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The prognostic biomarker TPGS2 is correlated with immune infiltrates in pan-cancer: a bioinformatics analysis
Zujun Ding1, Qing Ding2, Hang Li1
1Department of General Surgery, Affiliated Hospital of Hangzhou Normal University, Hangzhou, China; 2Department of Pharmacy, Children’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
Contributions: (I) Conception and design: Z Ding; (II) Administrative support: H Li; (III) Provision of study materials: Z Ding, Q Ding; (IV) Collection and assembly of data: Z Ding; (V) Data analysis and interpretation: Z Ding, Q Ding; (VI) Manuscript writing: All authors; (VII) Final ☒ approval of manuscript: All authors.
Correspondence to: Hang Li, MD. Department of General Surgery, Affiliated Hospital of Hangzhou Normal University, 126 Wenzhou Road, Hangzhou 310015, China. Email: hanglhznu@126.com.
Background: Tubulin polyglutamylase complex subunit 2 (TPGS2) is an element of the neuronal polyglutamylase complex that plays a role in the post-translational addition of glutamate residues to C-terminal tubulin tails. Recent research has shown that TPGS2 is associated with some tumors, but the roles of TPGS2 in tumor immunity remain unclear.
Methods: The research data were mainly sourced from The Cancer Genome Atlas. The data were analyzed to identify potential correlations between TPGS2 expression and survival, gene alterations, the tumor mutational burden (TMB), microsatellite instability (MSI), immune infiltration, and various immune-related genes across various cancers. The Wilcoxon rank-sum test was used to identify the significance. A log- rank test and univariate Cox regression analysis were performed to assess the survival state of the patients. Spearman’s correlation coefficients were used to show the correlations.
Results: TPGS2 exhibited abnormal expression patterns in most types of cancers, and has promising prognostic potential in adrenocortical carcinoma and liver hepatocellular carcinoma. Further, TPGS2 expression was significantly correlated with molecular and immune subtypes. Moreover, the single-cell analyses showed that the expression of TPGS2 was associated with the cell cycle, metastasis, invasion, inflammation, and DNA damage. In addition, the immune cell infiltration analysis and gene-set enrichment analysis demonstrated that a variety of immune cells and immune processes were associated with TPGS2 expression in various cancers. Further, immune regulators, including immunoinhibitors, immunostimulators, the major histocompatibility complex, chemokines, and chemokine receptors, were correlated with TPGS2 expression in different cancer types. Finally, the TMB and MSI, which have been identified as powerful predictors of immunotherapy, were shown to be correlated with the expression of TPGS2 across human cancers.
Conclusions: TPGS2 is aberrantly expressed in most cancer tissues and might be associated with immune cell infiltration in the tumor microenvironment. TPGS2 could serve not only as a biomarker for predicting clinical outcomes, but also as a promising biomarker for evaluating and developing new approaches to immunotherapy in many types of cancers, especially colon adenocarcinoma and stomach adenocarcinoma.
Keywords: Tubulin polyglutamylase complex subunit 2 (TPGS2); pan-cancer; tumor microenvironment (TME); biomarker; immunotherapy
Submitted Jan 29, 2023. Accepted for publication Oct 20, 2023. Published online Mar 21, 2024. doi: 10.21037/tcr-23-113
View this article at: https://dx.doi.org/10.21037/tcr-23-113
Introduction
At present, cancer is a leading cause of disease morbidity and mortality worldwide. Unfortunately, the number of newly diagnosed cases continues to grow (1,2). Current mainstream treatment modalities, including surgery, chemotherapy, radiotherapy, targeted therapy, and immunotherapy, still do not provide a satisfactory prognosis for cancer patients (3). This pan-cancer study sought to apply diagnostic and therapeutic applications in gastric cancer to a broad range of tumors with characteristics of similarity. The identification of common key genes between different types of cancers can help in cancer diagnosis and treatment (4,5).
The application of immunotherapy has opened up a new era in tumor treatment, and it has achieved encouraging results in the treatment of several tumors (including lung cancer, melanoma, and liver cancer), greatly improving the prognosis of patients (6,7). Many immune checkpoint inhibitors (ICIs), such as programmed cell death protein 1 (PD-1), programmed cell death ligand 1 (PD-L1), and cytotoxic T lymphocyte-associated antigen 4 (CTLA-4) inhibitors, have been established as routine treatments for many types malignancies; however, their clinical efficacy is limited (8,9). Given the complexities in the efficacy of immunotherapy, it is of great significance to explore new and more effective immune biomarkers.
The tumor microenvironment (TME) refers to the pericellular environment including immune cells, blood vessels, extracellular matrices, fibroblasts, mast cells, and various signaling molecules around the tumor (10,11). In recent years, the important role of the TME in tumorigenesis and development has been recognized.
Highlight box
Key findings
· Tubulin polyglutamylase complex subunit 2 (TPGS2) is a potential prognostic and immunotherapeutic biomarker in many types of cancers, especially colon adenocarcinoma and stomach adenocarcinoma.
What is known and what is new?
· TPGS2 has been found to be associated with some tumors.
· TPGS2 plays a crucial role in tumor immunity.
What is the implication, and what should change now?
· TPGS2 is a promising tumor immune target, and more research on TPGS2 and tumor immunity should be conducted.
Tumor proliferation, infiltration, and metastasis depend not only on the tumor cells but also on the regulation of various cellular and signaling molecules in the TME (12). T cells, which are crucial in the anti-tumor immune response, have been relatively well studied among adaptive immune cells (13,14). Conversely, the second adaptive immune cell population (i.e., B cells) in the TME has not been well characterized.
However, in the last 5 years, several studies have reported an association between the presence of B cells in the TME and improved clinical outcomes (15-20). B cells can resist tumors by producing tumor-specific antibodies under certain conditions, but specific B cell subsets and antibody specificity can also suppress anti-tumor immunity and promote tumor growth (21). Meanwhile, infiltrating B cells are an important component of tertiary lymphoid structures (TLSs) in tumor tissues (15). Many studies have shown that the number and proportion of stromal cells and immune cells in tumor tissues are closely related to clinical features and prognosis (22-28). A thorough understanding of the TME is essential for accurate evaluation and treatment. To improve prognosis, more targeted molecules need to be identified for cancer diagnosis and treatment and to assess patient prognosis.
Few relevant studies have been conducted on tubulin polyglutamylase complex subunit 2 (TPGS2). We obtained partial information on TPGS2 from the Entrez Molecular Sequence Database Entrez (https://www.ncbi.nlm.nih.gov/ search/), which showed that TPGS2 encodes a protein that is an element of the neuronal polyglutamylase complex, which plays a role in the post-translational addition of glutamate residues to C-terminal tubulin tails, and alternatively spliced transcript variants encoding multiple isoforms have been observed for this gene. TPGS2 also appears to be associated with tumors and the TME (29,30).
