gender, WHO grade, histology, isoci- trate dehydrogenase (IDH) mutation status, and 1p/19 codeletion status) and the 1-, 3-, and 5-year time-dependent receiver operating characteristic (ROC) values.
2.3 A3C-related gene identification
A3C-related genes were identified by weighted gene co-expression network analysis (WGCNA), a powerful method to screen key modules and genes [23]. The main steps of WGCNA were described in the previous study [24]. The gene set most associated with A3C was regarded as the A3C module eigengene and was included for further analysis. The genes with gene significance (GS) >0.5 and module membership (MM) >0.8 in the eigengene module were identified as A3C- related genes.
2.4 Unsupervised clustering analysis and validation
Twenty-eight A3C-related genes were adopted for unsu- pervised clustering analysis based on the gene expression. A consensus clustering algorithm was used to confirm the number of clusters and determine the stability of the samples [25]. KM survival curves were plotted to com- pare the prognosis between different clusters. Moreover, the distribution of three kinds of data was described in clusters. For further verification, a heat map was plotted to describe the distribution of the three data types in different clusters. These three types of data include the distribution of immune cell (dendritic cells, macrophage cells, neutrophils, CD8 T cells, CD4 T cells and B cells) infiltration from the Tumor IMmune Estimation Resource (TIMER) database (https://cistrome.shi nyapps.io/timer/), the key molecular prognostic markers and clinical information of LGGs, and the differential expression of A3C-related genes.
2.5 Gene set variation analysis (GSVA) and single-sample gene-set enrichment analysis (ssGSEA)
GSVA was performed using the ‘GSVA’ package to explore the difference in biological processes between the clusters [26]. A curated gene set of ‘c2.cp.kegg.v7.2.symbols’ acquired from the GSEA database (https://www.gsea-msigdb.org/gsea/index. jsp) was used for GSVA analysis. Kyoto encyclo- pedia of genes and genomes (KEGG) was consid- ered significant if the adjusted P value was less than 0.05. The top 20 KEGG pathways from the cluster with the best prognosis and the cluster with the worst prognosis were plotted on the heat map. Twenty-three immune cells computed by reference gene sets were provided by the pre- vious study [27]. Immune cell infiltration, includ- ing activated B cells, activated CD4 T cells, activated CD8 T cells, activated dendritic cells and so on, was quantified by the relative abun- dance in glioma tumor microenvironment (TME) using the ssGSEA algorithm.
2.6 Validation of prognosis in clusters
Although the samples were assigned to different clus- ters, the difference in prognosis between clusters was still unexplored. Firstly, principal component analysis (PCA) according to the A3C-related genes expression was performed to assess the sample distribution. PCA 1 and PCA 2 were included to calculate a score that we defined as the A3C score [25]. Secondly, KM survival plot curves were used to explore the relationship between the A3C score and OS in TCGA and CGGA. In addition, the prognostic prediction inde- pendence of the A3C score was assessed using uni- variate and multivariate Cox proportional hazard
regression analyses. Thirdly, the relationship between A3C expression and A3C score was also investigated.
2.7 Immune cell infiltration and chemotherapeutic drug sensitivity
The difference in immune cell infiltration among the clusters based on the A3C-related gene expression was explored. The immune score, stromal score and com- bined score provided by Yoshihara et al [28] and Ceccarelli et al. [29] were visualized in clusters to portray the difference. Correlations between A3C expression and immune cell infiltration were analyzed by Pearson corre- lation. The same was performed to analyze the correla- tion between A3C expression and 20 immune checkpoints expression (CD274, hepatitis A virus cellular receptor 2 (HAVCR2), programmed cell death 1 (PDCD1), and so on). Chemotherapy is an essential anti- tumor treatment. However, drug resistance leads to treat- ment failure in malignant tumors. Therefore, the func- tion of the expression of A3C-related genes, expression of A3C, and A3C score in chemotherapeutic drug sensitivity were assessed with CellMiner (https://discover.nci.nih. gov/cellminer/home.do) [30,31].
2.8 Acquisition and processing of scRNA-seq of LGGs
For a further exploration of the function of A3C in LGGs, scRNA-seq data was analyzed using Seurat (version 4.1.1). The scRNA-seq data was extracted from Gene Expression Omnibus (GSE1387941, GSE117891). Harmony was the algorithms of scRNA data integration [32]. After cell filtering and quality control 22,871 cells with 27,568 genes were included. The RunPCA function was used to project the cells into principle components in Seurat, and then RunUMAP function was included for a 2-dimensional visualization [33]. The most variable 2000 genes were selected for achieving 15 clusters with a resolution of 0.4. Cells were assigned into 11 major cell clusters according to the cell type-specific markers: Oligodendrocyte (‘ERMN’, ‘GRIA2’) [34,35], Astrocyte (‘AQP4’, ‘CHI3LI’) [36,37], Macrophage (‘TREM1’, ‘C1QC’) [38,39], Neuro (‘TOP2A’) [40], Neural progenitor cell (‘PBK’) [41], Cancer cell (‘RSPH1’) [39], Conventional dendritic cell 2b (‘S100A9’) [42], T cell (‘IL32’, ‘CD2’) [43,44], B cell (‘MZB1’) [45], Endothelial cell (‘CLDN5’) [46], and one cluster failed to group in any cell type.
2.9 Statistical analysis
The R (version 4.1.2) was used for statistical ana- lyses. Survival rates were calculated using KM curve with a log-rank test. Differentially expressed genes (DEGs) were screened by Wilcox test.