In our preliminary analysis, we confirmed that TPGS2 has a special role in cancer immunity. Based on this finding, we then performed the pan-cancer analysis to explore the expression, prognostic function, and immune role of TPGS2 in various cancers. We comprehensively analyzed the relationship between TPGS2 expression and patient prognosis in 33 types of cancer. Additionally, we further evaluated the association between TPGS2 and tumor- infiltrating immune cells. Our findings revealed that TPGS2 has a potential role in the development and progression of cancers; thus, TPGS2 may serve as a potential prognostic and immunotherapeutic biomarker. We present this article in accordance with the REMARK reporting checklist (available at https://tcr.amegroups.com/article/
view/10.21037/tcr-23-113/rc).
Methods
The research data were downloaded from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), Cancer Cell Line Encyclopedia (CCLE), and Human Protein Atlas (HPA). databases. Data were downloaded for the following tumors: adrenocortical carcinoma (ACC), bladder urothelial carcinoma (BLCA), breast carcinoma (BRCA), cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), cholangiocarcinoma (CHOL), colon adenocarcinoma (COAD), lymphoid neoplasm diffuse large B-cell lymphoma (DLBC), esophageal carcinoma (ESCA), glioblastoma multiforme (GBM), head and neck squamous cell carcinoma (HNSC), kidney chromophobe (KICH), kidney renal clear cell carcinoma (KIRC), kidney renal papillary cell carcinoma (KIRP), acute myeloid leukemia (LAML), brain low-grade glioma (LGG), liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), mesothelioma (MESO), ovarian serous cystadenocarcinoma (OV), pancreatic adenocarcinoma (PAAD), pheochromocytoma and paraganglioma (PCPG), prostate adenocarcinoma (PRAD), rectum adenocarcinoma (READ), sarcoma (SARC), skin cutaneous melanoma (SKCM), stomach adenocarcinoma (STAD), testicular germ cell tumor (TGCT), thyroid carcinoma (THCA), thymoma (THYM), uterine corpus endometrial carcinoma (UCEC), uterine carcinosarcoma (UCS), and uveal melanoma (UM or UVM). The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013).
Data source and processing
The gene expression and clinical data of normal human tissues and cancer tissues were downloaded from the GTEx database and TCGA by UCSC Xena (https://xenabrowser.net/) (31). For a multidimensional demonstration, the expression of TPGS2 was analyzed in various cancer cell lines with data from the CCLE. The transcripts per million (TPM) format and the log2(TPM+1) format were used for the expression profiles and subsequent analyses. Statistical significance was defined as follows: P<0.05, P<0.01, and P<0.001.
IHC of TPGS2
The protein expression of the tumor tissues and normal
tissues from the HPA (http://www.proteinatlas.org/) database was applied to verify the protein expression levels of TPGS2 (32). Data from the HPA database were also used to confirm the intensity of TPGS2 in immunohistochemical staining in six normal and cancer tissues, including LIHC, LUSC, COAD, STAD, PRAD, and TGCT.
Genomic alterations analysis of TPGS2
The cBioPortal (http://www.cbioportal.org) is a multipurpose cancer genomics database that can recognize the molecular information of cancer tissues and comprehend the associated genetics, epigenetics, gene expression, and proteome information (33,34). The cBioPortal was used to display the alteration frequency (including mutation, structural variation, amplification, deep deletion, and multiple alterations) across cancers, and the results were visualized in bar plots. We also obtained a landscape map of the gene mutation sites, a correlation diagram of the copy number alterations (CNAs) and TPGS2 expression, and Kaplan-Meier curves of the pan-cancer data using the cBioPortal webtool.
Prognostic analysis
The UCSC Xena database was used to download the related prognostic data, including overall survival (OS), progression-free survival (PFS), and disease-specific survival (DSS) data. Next, we plotted the Kaplan-Meier model and univariate Cox regression results to assess the prognosis of various cancers. The TPGS2 expression median of each cancer was used to divide patients into high- and low- expression subgroups. Next, the Kaplan-Meier method was used to compute the log-rank P value and hazard ratio (HR) with a 95% confidence interval (CI). The “survival” package (3.2-10) was used for the statistical analysis of the survival data, and the “survminer” package (0.4.9) was used for the visualization.
Single-cell analysis of TPGS2
The Cancer Single-cell State Atlas (CancerSEA), a specialized single-cell sequencing database, provides various functional data on cancer cells at the single-cell level (35). The average correlations between the TPGS2 expression and functional states in different cancers were summarized and presented in a heatmap. The correlations between TPGS2 expression and several tumor functions
were investigated using single-cell sequencing data. The TPGS2 expression profiles of single cells are shown in the t-distributed stochastic neighbor embedding (t-SNE) diagrams.
Gene set enrichment analysis (GSEA)
A GSEA was conducted using the “clusterProfiler” package (3.14.3), and “ggplot2” (3.3.3) was used to graph the results (36,37). The reference gene set was c5.bp.v7.2.symbols.gmt (Gene Ontology), which was derived from the website of the Molecular Signatures Database (MSigDB, https://www. gsea-msigdb.org/gsea/index.jsp). It is generally accepted that the threshold of significant enrichment is a false discovery rate (FDR) <0.25 and a P adjusted value <0.05.
Immune cell infiltration analysis and TME
The immune cell infiltration analysis was largely performed using the Tumor Immune Estimation Resource (TIMER) (38). The TPGS2-associated immune cell infiltration correlations were downloaded from the TIMER 2.0 database (http://timer.cistrome.org/). Finally, we visualized the statistical Spearman correlations between TPGS2 messenger RNA (mRNA) expression and 20 immune cell subsets.
Correlation analysis of the TMB, MSI, and immune regulators
A Spearman correlation analysis of immune regulators and TPGS2 expression was performed to investigate the correlation between TPGS2 and the reported biomarkers of cancer immunotherapy, including immunostimulators, immunoinhibitors, the major histocompatibility complex (MHC) genes, chemokines, and chemokine receptors, for various cancer types. A Spearman correlation analysis was also conducted to analyze the relationship between the tumor mutational burden (TMB) (39), microsatellite instability (MSI), and TPGS2 expression (40) across various cancers.
Statistical analysis
The Wilcoxon rank-sum test was used to evaluate the statistical significance of and compare the TPGS2 expression levels between tumor and normal tissues. The survival analysis was performed using the Kaplan-Meier method
(log-rank test) and a univariate Cox regression analysis was also conducted. A Spearman correlation analysis was conducted to evaluate the correlations between TPGS2 and other factors, such as immune cell infiltration, the TMB, and MSI. A P value <0.05 was considered statistically significant.
Results
Expression of TPGS2 in cancer tissues
First, we integrated the mRNA expression levels of normal tissues in the GTEx database. The results showed that TPGS2 was highly expressed in the normal tissues of TGCT, BLCA, and OV, and most lowly expressed in LIHC (Figure 1A). According to the tumor cell data from the CCLE, compared to the other tumor cells, TPGS2 was the most highly expressed in small cell lung cancer and CESC, and the most lowly expressed in chronic lymphocytic leukemia (Figure 1B). We then combined the data from TCGA and GTEx databases to reflect the expression levels of TPGS2 mRNA in various malignancies (Figure 1C). The results showed that TPGS2 mRNA was more highly expressed in 22 kinds of tumors (BRCA, CESC, CHOL, COAD, DLBC, ESCA, GBM, HNSC, LGG, LIHC, LUAD, LUSC, OV, PAAD, PCPG, READ, SKCM, STAD, THCA, THYM, UCEC, and UCS) than their respective normal tissues. Conversely, TPGS2 was more lowly expressed in five tumors (ACC, KIRC, LAML, PRAD, and TGCT) than their normal tissues. Further, TPGS2 mRNA was significantly more highly expressed in BLCA, CHOL, COAD, ESCA, HNSC, LIHC, LUSC, STAD, and UCEC cancer tissues than matched normal tissues, and more highly expressed in KICH and PRAD tumor tissues (Figure 1D).
Moreover, we evaluated the protein expression of TPGS2 between normal and tumor tissues using the HPA database. As Figure 2 shows, compared to the weak staining of TPGS2 in the normal liver, lung, colon, and stomach tissues, stronger staining was observed in the LIHC, LUSC, COAD, and STAD tissues (Figure 2A-2D). Normal prostate and testes tissues had medium TPGS2 staining, while their tumor tissues had weaker staining (Figure 2E,2F). Thus, the immunohistochemistry (IHC) results re-confirmed our previous analyses. These results indicated that TPGS2 is aberrantly expressed across human cancers, and we speculated that TPGS2 may be able to inform the prognosis and treatment of various cancers.
A
TGCT
LIHC
PAAD
B
PAAD
STAD
CESC
SCLC
CLL
BLCA
8
7.5
ESCA
LGG
HNSC
OV
7
DLBC
ALL
7
COAD-READ
SKCM
6
THYM
NB
6
5
STAD
5
ACC
KIRC
GBM
6
LAML
4
5.5
LGG
LCML
3
KICH
SARC
5
MESO
Ewings
GBM
KIRP
DLBC
MM
LUSC
COAD
BLCA
LUAD
LUAD
READ
LUSC
BRCA
THCA
PRAD
KIRC
OV
CESC
BRCA
PRAD
LIHC
ESCA
UVM
UCEC
THCA
UCEC
MB
SKCM
NSC
Mean expression
Mean expression
C
10
ns
ns
**
ns
The expression of TPGS2 Log2 (TPM+1)
8
6
Normal
Tumor
4
2
0
ACC
BLCA
BRCA
CESC
CHOL
COAD
DLBC
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
D
7
ns
**
ns
The expression of TPGS2 Log2 (TPM+1)
6
**
**
ns
ns
ns
5
**
*
ns
*
ns
Normal
4
Tumor
3
2
1
BLCA
BRCA
CHOL
COAD
ESCA
HNSC
KICH
KIRC
KIRP
LIHC
LUAD
LUSC
PAAD
PRAD
READ
STAD
THCA
UCEC
Association with molecular and immune subtypes
To explore the associations between TPGS2 expression and molecular and immune subtypes across human cancers, we performed a further analysis by the tumor-immune system interaction database (TISDB). As Figure 3A-3F
show, TPGS2 expression was significantly associated with the molecular stages of many cancers, such as BRCA, COAD, HNSC, LGG, LUSC, and PCPG. To explore the relationship between TPGS2 and cancer immunity, we analyzed the correlation between the immune subtypes and TPGS2 expression, and found that the expression of TPGS2
A
B
80×
80×
80×
80×
80×
80×
Liver normal
LIHC
LIHC
Lung normal
LUSC
LUSC
C
D
80×
80×
80×
80×
80×
80×
Colon normal
COAD
COAD
Stomach normal
STAD
STAD
E
F
80×
80×
80×
80×
80×
80×
Prostate normal
PRAD
PRAD
Testis normal
TGCT
TGCT
was significantly related to immune subtypes in many cancers, including BRCA, KIRC, LIHC, STAD, OV, and SARC (Figure 3G-3L). These results indicated that TPGS2 has potential prediction and treatment functions in pan- cancer.
Genetic alteration of TPGS2
Given the abnormal expression of TPGS2 observed in cancer, we sought to examine whether genetic alterations in TPGS2 caused this change. Therefore, we conducted a genetic alteration analysis of TPGS2 using the cBioPortal database with data from TCGA, and PanCancer Atlas. As Figure 4A shows, PAAD had the highest alteration rate (6%)
with “amplification” and “deep deletion” as the primary types. Conversely, the “deep deletion” type of the CNAs was the primary altered type in the ESAD cases, which had an alteration frequency of ~4%. Notably, the main genetic alteration in UCEC and SKCM was “mutation” (Figure 4A). As Figure 4B shows, after examining putative CNAs from the significant targets in cancer (GISTIC) module, the CNAs were closely related to the mRNA expression of TPGS2.
Clinical prognostic significance of TPGS2
To examine the prognostic role of TPGS2 across human cancers, prognostic indicators in 33 cancers were
A
BRCA:TPGS2_exp
Pv=4.97e-48
B
C
n=Basal 172,
COAD:TPGS2_exp
PV=8.72e-20
HNSC :: TPGS2_exp
Her2 73,
Pv=2.65e-06
LumA 508,
n=CIN 226, GS 49,
HM-SNV 6, HM-indel 60
n=Atypical 67,
LumB 191,
Basal 87,
Normal 137
Classical 48,
Mesenchymal 74
Expression (log2CPM)
9
Expression (log2CPM)
.
Expression (log2CPM)
9
8
6
i
B
8
8
7
I
B
7
i
4
H
!
6
4
6
i
5
5
4
2
4
Basal
Her2
LumA
LumB
Normal
CIN
GS
HM-SNV
HM-indel
Atypical
Basal
Classical
Mesenchymal
Subtype
Subtype
Subtype
D
LGG :: TPGS2_exp
E
F
PCPG:TPGS2_exp
Pv=1.07e-07
n=Classic-like 23, Codel 171,
LUSC :: TPGS2_exp Pv=1.41e-07 n=Basal 42, Classical 63, Primitive 26, Secretory 39
Pv=7.35e-10
n=Corticaladmixture 22,
G-CIMP-high 234, G-CIMP-low 12,
Kinasesignaling 68,
Expression (log2CPM)
Mesenchymal-like 45, PA-like 26
Pseudohypoxia 61, Wnt-altered 22
8
Expression (log2CPM)
9
Expression (log2CPM)
8
7
8
7
6
!