DAPI
A3C
CD31
merge
The expression of A3C in cancers and corresponding tissues
Prognostic role of A3C in glioma and subtypes, verified in CGGA
A3C is prognostic marker in LGG
Screening A3C related genes using WGCNA
28 A3C related genes, GS>0.5, |MM|>0.8
Unsupervised clustering analysis and validation
Three clusters
Assess the difference between clusters
Prognostic role: Survival analysis
Gene function enrichment analysis: DEGs and GSVA
Immune assessment: Immune cells infiltration, immune score, stromal score, combined score
Assess the distribution of samples: PCA
Calculate A3C score according PCA 1 and PCA 2
Prognostic role: Survival analysis, independence assessment
Correlation assessment, Chemotherapeutic drug sensitivity
scRNA analysis
Prognostic predictors were identified by univariate and multivariate Cox regression analysis. Then, (receiver operating characteristic) ROC curves
were adopted for assessing the predictive accuracy. P value < 0.05 was considered statistically significant.
a
Type E Normal Tumor
**
*
**
8
6
O
A3C expression
4
2
0
1
+
ACC
BLCA
BRCA
CESC
CHOL
COAD
DLBC
ESCA
GBM
HNSC
KICH
KIRC
LAML
LGG
LIHC
LUAD
LUSC
MESO
OV
PAAD
PCPG
PRAD
READ
SARC
SKCM
STAD
TGCT
THCA
THYM
UCEC
UCS
UVM
b
Type Normal @LGG @GBM
C
high
low
p<0.001
d
high
low
p=0.055
1.00
1.001
6
0.75
0.75
A3C expression
0.50
0.50
4.
8
3 Ő
0.25
0.25
2
0.00
0.00
0
2
4
6
8
Time(years)
10
12
14
16
18
20
0
2
Time (years)
4
6
8
high
345 101 35
19
10
5
2
1
0
0
0
high- 83
10
1
0
0
0
low
346 178 63
38
17
14
7
4
1
0
0
low.
84
15
2
1
0
Normal
LGG
GBM
0
2
4
6
Time (years)
8
10
12
14
16
18
20
0
2
Time(years)
4
6
8
e
high
low
p=0.012
f
high
low
p<0.001
g
high
- low
p<0.001
1.00
1.00-
1.001
0.75
0.75
0.75
旺 0.50
0.50-
PF
0.50
8
0.25
0.25
0.25-
p<0.001
0.00
0.00
0.00
0
1
Time (years)
2
3
4
0
2
4
6
8
Time (years)
10
12
14
16
18
20
0
2
4
6
Time (years)
8
10
12
14
high- 83
14
5
2
high-
262 116 47
30
17
10
4
3
1
0
0
low
0
83
25
7
2
1
low-
262
138
48
26
10
9
5
2
0
0
0
high.
low
261
262
79
112
28
29
16
8
Unico
NOT
0
14
1
0
1
0
1
Time (years)
2
3
4
0
2
4
6
8
10
12
14
16
18
0
2
4
Time (years)
8
Time (years)
20
6
10
12
14
h
1.00-
high
low
p<0.001
i
1.00-
+
high
= low
p=0.111
j
1.00-
high
low
p<0.001
0.75
0.75
0.75
8 0.50
00
0.50
8 0.50
0.25.
0.25
0.25.
0.00
0.00
0.00
0
2
4
Time (years)
6
8
10
12
0
2
4
Time (years)
6
8
10
0
2
4
Time (years)
6
8
10
12
high-462
179
87
45
14
3
low
0
462
306
179
111
22
0
high
173
32
10
4
1
0
high-
285
187
104
50
18
5
66
low
174
45
15
9
2
1
low
0
288
220
136
92
59
19
0
0
2
4
Time(years)
6
8
10
12
0
2
4
Time (years)
6
8
10
0
2
4
Time (years)
6
8
10
12
3 Results
3.1 The expression of A3C in cancers and prognostic role in glioma
The immunofluorescence was double stained for A3C and CD31, showing extensive colocalization (Figure 1). Figure 2 shows the comprehensive biological analysis flowchart. In TCGA, 23 kinds of cancers corresponded
to normal tissue RNA-seq data, among which 13 can- cers had a higher expression of A3C than normal tis- sues, and only two cancers had a lower expression in tumor tissues (Figure 3(a)). The expression of A3C in LGG samples is higher than in normal samples, but it is lower than in GBM (Figure 3(b)). The Survival curve indicated that A3C is a poor biomarker for glioma (Figure 3(c)), and A3C has little effect on OS and
a
b
pvalue
Hazard ratio
pvalue
Hazard ratio
Glioma
<0.001
2.430(2.175-2.716)
Glioma
<0.001
1.239(1.159-1.326)
GBM
0.059
1.226(0.992-1.514)
GBM
0.047
1.098(1.001-1.203)
LGG
<0.001
2.299(1.907-2.770)
LGG
<0.001
1.306(1.181-1.445)
IDH Mutation type
<0.001
1.807(1.357-2.406)
IDH Mutation type
0.113
1.092(0.979-1.217)
IDH Wild type
<0.001
1.454(1.252-1.690)
IDH Wild type
0.001
1.160(1.059-1.272)
1p/19q
Codeletion
0.002
2.265(1.350-3.802)
1p/19q
Codeletion
0.171
0.845(0.663-1.076)
1p/19q
Non-codeletion
<0.001 2.286(2.019-2.589)
1p/19q
Non-codeletion
0.004
1.124(1.037-1.218)
1.0
2.0
4.0
0.71
1.0
1.41
Hazard ratio
Hazard ratio
c
d
Hazard ratio
e
pvalue
pvalue
Hazard ratio
1.0
Age
<0.001 1.057(1.040-1.073)
<0.001
1.045(1.026-1.064)
GenderM
0.700 1.079(0.732-1.591)
0.315 1.232(0.820-1.852)
True positive rate
0.8
Grade !!
<0.001 3.173(2.066-4.875)
0.001
2.150(1.351-3.422)
0.6
Histology
0.005 1.378(1.101-1.725)
+
0.175 0.819(0.614-1.093)
0.4
IDH Wild type
<0.001 6.647(4.435-9.962)
<0.001
2.691(1.565-4.629)
1p/19q Non-codeletion
<0.001 2.625(1.595-4.319)
0.044
1.933(1.019-3.666)
0.2
5- year (AUC=0.702)
0.0
3- year (AUC=0.762)
A3C
<0.001 2.345(1.908-2.882)
-
<0.001
1.787(1.366-2.338)
1- year (AUC=0.826)
0
2
4
6
8
0
1
2
3
4
0.0
0.2
0.4
0.6
0.8
1.0
Hazard ratio
Hazard ratio
False positive rate
f
pvalue
Hazard ratio
g
pvalue
Hazard ratio
h
1.0
Age
0.005 1.018(1.005-1.032)
0.011
1.015(1.003-1.028)
Gender M
0.916 0.986(0.765-1.271)
0.980
1.003(0.773-1.302)
True positive rate
0.8
Grade |
<0.0013.143(2.387-4.140)
<0.001
2.921(2.204-3.872)
0.6
Histology
<0.0011.838(1.564-2.161)
0.099
1.285(0.953-1.733)
0.4
IDH Wild type
<0.0012.183(1.666-2.860)
0.078
1.309(0.970-1.767)
1p/19q Non-codeletion
<0.0013.620(2.573-5.093)
0.041
1.939(1.026-3.662)
0.2
5- year (AUC=0.676)
<0.0011.604(1.408-1.826)
0.0
3- year (AUC=0.661)
A3C
+
0.003
1.245(1.075-1.443)
1- year (AUC=0.629)
0
1
2
3
4
5
0
1
2
3
0.0
0.2
0.4
0.6
0.8
1.0
Hazard ratio
Hazard ratio
False positive rate
progression-free interval in GBM (Figure 3(d,e)). However, A3C plays a prognostic role in LGGs (Figure 3(f,g)). Likewise, a similar function was vali- dated in CGGA (Figure 3(h-j)). Moreover, univariate and multivariate Cox proportional hazard regression analyses (Figure 4(a,b)) indicated that the prognostic role of A3C is stable in LGGs.
3.2 Independent prognostic role of the expression of A3C
According to the training cohort, A3C is identified as an independent prognostic gene from age, gen- der, WHO grade, histology, IDH mutation and 1p/ 19q codeletion status (Figure 4(c,d)). The area
under ROC curve value of A3C in predicting the 1-, 3-, and 5-year OS were 0.826, 0.762, and 0.702, respectively, which implied a mild prognostic accu- racy in the training cohort (Figure 4(e)). In the testing cohorts, A3C was also identified as an inde- pendent predictor and had valuable area under ROC curve (AUC) values (Figure 4(f-h)).
3.3 Identification of A3C related genes by WGCNA
After excluding the samples with incomplete clinical information, 424 LGG samples from TCGA were left. Subsequently, 3047 genes were identified as variant genes with variance was greater than the quartiles and were included in the co-expression network. All
a
Cluster Dendrogram
b
Eigengene adjacency heatmap
c
MM vs. GS
1.0
1
0.8
Cor=0.86, p<0.001
0.8
Height
0.8
0.6
0.6
A3C
0.6
GS for OS
y.
0
0.4
0.4
2
0
5
0
0.2
Dynamic Tree Cut
0.0
0
Merged
dynamic
1
0.2
0.4
0.6
0.8
A3C
MM in greenyellow module
d
consensus matrix k=3
e
1.00-
p<0.001
cluster
1
WN 3
A
0.75
B
Survival probability
C
0.50
0.25
0.00
I
0
2
4
6
8
10
12
14
16
18
20
Time (years)
Number at risk
cluster
A
175
60
28
15
6
4
2
0
0
0
0
B
155
62
26
17
8
6
4
1
0
0
0
C
94
25
14
7
3
2
1
1
1
0
0
0
2
4
6
8
10
12
14
16
18
20
Time (years)
the samples were clustered by Pearson’s correlation coefficient. Finally, 402 LGG samples were selected in a targeted cluster by removing the outliers. The soft threshold value to construct a scale-free network was determined to be four (R2 = 0.94, slope= - 1.4). Dynamic tree clipping was used to screen out 10 modules, and the green-yellow module, having a closer relationship with A3C than other modules, was considered biologically significant (Figure 5(a,b)). The correlation coefficient between MM and GS was 0.86 (Figure 5(c)). Twenty-eight genes that had a | MM| > 0.8 and a p <0.001 were deemed as A3C- related genes, which were prognostic genes based on the mortality analyses conducted using Cox propor- tional hazard ratios and KM analyses.
3.4 Sample cluster and survival analysis
424 samples were classified into three modification patterns based on the expression of 28 A3C-related genes by unsupervised clustering (Figure 5(d)). Three patterns were also termed clusters A-C, including 175
samples in cluster A, 155 samples in cluster B, and 94 samples in cluster C. Survival analyses revealed sig- nificant differences between the clusters. Cluster B had the best prognosis with a median survival time of about 12 years, while cluster C demonstrated the worst prognosis with a median survival time of close to 4 years (Figure 5(e)).
3.5 Clinical features and immune cell infiltration analysis in A3C-related gene modification patterns
A3C is known for its immunomodulatory function in HIV-1. However, little is known about whether A3C has an equal immunomodulatory effect in malignant tumors. The infiltration of six types of immune cells, including dendritic, macrophage, neutrophil, CD8 T cell, CD4 T cell and B cell, presents a distinct character in the three clusters, especially in cluster B and cluster C. Likewise, several prognosis-related clinical features, such as 1p19q codeletion, IDH mutation and WHO grade, varied across the three clusters, especially in