!
H
8
H
N
7
N
1
6
:
:
5
6
·
5
4
Classic-like
Codel
G-CIMP-high
G-CIMP-low
Mesenchymal-like
PA-like
5
Basal
Classical
Primitive
Secretory
Corticaladmixture
Kinasesignaling
Pseudohypoxia
Wnt-altered
Subtype
Subtype
Subtype
G
BRCA :: TPGS2_exp Pv=1.28e-19
H
KIRC:TPGS2_exp Pv=1.23e-04
I
n=C1 369, C2 390, C3 191, C4 92, C6 40
n=C1 7, C2 20, C3 445, C4 27, C5 3, C6 13
LIHC :: TPGS2_exp Pv=4.11e-08
8
n=C1 22, C2 45, C3 135, C4 159, C6 1
Expression (log2CPM)
9
Expression (log2CPM)
Expression (log2CPM)
8
8
6
7
!
!
6
H
H
6
H
6
8
9
H
4
5
4
4
2
C1
C2
C3
C4
C6
C1
C2
C3
C4
C5
C6
C1
C2
C3
C4
C6
Subtype
Subtype
Subtype
J
STAD:TPGS2_exp Pv=1.18e-04 n=C1 129, C2 210, C3 36, C4 9, C6 7
K
OV:TPGS2_exp Pv=3.38e-03
L
SARC:TPGS2_exp Pv=1.82e-04 n=C1 64, C2 38, C3 42, C4 59, C6 20
n=C1 46, C2 159, C3 3,C4 61
Expression (log2CPM)
Expression (log2CPM)
10
Expression (log2CPM)
8
8
7
.
8
H
6
i
H
6
8
,
6
O
5
4
4
3
C1
C2
C3
C4
C6
C1
C2
C3
C4
C1
C2
C3
C4
C6
Subtype
Subtype
Subtype
A
6
Mutation
Structural variant
Amplification
Deep deletion
Multiple alterations
5
Alteration frequency, %
4
3
2
1
Structural variant data +
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Mutation data
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
CNA data
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Pancreatic Adenocarcinoma (TCGA, PanCancer Atlas)
Esophageal Adenocarcinoma (TCGA, PanCancer Atlas)
Stomach Adenocarcinoma (TCGA, PanCancer Atlas)
Ovarian Serous Cystadenocarcinoma (TCGA, PanCancer Atlas)
Lung Squamous Cell Carcinoma (TCGA, PanCancer Atlas)
Uterine Corpus Endometrial Carcinoma (TCGA, PanCancer Atlas)
Sarcoma (TCGA, PanCancer Atlas)
Bladder Urothelial Carcinoma (TCGA, PanCancer Atlas)
Diffuse Large B-Cell Lymphoma (TCGA, PanCancer Atlas)
Lung Adenocarcinoma (TCGA, PanCancer Atlas)
Head and Neck Squamous Cell Carcinoma (TCGA, PanCancer Atlas)
Uterine Carcinosarcoma (TCGA, PanCancer Atlas)
Skin Cutaneous Melanoma (TCGA, PanCancer Atlas)
Colorectal Adenocarcinoma (TCGA, PanCancer Atlas) Liver Hepatocellular Carcinoma (TCGA, PanCancer Atlas)
Brain Lower Grade Glioma (TCGA, PanCancer Atlas)
Cervical Squamous Cell Carcinoma (TCGA, PanCancer Atlas)
Testicular Germ Cell Tumors (TCGA, PanCancer Atlas)
Breast Invasive Carcinoma (TCGA, PanCancer Atlas)
Kidney Renal Clear Cell Carcinoma (TCGA, PanCancer Atlas)
Prostate Adenocarcinoma (TCGA, PanCancer Atlas) Acute Myeloid Leukemia (TCGA, PanCancer Atlas) Glioblastoma Multiforme (TCGA, PanCancer Atlas) +
Thymoma (TCGA, PanCancer Atlas)
Thyroid Carcinoma (TCGA, PanCancer Atlas)
Kidney Chromophobe (TCGA, PanCancer Atlas)
Kidney Renal Papillary Cell Carcinoma (TCGA, PanCancer Atlas)
Uveal Melanoma (TCGA, PanCancer Atlas)
Cholangiocarcinoma (TCGA, PanCancer Atlas)
Adrenocortical Carcinoma (TCGA, PanCancer Atlas)
Pheochromocytoma and Paraganglioma (TCGA, PanCancer Atlas)
Mesothelioma (TCGA, PanCancer Atlas)
B
TPGS2: mRNA Expression, RSEM (batch normalized from
14
TPGS2
13
Splice (VUS)
Not mutated
· Gain
illumina HiSeq_RNASeqV2) [log2(value + 1)]
12
Deep deletion
11
· Truncating (VUS)
Not profiled for mutations
10
Diploid
9
· Structural variant1
Missense (VUS)
8
o Amplification
· Shallow deletion
7
6
5
Deep deletion
Shallow deletion
Diploid
Gain
Amplification
TPGS2: Putative copy-number alterations from GISTIC
investigated using the Kaplan-Meier method and a univariate Cox regression analysis. The results showed that TPGS2 was highly correlated with the prognosis of most cancers. As Figure 5A shows, the univariate Cox regression analysis of OS suggested that the high expression of TPGS2
was a risk factor for the poor prognosis of ACC, BLCA, LGG, LIHC, MESO, SARC, STAD, and UCS, while it was a protective factor for OV and THYM. The results of the Kaplan-Meier OS analysis, which were basically consistent with the results of the univariate Cox regression analysis,
| A Cancer | P value | Hazard Ratio(95% CI) | |
|---|---|---|---|
| ACC | 0.0035 | 3.42787(1.49986,7.83422) | |
| BLCA | 0.0140 | 1.45156(1.07835,1.95395) | |
| BRCA | 0.9666 | 0.99324(0.72279,1.36488) | |
| CESC | 0.2191 | 0.74506(0.46596,1.19133) | |
| CHOL | 0.7518 | 1.16929(0.44362,3.08199) | |
| COAD | 0.2740 | 0.80327(0.54249,1.18941) | |
| DLBC | 0.5730 | 0.66075(0.15642,2.79112) | |
| ESCA | 0.6674 | 1.11311(0.68281,1.81459) | |
| GBM | 0.2830 | 1.21933(0.84901,1.75116) | |
| HNSC | 0.0709 | 1.28282(0.979,1.68091) | |
| KICH | 0.2376 | 2.30721(0.57615,9.23927) | |
| KIRC | 0.0813 | 0.76544(0.56675,1.03378) | |
| KIRP | 0.3552 | 0.75443(0.41513,1.37106) | |
| LAML | 0.3277 | 1.23428(0.80976,1.88135) | |
| LGG | 0.0007 | 1.89069(1.31078,2.72716) | |
| LIHC | 0.0061 | 1.62865(1.14901,2.30851) | |
| LUAD | 0.9128 | 0.98392(0.73614,1.31511) | |
| LUSC | 0.2637 | 0.85718(0.65421,1.12313) | |
| MESO | 0.0014 | 2.17943(1.35073,3.51654) | |
| OV | 0.0259 | 0.74365(0.57306,0.96503) | |
| PAAD | 0.0746 | 1.45428(0.96352,2.195) | |
| PCPG | 0.4056 | 0.48661(0.08912,2.65709) | |
| PRAD | 0.1549 | 2.69349(0.68768,10.54971) | |
| READ | 0.1610 | 0.54709(0.23541,1.27144) | |
| SARC | 0.0479 | 1.4957(1.00368,2.22892) | |
| SKCM | 0.1926 | 1.19513(0.91402,1.56271) | |
| STAD | 0.0070 | 1.57874(1.13307,2.19969) | |
| THCA | 0.8724 | 0.92268(0.34566,2.46297) | |
| THYM | 0.0337 | 0.10475(0.01305,0.84059) | |
| UCEC | 0.4053 | 1.19142(0.78868,1.79984) | |
| UCS | 0.0391 | 2.05947(1.03663,4.09156) | |
| UVM | 0.6013 | 1.24823(0.54337,2.86746) |
B
C
D
ACC
BLCA
LGG
1.0
TPGS2
1.0
TPGS2
1.0
TPGS2
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
Overall survival
0.2
HR =3.39 (95% CI, 1.48-7.75)
HR =1.39 (95% CI, 1.03-1.86)
Overall survival
HR =1.87 (95% CI, 1.31-2.65)
0.0
P=0.004
0.0
P=0.029
0.0
P=0.001
0
50
100
150
0
40
80
120
160
0
50
100
150
200
Time, months
Time, months
Time, months
E
LIHC
F
MESO
G
OV
1.0
TPGS2
1.0
TPGS2
1.0
TPGS2
Low
Low
High
++ Low
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
Overall survival
0.2
HR =1.72 (95% CI, 1.21-2.44)
HR =2.28 (95% Gl,1.41-3.69)
Overall survival
HR =0.74 (95% CI, 0.57-0.96)
0.0
P=0.002
0.0
P=0.001
0.0
P=0.024
0
30
60
90
120
0
25
50
75
0
50
100
150
Time, months
Time, months
Time, months
H
SARC
I
STAD
J
THYM
1.0
TPGS2
1.0
TPGS2
1.0
TPGS2
+ Low
Low
Low
High
High
Survival probability
0.8
High
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
0.2
HR =1.61 (95% CI, 1.08-2.40)
Overall survival
HR =1.49 (95% CI, 1.07-2.08)
Overall survival
HR =0.10 (95% CI, 0.01-0.83)
0.0
P=0.02
0.0
P=0.018
0.0
P=0.032
0
50
100
150
200
0
25
50
75
100
125
0
50
100
150
Time, months
Time, months
Time, months
0.0130501 2 3 4 5 6 7 8 9 10 11 Hazard Ratio
showed that the high expression of TPGS2 was significantly correlated with a poor prognosis in SARC, MESO, LIHC, LGG, BLCA, and ACC, but was significantly correlated with a better prognosis in OV and THYM (Figure 5B-5}). The results of the Cox regression analysis of PFS revealed that TPGS2 was a risk factor in ACC, HNSC, MESO, and UCS (Figure 6A). The results of the Kaplan-Meier analysis showed that patients with high TPGS2 expression had poorer PFS than those with low TPGS2 expression in ACC, CHOL, HNSC, LIHC, SARC, and UCS, but had better PFS in PCPG, LUCA (Figure 6B-6I). Additionally, the results of the Cox regression analysis of DSS revealed that TPGS2 acts as a risk factor for ACC, LGG, LIHC,
MESO, PAAD, and SARC, but acts as a protective factor for OV (Figure 7A). Further, the results of the Kaplan- Meier analysis of DSS showed that a high expression of TPGS2 was associated with a worse prognosis in ACC, LGG, LIHC, MESO, PAAD, and SARC (Figure 7B-7H). Overall, TPGS2 expression was significantly associated with prognostic parameters in many cancers.