234
Q
Dendritic
Dendritic
1
Macrophage
b
Neutrophil
CD8 T cell
0
S. ZHAO ET AL.
4
CD4 T cell
-1
0
1
B cell
Macrophage
-2
Cluster
KEGG VIRAL MYOCARDITIS
APOBEC3C
1
0.4
1p19q Codeletioon
IDH Mutation
C
B
-0.6
WHO Grade
0
Age
Cluster
Neutrophil
1
KEGG VIRAL MYOCARDITIS
ALOX5AP
ARHGDIB
0
ARPC1B
CD8 T cell
1
NATURAL_KILLER_CELL_MEDIATED_CYTOTOXICITY
LEUKOCYTE_TRANSENDOTHELIAL_MIGRATION JAK_STAT_SIGNALING_PATHWAY
B2M
0
C1QA
CD4 T cell
TOLL_LIKE_RECEPTOR_SIGNALING_PATHWAY
APOPTOSIS
Cluster
C1QC
1
C3
B_CELL_RECEPTOR_SIGNALING_PATHWAY
0
CD53
B cell
1
COMPLEMENT_AND_COAGULATION_CASCADES
CYTOSOLIC_DNA_SENSING_PATHWAY
CD74
CLIC1
0
CTSA
APOBEC3C
CYTOKINE_CYTOKINE_RECEPTOR_INTERACTION
1
CYBA
HEMATOPOIETIC_CELL_LINEAGE
FCER1G
0
GMFG
1p19q Codeletion
1
TYPE ___ DIABETES_MELLITUS
HAVCR2
HCLS1
0
GRAFT_VERSUS_HOST_DISEASE
HLA-DRA
IDH Mutation
1
AUTOIMMUNE_THYROID_DISEASE
ALLOGRAFT_REJECTION
HLA-E
4
LAPTM5
0
WHO Grade
2
INTESTINAL_IMMUNE_NETWORK_FOR_IGA_PRODUCTION
NPC2
2
LEISHMANIA_INFECTION
OLFML3
SYSTEMIC_LUPUS_ERYTHEMATOSUS
S100A11
1
Age
SASH3
0
1
ANTIGEN_PROCESSING_AND_PRESENTATION
ASTHMA
SERPINA1
0
-2
PRIMARY_IMMUNODEFICIENCY
SLC39A1
Cluster
SPI1
ImU A
TMEM109
C
-4
TMSB4X
GSVA items were displayed according to clusters B and C.
T cell, B cell) infiltrations, and distribution of 1p/19q codeletion, IDH mutation, WHO grade and age in three clusters. (b) the top 20
Figure 6. (a) the expression of A3C and 28 A3C-related genes, immune cells (dendritic, macrophage, neutrophil, CD8 T cell, CD4
cluster B and cluster C. The 28 A3C-related genes had a higher expression in cluster C, indicating a poorer prognosis than the other clusters (Figure 6(a)). Notably, cluster B was associated with the best prognosis, while cluster C indicated the worst prognosis. DEGs between clusters B and C were further analyzed by GSVA for functional biological annotation. The top 20 GSVA items were visualized, and the result implies that many immune-related pathways, including natural killer cell- mediated cytotoxicity, B cell receptor signaling pathway, primary immunodeficiency and so on, occurred due to the gene expression difference between the two clusters (Figure 6(b)). According to the GSEA results put forward
by Charoentong et al. [27], the infiltration of 23 subpo- pulations of immune cells related to adaptive immunity and innate immunity was assessed in three clusters. The results implied that 22 immune cell subpopulations have a different infiltration in different clusters, except for type 2 T-helper cells (Figure 7(a)). PCA in Figure 7(b) revealed three principal components based on the dis- tribution of the samples in three clusters. The three groups were combined in pairs, and then the DEGs were screened. Finally, a total of 167 genes with inter- secting differential expressions were obtained (Figure 7(c)). Patients with higher A3C-related gene scores demonstrated a markedly prolonged survival
a
Cluster
A
B
C
1.00
ns
Immune infiltration
0.75
0.50
0.25
0.00
Activated B cell
Activated CD4 T cell
Activated CD8 T cell
Activated dendritic cell’
CD56bright natural killer cell’
CD56dim natural killer cell
Eosinophil
Gamma delta T cell’
Immature B cell’
Immature dendritic cell
MDSC
Macrophage
Mast cell’
Monocyte
Natural killer T cell’
Natural killer cell
Neutrophil
Plasmacytoid dendritic cell’
Regulatory T cell
T follicular helper cell’
Type 1 T helper cell ’
Type 17 T helper cell’
Type 2 T helper cell
b
c
d
p<0.001
APOBEC3C score
1.004
Low
4
Cluster
A
Survival probability
High
B
C
B-A
1022
C-A
0.75-
2
0.50-
PC2
17
250
167
336
OD
13
0.25-
0
APOBEC3C score
0.00
0
2
4
6
8
10
12
14
16
18
20
-2
Number at risk
Time (years)
C-B
Low-
249
84
40
23
9
6
3
1
1
0
0
-10
-5
0
5
10
High-
175
63
28
16
8
6
4
1
0
0
0
PC1
0
2
4
6
8
10
12
Time (years)
14
16
18
20
e
f
g
1.00-
p<0.001
APOBEC3C score
Survival probability
Low
pvalue
Hazard ratio
pvalue
Hazard ratio
0.75
High
Age
<0.001
1.089(1.063-1.115)
0.009
1.016(1.004-1.028)
0.50
Gender M
0.564
0.863(0.524-1.422)
0.705
1.051(0.812-1.361)
0.25
Grade !!!
0.002 2.453(1.383-4.352)
<0.001 2.942(2.224-3.893)
IDH Wild type
0.054
1.910(0.988-3.693)
0.017
1.418(1.065-1.888)
APOBEC3C Score
0.00
0
2
4
6
8
10
12
14
1p/19q
Non-codeletion
0.302
1.460(0.712-2.996)
<0.001 2.306(1.550-3.432)
Number at risk
Time (years)
Low{ 247
143
87
44
21
9
2
0
APOBEC3C score Low <0.001 3.317(1.634-6.734)
.