TPGS2 expression patterns in single-cell analyses and related-functional status
Due to the complexity of tumor cells, the new technique of single-cell transcriptome sequencing is increasingly
| A Cancer | P value | Hazard Ratio(95% CI) | ||
|---|---|---|---|---|
| ACC | <0.0001 | 4.51965(2.24091,9.1156) | ||
| BLCA | 0.2317 | 1.19994(0.89008,1.61766) | ||
| BRCA | 0.6016 | 0.91703(0.66246,1.26943) | ||
| CESC | 0.9866 | 0.99605(0.62716,1.5819) | ||
| CHOL | 0.1074 | 2.13312(0.84823,5.36433) | ||
| COAD | 0.292 | 0.82516(0.57717,1.17971) | ||
| DLBC | 0.4405 | 0.61379(0.17756,2.12175) | ||
| ESCA | 0.2173 | 1.3202(0.84913,2.05261) | ||
| GBM | 0.4613 | 0.8734(0.60925,1.25207) | ||
| HNSC | 0.0365 | 1.35475(1.01931,1.80056) | ||
| KICH | 0.9661 | 0.97456(0.29698,3.19808) | ||
| KIRC | 0.078 | 0.75293(0.54917,1.0323) | ||
| KIRP | 0.9169 | 1.02808(0.61116,1.72941) | ||
| LGG | 0.0961 | 1.27406(0.95783,1.6947) | ||
| LIHC | 0.08 | 1.29899(0.96919,1.74102) | ||
| LUAD | 0.1705 | 0.82579(0.62803,1.08582) | ||
| LUSC | 0.4128 | 0.87311(0.63103,1.20806) | ||
| MESO | 0.0402 | 1.74874(1.02537,2.9824) | ||
| OV | 0.2645 | 0.87307(0.68787,1.10813) | ||
| PAAD | 0.1263 | 1.35446(0.91805,1.99833) | ||
| PCPG | 0.0569 | 0.39618(0.15277,1.02745) | ||
| PRAD | 0.7751 | 0.94267(0.62871,1.41341) | ||
| READ | 0.6728 | 0.86948(0.45436,1.66389) | ||
| SARC | 0.061 | 1.37683(0.98531,1.92392) | ||
| SKCM | 0.7504 | 1.03702(0.82893,1.29735) | ||
| STAD | 0.2698 | 1.21982(0.8571,1.73604) | ||
| TGCT | 0.8835 | 0.95135(0.48831,1.85349) | ||
| THCA | 0.4097 | 0.79838(0.46747,1.36354) | ||
| THYM | 0.1887 | 0.55272(0.22833,1.33795) | ||
| UCEC | 0.2115 | 1.25373(0.87934,1.78753) | ||
| UCS | 0.0112 | 2.36019(1.21515,4.5842) | ||
| UVM | 0.9902 | 0.99542(0.4769,2.0777) |
B
ACC
C
CHOL
D
HNSC
1.0
TPGS2
1.0-
TPGS2
1.0
TPGS2
++ 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
Progress free interval
0.2
Progress free interval
0.2
HR =4.36 (95% CI, 2.16-8.81)
HR =2.63 (95% CI, 1.01-6.86)
Progress free interval
HR=1.38 (95% CI, 1.04-1.83)
0.0
P<0.001
0.0
P=0.048
0.0
P=0.027
0
50
100
150
0
20
40
60
0
50
100
150
200
Time, months
Time, months
Time, months
E
LIHC
F
PCPG
G
SARC
1.0
TPGS2
1.0
TPGS2
1.0
TPGS2
Low
++Low
++High
++ Low
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
Progress free interval
0.2
Progress free interval
0.2
HR =1.35 (95% CI, 1.01-1.81)
HR =0.34 (95% CI, 0.13-0.88)
Progress free interval
HR =1.49 (95% CI, 1.07-2.08)
0.0
P=0.041
0.0
P=0.027
0.0
P=0.018
0
30
60
90
120
0
50
100
150
200
0
40
80
120
160
Time, months
Time, months
Time, months
H
UCS
I
LUCA
1.0
TPGS2
1.0
TPGS2
+ Low
++ Low
0.8
High
0.8
High
Survival probability
Survival probability
0.6
0.6
0.4
0.4
0.2
Progress free interval HR =2.37 (95% CI, 1.2
0.2
Progress free interval
0.0
-4.67)
0.0
HR =0.74 (95% CI, 0.60-0.91)
P=0.013
P=0.004
0
50
100
0
50
100
150
200
250
0.15277 10
2
3
4
5
6
7
8
9
Hazard Ratio
Time, months
Time, months
being used to analyze a variety of cancer cells, immune cells, endothelial cells, and stromal cells. To explore the expression of TPGS2 in single-cell analyses in pan- cancer and its relationship with tumor functional status, we obtained tumor single-cell data on TPGS2 from the CancerSEA.
As Figure 8A shows, we found that many types of cancers, including UM, non-small cell lung cancer (NSCLC), and high-grade glioma (HGG), were associated with most tumor functional states. We also analyzed expression distribution with t-SNE plots, and the correlation between TPGS2 expression and functional states in different cancers based on the CancerSEA database, and found that TPGS2 expression in UM was significantly associated with DNA
repair, DNA damage, apoptosis, metastasis, invasion, and quiescence (Figure 8B); acute myelocytic leukemia (AML) was closely associated with invasion and differentiation (Figure 8C); retinoblastoma (RB) was correlated with differentiation and angiogenesis (Figure 8D); chronic myeloid leukemia (CML) was closely associated with proliferation (Figure 8E); and NSCLC was associated with epithelial-to-mesenchymal transition (Figure 8F). The t-SNE plots showed the expression distribution of TPGS2 in UM, AML, RB, CML and NSCLC cells (Figure 8G- 8K). In the plots, every point represents a single cell, and the color of the point represents the expression level of TPGS2 in the cell. The results suggest TPGS2 might play an important role in some functional states, and most of the
A
Cancer
P value
Hazard Ratio(95% CI)
B
ACC
C
LGG
D
LIHC
ACC
0.0041
3.587(1.5003,8.57604)
A
1.0
TPGS2
1.0
TPGS2
1.0.
TPGS2
BLCA
0.0754
1.3863(0.96716,1.98709)
Low
Low
+Low
High
++ High
High
BRCA
0.3660
0.82097(0.53531,1.25909)
Survival probability
0.8
Survival probability
0.8
Survival probability
0.8
CESC
0.7041
0.9024(0.53115,1.53313)
0.6
0.6
0.6
CHOL
0.4045
1.58079(0.53864,4.63922)
0.4
0.4
0.4
COAD
0.7096
0.91079(0.557,1.4893)
DLBC
0.3926
0.37042(0.03801,3.61026)
0.2
Disease specific survival
0.2
Disease specific survival
0.2
Disease specific survival HR =2.00 (95% CI, 1.27-3.16)
ESCA
0.3523
1.3146(0.73874,2.33932)
HR =3.54 (95% CI, 1.48-8.47)
HR =1.76 (95% CI, 1.22-2.54)
0.0
P=0.005
0.0
P=0.002
0.0
P=0.003
GBM
0.2001
1.28923(0.87413,1.90146)
0
50
100
150
0
50
100
150
200
0
30
60
90
120
HNSC
0.1117
1.32755(0.93634,1.88222)
Time, months
Time, months
Time, months
KICH
0.5475
1.58392(0.35396,7.08775)
E
MESO
F
OV
G
PAAD
KIRC
0.0975
0.72356(0.49349,1.06091)
1.0
TPGS2
1.0
TPGS2
KIRP
0.2897
0.66367(0.31071,1.4176)
Low
+Low
1.0
TPGS2
High
+ Low
LGG
0.0019
1.83527(1.25147,2.69141)
Survival probability
0.8
0.8
High
Survival probability
0.8
++ High
Survival probability
LIHC
0.0078
1.84747(1.17531,2.90403)
0.6
0.6
0.6
LUAD
0.7466
0.9407(0.64927,1.36294)
LUSC
0.1151
0.71209(0.46678,1.08633)
0.4
0.4
0.4
MESO
0.0004
3.31704(1.71104,6.43043)
0.2
Disease specific survival
0.2
Disease specific survival HR =0.71 (95% CI, 0.54-0.95)
0.2
HR =3.11 (95% CI, 1.62-5.97)
Disease specific survival
OV
0.0157
0.70708(0.53381,0.93657)
HR =1.71 (95% CI, 1.06-2.76)
0.0
P=0.001
0.0
P=0.019
0.0
P=0.028
PAAD
0.0169
1.78019(1.10916,2.85717)
0
25
50
75
0
50
100
150
0
25
50
75
PCPG
0.3319
0.32617(0.03393,3.13584)
Time, months
Time, months
Time, months
PRAD
0.5591
1.71142(0.28206,10.38404)
H
READ
0.3657
0.60322(0.20175,1.8036)
SARC
SARC
0.0123
1.76559(1.13135,2.75538)
1.0
TPGS2
Low
High
SKCM
0.3950
1.13211(0.85061,1.50677)
Survival probability
0.8
STAD
0.1495
1.35994(0.89529,2.06574)
0.6
THCA
0.8084
1.20375(0.269,5.38655)
THYM
0.2405
0.25638(0.02641,2.4888)
0.4
UCEC
0.1692
1.43377(0.85778,2.39652)
0.2
Disease specific survival
UCS
0.1394
1.71436(0.83888,3.50349)
HR =1.92 (95% CI, 1.23-3.01)
UVM
0.8278
1.0999(0.46627,2.59458)
0.0
P=0.004
0
50
100
150
200
0.02641
I
2
3
4
5
6
7
8
9
10
Time, months
Hazard Ratio
functional states have been linked to the occurrence and progression of cancers.
GSEA
To explore the biological processes related to TPGS2 expression across human cancers, we performed a differential expression analysis between the top 50% TPGS2 expression subgroup and the bottom TPGS2 expression subgroup for each type of cancer. We conducted a GSEA to evaluate the biological processes of 33 types of cancers from TCGA. The results showed that TPGS2 is likely involved in a great deal of immune regulation-related biological processes, especially immunoglobulin (Ig) production, the
humoral immune response mediated by circulating Ig, B cell-mediated immunity, the regulation of the humoral immune response, and the B cell receptor signaling pathway (Figure 9A-9F). This provides evidence that TPGS2 may be involved in the immune response and cancers, which prompted us to explore the role of TPGS2 in the cancer- immune process and immune microenvironment.
Immune cell infiltration analysis
To further explore the role of TPGS2 in tumor immunity, we explored the correlations between TPGS2 expression and immune cell infiltration levels across cancers. TIMER 2.0 was used to generate a heatmap associated with a variety
A
ALL
B
*
AML
*
**
**
*
**
**
**
**
geneExp
CML
**
**
*
**
**
**
**
**
**
**
**
G
CRC
*
Correlation
P value
BRCA
Expression distribution with t-SNE plot
**
**
**
*
*
**
*P<0.05
DNArepair
-0.56
AST
**
**
**
**
*
**
** P<0.01
50
GBM
**
**
**
*
**
**
*
Correlation
Glioma
**
**
**
**
**
*
1.0
DNAdamage
-0.53
3.3
HGG
**
**
**
*
**
**
**
**
*
**
**
**
25
ODG
2.7
**
*
**
**
*
**
**
**
0.5
HNSCC
tSNE2
2.2
**
**
*
**
**
*
**
**
RCC
0.0
Apoptosis
-0.49
0
1.6
LUAD
*
-0.5
1.1
*
**
NSCLC
**
**
0.5
**
**
**
**
*
**
**
**
**
**
**
OV
**
**
-1.0
Metastasis
-0.42
-25
0.0
MEL
*
**
*
**
*
**
*
Expression
RB
**
**
**
**
**
**
**
**
**
**
**
**
UM
**
**
**
**
**
**
**
**
**
**
**
**
**
Invasion
CellCycle
DNArepair
Inflammation
Proliferation
-0.41
-50
Angiogenesis
Apoptosis
Differentiation
DNAdamage
EMT
Hypoxia
Invasion
Metastasis
Quiescence
Stemness
-60
-40
-20
0
20
40
60
tSNE1
Quiescence
-0.38
C
D
E
F
geneExp
geneExp
Invasion
Correlation P value
Correlation P value
0.36
Differentiation
0.35
*
geneExp
geneExp
Correlation P value
Correlation
Differentiation
Proliferation
-0.45
EMT
III
0.31
P value *
0.31
Angiogenesis
0.30
U
H
Expression distribution with t-SNE plot
I
Expression distribution with t-SNE plot
J
Expression distribution with t-SNE plot
K
Expression distribution with t-SNE plot
30
75
150
200
20
3.3
50
100
150
2.7
3.1
10.4
10
25
2.6
50
8.7
100
2.6
2.2
tSNE2
1.6
2.1
6.9
2.2
1.7
0
1.1
0.5
INSI
0
1.6
tSNE2
50
1.0
0
5.3
tSNE2
3.5
92
1.3
..
-10
0.0
-25
0.5
-50
1.7
0
0.9
Expression
0.0
0.0
-50
0.4
0.0
-20
-50
Expression
-100
Expression
-100
Expression
-30
-75
-150
-150
-100
-50
0
50
100
150
-75
-50
-25
0
25
50
-75
-50
-25
0
25
50
75
100
-200
-100
0
100
200
tSNE1
ISNE1
tSNE1
ISNE1
of immune cell infiltration, which was performed on a variety of quantitative immuno-infiltration platforms. The results were then categorized based on different immune score categories (Figures S1-S6). These analyses showed the infiltration levels of cluster of differentiation (CD)8+ T cells, CD4+ T cells, T regulatory cells (Tregs), B cells, neutrophils, macrophages, progenitors, dendritic cells (DCs), mast cells, cancer-associated fibroblasts (CAFs), endothelial cell (endos), natural killer (NK) cells, T cell follicular helper (Tfh) cells, T cell gamma delta (yö’T) cells, NK T cells, monocytes, myeloid-derived suppressor cells (MDSCs), and eosinophils (Eos). As Figure 10 shows, TPGS2 was positively related to the level of many cells, such as macrophages, CD4+ T cells, CD8+ T cells, B cells, neutrophils, and CAFs. We also found that TPGS2
had a higher correlation with immune cell infiltration in THYM and HNSC than other tumors, especially in CD4+ T cells, CD8+ T cells, macrophages, and B cells. However, their trend of correlation was slightly different, which might be related to the immune specificity of these cancers.
Co-expression of TPGS2 with immune-associated genes
To further explore the role of TPGS2 in cancer immunity, we performed a Spearman correlation analysis to reveal the correlation between TPGS2 expression and immune-related genes (Figure 11A-11E). The heatmaps illustrated that the gene encoding immunoinhibitor, immunostimulator, MHC, chemokine, and chemokine receptor proteins were significantly correlated with the expression of TPGS2 in
A
PAAD
B
LIHC
C
COAD
GO HUMORAL IMMUNE RESPONSE MEDIATED BY CIRCULATING
GO B CELL RECEPTOR SIGNALING PATHWAY
IMMUNOGLOBULIN
I
GO IMMUNOGLOBULIN PRODUCTION
GO ADAPTIVE IMMUNE RESPONSE BASED ON SOMATIC RECOMBINATION OF IMMUNE RECEPTORS BUILT FROM IMMUNOGLOBULIN SUPERFAMILY
GO B CELL MEDIATED IMMUNITY -
.