<0.001 2.034(1.499-2.759)
High
264
229
171
127
79
47
10
0
0
2
4
6
Time (years)
8
10
12
14
0
1
1234
5
6
0
1
2
3
Hazard ratio
Hazard ratio
time compared to those with lower scores (Figure 7(d,e)). Moreover, the A3C score was identified as an indepen- dent factor of clinical characteristics and molecular fea- tures (Figure 7(f,g)). High A3C expression samples were more likely to have a higher percent weight of low A3C score than that of low A3C expression samples (83% vs 35%) (Figure 8(a)). Samples in cluster C accompanied with the worst outcome were almost assigned to the high A3C group and got an A3C score (Figure 8(b,c). In Figure 8(d-f), the A3C expression, immune score, stro- mal score and combined score in cluster A, cluster B and cluster C were significantly different. The correlation
between A3C score and immune cell infiltration was equivalent to the correlation between A3C and immune cell infiltration in Figure 9. The A3C score revealed a strong co-expression relationship with nature killer T cell, natural killer cell, myeloid-derived suppressor cell (MDSC), immature B cell, activated dendritic cell, gamma delta T cell (cor >0.7). As shown in Figure 10, the expression of A3C had a significant positive correlation with the A3C score (Cor = 0.77). There was a positive co- expression relationship between A3C and immune checkpoints, and the correlation values with CD86, HAVCR2, PDCD1 ligand 2 (PDCD1LG2), and protein
a
b
C
100
APOBEC3C Score
Cluster A BEC
17%
High
60
p < 0.001
Low
High
High
Alive
p < 0.001
75
I
Percent weight
APOBEC3C Expression
p < 0.001
65%
40
50
83%
3
20
25
35%
Low
Low
Dead
0
0
0
high
low
A
B
C
APOBEC3C
Cluster
APOBEC3C
APOBEC3C score
Survival status
Cluster
d
e
f
Cluster BABBEC
Cluster BABBEC
Cluster 中A 中B 中C
p < 0.001
p < 0.001
p < 0.001
p < 0.001
5000.
p < 0.001
10000
p < 0.001
4000
p < 0.001
4000-
p < 0.001
p < 0.001
Immune Score
Stromal Score
Combined Score
7500
3000.
5000
2000
2000-
2500
1000-
0
0
0
A
B
C
A
B
C
A
B
C
Cluster
Cluster
Cluster
| cell | CD4 T cell | CD8 T cell | dendritic cell | killer cell | cell | cell | cell | dendritic cell | T cell | cell | dendritic cell | cell | cell | cell | cell | cell | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| natural | natural killer | |||||||||||||||||||||||||||
| T | helper | helper | ||||||||||||||||||||||||||
| A3C score | A3C | Activated B | Activated | Activated | Activated | CD56 bright | CD56 dim | Eosinophil | Gamma delta | Immature B | Immature | MDSC | Macrophage | Mast cell | Monocyte | Natural killer | Natural killer | Neutrophil | Plasmacytoid | Regulatory T | T follicular | Type 1 T helper | helper | |||||
| Type 17 T | Type 2 T | |||||||||||||||||||||||||||
| A3C score | 1 | |||||||||||||||||||||||||||
| A3C | -0.77 | |||||||||||||||||||||||||||
| Activated B cell | -0.22 | 0.31 | 0.8 | |||||||||||||||||||||||||
| Activated CD4 T cell | -0.37 | 0.37 | -0.03 | |||||||||||||||||||||||||
| Activated CD8 T cell | -0.61 | 0.56 | 0.23 | 0.27 | ||||||||||||||||||||||||
| Activated dendritic cell | -0.73 | 0.74 | 0.35 | 0.33 | 0.55 | 0.6 | ||||||||||||||||||||||
| CD56 bright natural killer cell | -0.39 | 0.14 | 0.09 | 0.11 | 0.43 | 0.08 | ||||||||||||||||||||||
| CD56 dim natural killer cell | 0.33 | -0.18 | -0.1 | -0.21 | -0.24 | -0.11 | -0.26 | 0.4 | ||||||||||||||||||||
| Eosinophil | -0.27 | 0.19 | 0.3 | 0.32 | 0.2 | 0.33 | 0.16 | -0.16 | ||||||||||||||||||||
| Gamma delta T cell | -0.74 | 0.69 | -0.02 | 0.51 | 0.55 | 0.63 | 0.18 | -0.07 | 0.09 | 0.2 | ||||||||||||||||||
| Immature B cell | -0.77 | 0.78 | 0.54 | 0.33 | 0.62 | 0.76 | 0.24 | -0.29 | 0.41 | 0.56 | ||||||||||||||||||
| Immature dendritic cell | -0.58 | 0.59 | 0.46 | 0.15 | 0.42 | 0.67 | 0.23 | -0.09 | 0.43 | 0.34 | 0.68 | |||||||||||||||||
| MDSC | -0.78 | 0.74 | 0.45 | 0.42 | 0.64 | 0.82 | 0.3 | -0.2 | 0.48 | 0.6 | 0.84 | 0.7 | 0 | |||||||||||||||
| Macrophage | -0.59 | 0.63 | 0.56 | 0.27 | 0.59 | 0.75 | 0.21 | -0.16 | 0.43 | 0.42 | 0.77 | 0.68 | 0.86 | |||||||||||||||
| Mast cell | -0.62 | 0.59 | 0.39 | 0.42 | 0.5 | 0.75 | 0.13 | -0.17 | 0.63 | 0.43 | 0.7 | 0.65 | 0.82 | 0.74 | -0.2 | |||||||||||||
| Monocyte | 0.2 | 0 | 0.18 | -0.15 | 0.08 | 0.02 | -0.18 | 0.18 | -0.1 | -0.1 | -0.02 | -0.01 | 0.02 | 0.2 | 0.04 | |||||||||||||
| Natural killer T cell | -0.88 | 0.77 | 0.27 | 0.4 | 0.5 | 0.77 | 0.22 | -0.18 | 0.29 | 0.74 | 0.76 | 0.62 | 0.77 | 0.6 | 0.67 | -0.12 | ||||||||||||
| Natural killer cell | -0.82 | 0.79 | 0.48 | 0.32 | 0.52 | 0.8 | 0.19 | -0.21 | 0.4 | 0.58 | 0.85 | 0.68 | 0.85 | 0.74 | 0.78 | 0.01 | 0.86 | -0.4 | ||||||||||
| Neutrophil | -0.26 | 0.22 | 0.17 | 0.06 | 0.31 | 0.19 | 0.25 | -0.07 | 0.24 | 0.21 | 0.25 | 0.23 | 0.39 | 0.29 | 0.28 | 0.04 | 0.2 | 0.28 | ||||||||||
| Plasmacytoid dendritic cell | -0.55 | 0.49 | 0.11 | 0.06 | 0.32 | 0.57 | 0 | -0.04 | 0.03 | 0.45 | 0.46 | 0.35 | 0.43 | 0.3 | 0.44 | -0.03 | 0.57 | 0.55 | 0.23 | -0.6 | ||||||||
| Regulatory T cell | -0.61 | 0.66 | 0.4 | 0.42 | 0.59 | 0.7 | 0.21 | -0.21 | 0.47 | 0.5 | 0.74 | 0.62 | 0.83 | 0.78 | 0.69 | 0.06 | 0.59 | 0.7 | 0.4 | 0.33 | ||||||||
| T follicular helper cell | -0.6 | 0.65 | 0.55 | 0.32 | 0.49 | 0.75 | 0.18 | -0.17 | 0.5 | 0.38 | 0.79 | 0.7 | 0.84 | 0.83 | 0.78 | 0.12 | 0.66 | 0.81 | 0.27 | 0.38 | 0.79 | |||||||
| Type 1 T helper cell | -0.66 | 0.65 | 0.38 | 0.23 | 0.39 | 0.8 | 0.04 | -0.01 | 0.43 | 0.51 | 0.69 | 0.71 | 0.73 | 0.66 | 0.74 | -0.06 | 0.77 | 0.8 | 0.21 | 0.51 | 0.57 | 0.71 | -0.8 | |||||
| Type 17 T helper cell | -0.45 | 0.49 | 0.47 | 0.06 | 0.35 | 0.6 | 0.07 | -0.06 | 0.27 | 0.3 | 0.62 | 0.55 | 0.59 | 0.6 | 0.52 | 0.12 | 0.54 | 0.63 | 0.3 | 0.47 | 0.49 | 0.6 | 0.59 | |||||
| Type 2 T helper cell | -0.11 | 0.17 | -0.1 | 0.54 | -0.06 | 0 | -0.09 | -0.23 | -0.03 | 0.29 | 0.07 | -0.11 | -0.02 | -0.13 | -0.05 | -0.15 | 0.16 | 0.09 | -0.12 | -0.05 | 0.07 | -0.02 | -0.01 | -0.09 | -1 |
Figure 9. A3C, as well as A3C scores, have close correlation with the infiltration of immune cells, such as T cells, B cells, and macrophages.
| A3C score | A3C | B2M | CD274 | CD40 | CD48 | CD86 | CD8A | CTLA4 | HAVCR2 | IL23A | LDHA | LDHB | LGALS9 | PDCD1 | PDCD1LG2 | PTPRC | TNFRSF18 | TNFRSF4 | TNFSF9 | VTCN1 | YTHDF1 | 1 | |||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| A3c score | |||||||||||||||||||||||||
| A3C | -0.77 | ||||||||||||||||||||||||
| B2M | -0.70 | 0.68 | 0.8 | ||||||||||||||||||||||
| CD274 | -0.54 | 0.62 | 0.53 | ||||||||||||||||||||||
| CD40 | -0.63 | 0.63 | 0.58 | 0.48 | 0.6 | ||||||||||||||||||||
| CD48 | -0.53 | 0.60 | 0.68 | 0.47 | 0.57 | ||||||||||||||||||||
| CD86 | -0.71 | 0.71 | 0.64 | 0.51 | 0.65 | 0.53 | 0.4 | ||||||||||||||||||
| CD8A | -0.23 | 0.36 | 0.33 | 0.48 | 0.41 | 0.51 | 0.28 | ||||||||||||||||||
| CTLA4 | -0.40 | 0.43 | 0.47 | 0.37 | 0.43 | 0.50 | 0.42 | 0.45 | 0.2 | ||||||||||||||||
| HAVCR2 | -0.74 | 0.71 | 0.64 | 0.50 | 0.68 | 0.50 | 0.95 | 0.25 | 0.35 | ||||||||||||||||
| IL23A | -0.33 | 0.26 | 0.35 | 0.19 | 0.28 | 0.32 | 0.19 | 0.15 | 0.28 | 0.22 | 0 | ||||||||||||||
| LDHA | -0.38 | 0.39 | 0.26 | 0.43 | 0.42 | 0.21 | 0.30 | 0.47 | 0.19 | 0.33 | 0.10 | ||||||||||||||
| LDHB | 0.28 | -0.34 | -0.22 | -0.42 | -0.28 | -0.21 | -0.17 | -0.45 | -0.27 | -0.23 | -0.20 | -0.38 | |||||||||||||
| LGALS9 | -0.72 | 0.69 | 0.67 | 0.45 | 0.69 | 0.57 | 0.85 | 0.22 | 0.33 | 0.91 | 0.33 | 0.26 | -0.21 | -0.2 | |||||||||||
| PDCD1 | -0.45 | 0.45 | 0.49 | 0.43 | 0.46 | 0.53 | 0.49 | 0.50 | 0.44 | 0.46 | 0.33 | 0.30 | -0.33 | 0.43 | |||||||||||
| PDCD1LG2 | -0.76 | 0.78 | 0.66 | 0.67 | 0.61 | 0.55 | 0.73 | 0.42 | 0.50 | 0.71 | 0.24 | 0.42 | -0.36 | 0.64 | 0.42 | -0.4 | |||||||||
| PTPRC | -0.68 | 0.71 | 0.63 | 0.58 | 0.64 | 0.56 | 0.90 | 0.38 | 0.49 | 0.86 | 0.15 | 0.35 | -0.30 | 0.72 | 0.44 | 0.78 | |||||||||
| TNFRSF18 | 0.00 | 0.14 | 0.12 | 0.36 | 0.23 | 0.19 | 0.02 | 0.55 | 0.39 | 0.00 | 0.19 | 0.38 | -0.36 | 0.02 | 0.27 | 0.16 | 0.06 | -0.6 | |||||||
| TNFRSF4 | -0.30 | 0.28 | 0.34 | 0.21 | 0.44 | 0.31 | 0.09 | 0.26 | 0.33 | 0.10 | 0.31 | 0.25 | -0.16 | 0.21 | 0.35 | 0.17 | 0.07 | 0.44 | |||||||
| TNFSF9 | 0.31 | -0.20 | -0.17 | -0.02 | -0.02 | -0.1 | -0.16 | 0.32 | 0.04 | -0.17 | -0.01 | 0.21 | -0.20 | -0.18 | 0.12 | -0.20 | -0.18 | 0.43 | 0.08 | -0.8 | |||||
| VTCN1 | 0.02 | -0.09 | -0.12 | -0.18 | -0.14 | -0.10 | -0.06 | -0.23 | -0.06 | -0.06 | 0.04 | -0.15 | 0.10 | -0.07 | -0.12 | -0.14 | -0.08 | -0.10 | -0.05 | -0.19 | |||||
| YTHDF1 | -0.27 | 0.23 | 0.25 | 0.08 | -0.02 | 0.13 | 0.01 | -0.12 | 0.12 | 0.05 | 0.21 | -0.02 | -0.15 | 0.07 | 0.16 | 0.12 | 0.06 | -0.04 | 0.23 | -0.30 | 0.22 | -1 |
tyrosine phosphatase receptor type C (PTPRC) were all greater than 0.7.
3.6 Chemotherapeutics sensitivity
There were 28 A3C-related genes, 20 of which were correlated with OS. The 20 A3C-related genes demon- strated a close relationship between their expression levels and corresponding drug sensitivity. The gene- drug relationship was described using correlation sig- nificance. The 16 top-ranking results are presented in Figure 11(a). The majority of the relationships could be found in S3. Moreover, the high A3C score samples were most probably sensitive to half maximal inhibi- tory concentration (IC50) of pyrimethamine, NVP. BEZ235 and rapamycin (Figure 11(b-d)).
3.7 ScRNA analysis and Immunofluorescence
After quality control and normalization 22,871 cells containing 27,568 genes were screened for analysis. Finally, the cells were projected onto two dimensions
and clustered into 15 clusters by uniform manifold approximation and projection (UMAP). The 15 clus- ters were further divided into 11 groups according to marker genes, representing Neuro, Endothelial cell, B cell, T cell, Conventional dendritic cell 2b, Cancer cell, Neural progenitor cell, Macrophage, Astrocyte, Oligodendrocyte and Unknown cluster (UMAP, Figure 12(a)). A3C mainly expressed on B cells, T cells and macrophages, and its average expression on T cells is the highest (Figure 12(b)).
4 Discussion
The range of OS of LGGs is much broader than HGG. In general, LGGs eventually progress to HGGs and prolonging the glioma at the lower-grade state may be an effective strategy. Exploring the genetic heteroge- neity of LGGs provides an important practical insight. However, the various treatments currently in use for LGGs are unsatisfactory, although some achievements have been made. In the last decade, the immunother- apy has become a novel approach to cancer treatment
a
SASH3, Nelarabine
AIF1, Chelerythrine
Drug sensitivity score
Cor=0.923, p<0.001
Drug sensitivity score
CHI3L2, Nelarabine Cor=0.885, p<0.001
SIGLEC10, Nelarabine
Drug sensitivity score
Cor=0.872, p<0.001
Drug sensitivity score
Cor=0.811, p<0.001
·
.
.
6
6
6
2.
4.
4.
4
.
2
2
2
0
·
0
.
0
·
0
2.
.
.
0
2
4
6
0
2
4
Gene Expression
Gene Expression
6
0.0
Gene Expression
0.5
1.0
1.5
2.0
0
2
4
6
8
Gene Expression
PTGS1, Epothilone B
AIF1, Nelarabine
LST1, Chelerythrine
Drug sensitivity score
Drug sensitivity score
Drug sensitivity score
Drug sensitivity score
CD300A, Fluphenazine Cor=0.783, p<0.001
Cor =- 0.800, p<0.001
Cor=0.791, p<0.001
Cor=0.788, p<0.001
0
·
4
6-
·
6
·
2.
4
2
·
4.
4
2
0
2.
6
.
0
2
.
·
0-
-
0
1
Gene Expression
2
3
0
2
4
6
8
0
2
4
0
1
Gene Expression
Gene Expression
2
Gene Expression
3
SASH3, Chelerythrine
SPI1, Imexon
Drug sensitivity score
Drug sensitivity score
HAVCR2, Fluphenazine Cor=0.779, p<0.001
Drug sensitivity score
Drug sensitivity score
NCKAP1L, Imexon Cor=0.764, p<0.001
Cor=0.779, p<0.001
Cor=0.768, p<0.001
4
·
6
4
4.
·
2
·
4
3
3.
2
2
0
:
.
2
1
1
2
+
0
:
O
.
0
J
·
·
1
A
0
2
4
6
0
1
2
3
0
2
4
0
1
2
3
Gene Expression
Gene Expression
Gene Expression
Gene Expression
HAVCR2, Isotretinoin
HCST, Chelerythrine Cor=0.757, p<0.001
CD86, Isotretinoin
Drug sensitivity score
Drug sensitivity score
Drug sensitivity score
Drug sensitivity score
CD4, Chelerythrine Cor=0.739, p<0.001
Cor=0.763, p<0.001
Cor=0.752, p<0.001
.
.
·
·
4
·
2
4
2.
2
·
0
2
0
0-
.
2
0
{
2
0
Gene Expression
1
2
3
0
Gene Expression
2
4
0.0
0.5
Gene Expression
1.0
1.5
0
1
Gene Expression
2
3
4
5
b
A3C score
Low
High
c
AC3C score
Low
High
d
A3C score
Low
High
p < 0.001
-1.5-
p < 0.001
p < 0.001
Pyrimethamine senstivity (IC50)
6-
NVP.BEZ235 senstivity (IC50)
Rapamycin senstivity (IC50)
1-
5-
-2.0-
0-
4.
-2.5-
-1-
·
3.
-3.0
·
:
i
·
-2-
2.
:
-3.5.
-3-
·
Low
High
Low
High
Low
High
[47,48]. The ultimate goal for immunotherapy is to adjust the TME to improve clinical outcomes. Potential biomarkers of response probably act as future directions for glioma immune-oncology. The current study provided a molecular target for a better therapeutic response to the combined management of glioma. Mainly based on comprehensive bioinfor- matic analyses, multiple levels of expression, survival rate, and biological functions were used to describe the effect of A3C in LGGs. Further detection revealed that A3C was highly correlated with immune response and expressed in immune cells. A3C is a reasonable pre- dictor of LGG prognosis, and it is a target biomarker that may assist in immunotherapy.
A3C has been studied in the treatment of HIV-1, which can escape from the immune system, and drugs targeting A3C may offer a novel management for
treating the virus [49]. Recently, studies reported that A3C plays a role in regulating the biological functions of cancer cells. Kanabe et al. [50] observed that the expression of A3C was lower in breast cancer samples compared with 40 paired normal samples. Upregulating A3C by lentivirus aggravated C - to - T mutational burden and fueled human pre-leukemia stem cell (pre-LSC) progress to acute myeloid leuke- mia stem cells [51]. In prostate cancer, Kawahara et al. [52] established a tissue proteome signature with 11 prostate-derived proteins, including A3C protein, for stratifying patients in clinical practice. A genome-wide association study of multiple myeloma, including 7319 multiple myeloma cases and 234,385 controls, found that A3C plays a role in defining multiple myeloma predisposition. The findings established novel views into the role of A3C in multiple myeloma biology [53].
a
b
Neuro
10-
Endothelial cell
cell
B cell
☒
Percent Expressed
5
B cell
Unknown
T cell
☒
10
Macrophage
Oligodendrocyte
☒ 20
UMAP_2
Conventional dendritic cell 2b
☒ 30
0
Conventional dendritic cell 2b
☒ 40
Neuro
Identity
Unknown
Average Expression
Neural progenitor cell
Cancer cell
2.5
-5
2.0
1.5
Astrocyte
Neural progenitor cell
1.0
0.5
0.0
-10-
Macrophage
☒
-0.5
Cancer cell
Astrocyte
-15-
Endothelial cell
Oligodendrocyte
-15-10 -5 0 5 10
UMAP_1
APOBEC3C
Features
The expression of A3C slid in gastric cancer when cytotoxin-associated gene A was overexpressed, trig- gering tumor formation [54]. In glioma, three studies reported constructing prognostic signatures using bioinformation, identifying A3C as an ingredient in the signature [18,19,55]. Indeed, A3C is a molecular biomarker brightly associated with cancer biology. However, few reports have explored the role or mechanism of A3C in affecting the outcome of LGGs.
A3C probably acts as an immune-related prognostic biomarker in LGGs TME. Little is known about the prognostic role of A3C in LGGs. The survival curves from this study revealed that a higher expression of A3C might improve the outcome of LGGs. LGG samples were stratified according to A3C-related genes from WGCNA, indicating a significantly different prognosis between these strata. Furthermore, the A3C score is an assessment tool calculated using DEGs from clusters; the tool indirectly proved the connection between A3C and disease prognosis. Moreover, chemotherapeutic sensitiv- ity results revealed that the A3C score was related to sensitivity to pyrimethamine, NVP.BEZ235 and rapamy- cin, improving the therapeutic outcome for LGGs. In addition to its prognostic role, A3C may also exert immu- nomodulatory functions. A previous study reported that A3C protein activation within immune cells aroused the anticancer immune response [16]. Wang et al. [55] estab- lished a prognostic signature in glioma using 15 immune- related genes, including A3C. Likewise, our multi-
dimensional biological information analysis results also indicated that A3C was associated with glioma immune response, confirming the prior studies. Interestingly, the scRNA analysis implied that A3C mainly expressed on immune cells, such as T cells, B cells and macrophages, which were identified as an essential component of TME of glioma. TME changes stem from insufficient intersti- tial perfusion, hypoxia, low pH, and increased interstitial fluid osmotic pressure, triggering the development of disorganized blood vessels [56-59]. The abnormal TME impairs the efficiency of immunotherapy and tumor vessel normalization. For example, antiangiogenic ther- apy may increase the potency of antitumor immune cells and improve the outcome [60-62]. The abnormal blood vessels, in turn, hinder the accumulation of T cells around the tumor tissue. The application of drugs targeting the A3C gene may reduce the aggregation of immune cells, then regulate the TME to influence the growth of glioma cells.
From the point of view of the mechanism of action, the immunotherapy has a good theoretical basis. This study provides another molecular biomarker that can be used to develop novel drugs to promote the effi- ciency of immunotherapy in LGGs. Although the sam- ple size is large enough, the bioinformatics analysis results should be further verified, and immunofluor- escence alone is insufficient. Furthermore, the A3C gene is mainly found in humans and primates. Therefore, in vivo experiments involve ethics and
high costs. In conclusion, the beneficial role of immu- notherapy in LGGs is a hotly debated topic. The dete- rioration of TME lowers the efficiency of immunotherapy. A3C was identified as a potential target to improve the efficiency of immunotherapy.
5 Conclusions
A3C is an immune-related prognostic biomarker in LGGs. Developing drugs to block A3C could enhance the efficiency of immunotherapy and improve disease survival.
Acknowledgments
We would like to acknowledge the researchers and patients who contributed to the TCGA, CGGA and the reviewers for their helpful comments to this paper.
Availability of data and materials
The data could be found from the UCSC Xena (https:// xena.ucsc.edu/), Genotype-Tissue Expression (https://com monfund.nih.gov/GTEx/), the TIMER database (https:// cistrome.shinyapps.io/timer/), the GSEA database (https://www.gsea-msigdb.org/gsea/index.jsp) and the CellMiner (https://discover.nci.nih.gov/cellminer/home. do).
Disclosure statement
No potential conflict of interest was reported by the authors.
Funding
This work was supported by Young Talent Development Plan of Changzhou Health Commission under Grant CZQM2021011; Major Scientific Project of Changzhou under Grant ZD202219. Changzhou Sci & Tech Program under Grant CJ20230072.
ORCID
Liang Shen D http://orcid.org/0000-0002-8342-4153
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