GO FC RECEPTOR MEDIATED STIMULATORY SIGNALING PATHWAY
GO B CELL RECEPTOR SIGNALING
DOMAINS
PATHWAY
GO MEIOTIC CELL CYCLE PROCESS
GO RESPONSE TO CHEMOKINE
TT
GO COMPLEMENT ACTIVATION -
GO B CELL MEDIATED IMMUNITY
GO MEMBRANE INVAGINATION
T
GO PHAGOCYTOSIS RECOGNITION
GO POSITIVE REGULATION OF B CELL ACTIVATION
GO B CELL RECEPTOR SIGNALING PATHWAY
GO FC RECEPTOR MEDIATED
GO HUMORAL IMMUNE RESPONSE MEDIATED BY CIRCULATING IMMUNOGLOBULIN
STIMULATORY SIGNALING PATHWAY
P value
GO IMMUNOGLOBULIN PRODUCTION
GO MEIOTIC CELL CYCLE
TT
P value
GO PRODUCTION OF MOLECULAR MEDIATOR OF IMMUNE RESPONSE
P value
0.007
0.006
0.00115
0.04
GO POSITIVE REGULATION OF B CELL ACTIVATION
0.005
GO PHAGOCYTOSIS RECOGNITION
0.00110
GO ANTIGEN RECEPTOR MEDIATED SIGNALING PATHWAY
0.03
0.02
0.004
0.00105
0.01
GO REGULATION OF B CELL
ACTIVATION
GO REGULATION OF B CELL
0.00100
ACTIVATION
T
GO PHAGOCYTOSIS RECOGNITION
GO FC EPSILON RECEPTOR SIGNALING PATHWAY
GO MEIOSIS I CELL CYCLE
GO RESPONSE TO INTERFERON GAMMA
PROCESS
TT
TT
GO FC RECEPTOR SIGNALING PATHWAY
GO CHROMOSOME SEPARATION
GO MEMBRANE INVAGINATION -
GO MEMBRANE INVAGINATION
GO MITOTIC SISTER CHROMATID SEGREGATION
GO FC RECEPTOR MEDIATED STIMULATORY SIGNALING PATHWAY
GO REGULATION OF HUMORAL IMMUNE RESPONSE
GO CORNIFICATION
GO COLLAGEN FIBRIL
GO IMMUNOGLOBULIN PRODUCTION
GO POSITIVE REGULATION OF B CELL ACTIVATION
ORGANIZATION
GO REGULATION OF CHROMOSOME SEPARATION
GO CELL RECOGNITION
GO COMPLEMENT ACTIVATION
GO RESPONSE TO CHEMOKINE
GO METAPHASE ANAPHASE TRANSITION OF CELL CYCLE
GO POSITIVE REGULATION OF LYMPHOCYTE ACTIVATION
0.0
0 0.5
1.0
1.5
2.0
0
1
2
3
4
5
0
1
2
3
Enrichment score
Enrichment score
Enrichment score
D
ESCA
E
SKCM
F
THCA
GO HUMORAL IMMUNE RESPONSE MEDIATED BY CIRCULATING IMMUNOGLOBULIN
GO CORNIFICATION
I
GO HUMORAL IMMUNE RESPONSE MEDIATED BY CIRCULATING IMMUNOGLOBULIN
GO ANTIMICROBIAL HUMORAL IMMUNE RESPONSE MEDIATED BY ANTIMICROBIAL PEPTIDE
GO COMPLEMENT ACTIVATION
I
GO IMMUNOGLOBULIN PRODUCTION
GO PHAGOCYTOSIS RECOGNITION
GO KERATINIZATION
THỊ II I III
GO B CELL MEDIATED IMMUNITY .
GO REGULATION OF HUMORAL IMMUNE RESPONSE
GO ANTIMICROBIAL HUMORAL RESPONSE
GO COMPLEMENT ACTIVATION
GO B CELL RECEPTOR SIGNALING PATHWAY
GO SENSORY PERCEPTION OF SMELL
GO PHAGOCYTOSIS RECOGNITION
GO B CELL MEDIATED IMMUNITY -
GO KERATINOCYTE DIFFERENTIATION
GO B CELL RECEPTOR SIGNALING PATHWAY
P value
P value
P value
GO HUMORAL IMMUNE RESPONSE
GO ANTIBACTERIAL HUMORAL RESPONSE
0.00110
0.003
GO ORGAN OR TISSUE SPECIFIC IMMUNE RESPONSE
GO POSITIVE REGULATION OF B CELL ACTIVATION
GO IMMUNOGLOBULIN PRODUCTION -
0.001
0.002
0.00105
0.001
0.00100
GO MEMBRANE INVAGINATION
GO DEFENSE RESPONSE TO GRAM POSITIVE BACTERIUM
GO FC RECEPTOR MEDIATED STIMULATORY SIGNALING PATHWAY
GO REGULATION OF B CELL ACTIVATION
GO DEFENSE RESPONSE TO GRAM NEGATIVE BACTERIUM
GO LYMPHOCYTE MEDIATED IMMUNITY
TT
GO FC RECEPTOR MEDIATED STIMULATORY SIGNALING PATHWAY GO ADAPTIVE IMMUNE RESPONSE BASED ON SOMATIC RECOMBINATION
GO KILLING OF CELLS OF OTHER ORGANISM
GO REGULATION OF HUMORAL IMMUNE RESPONSE
GO MEMBRANE INVAGINATION
OF IMMUNE RECEPTORS BUILT FROM
GO REGULATION OF TYPE 2 IMMUNE RESPONSE
IMMUNOGLOBULIN SUPERFAMILY
DOMAINS
GO LYMPHOCYTE MEDIATED
IMMUNITY
GO DEFENSE RESPONSE TO FUNGUS
GO REGULATION OF B CELL ACTIVATION
GO POSITIVE REGULATION OF B CELL ACTIVATION
GO NEGATIVE REGULATION BY HOST
GO PRODUCTION OF MOLECULAR MEDIATOR OF IMMUNE RESPONSE
OF VIRAL PROCESS
GO PHAGOCYTOSIS
GO INNATE IMMUNE RESPONSE IN MUCOSA
GO PHAGOCYTOSIS
GO FC EPSILON RECEPTOR SIGNALING PATHWAY
GO CELL KILLING
II
GO FC EPSILON RECEPTOR SIGNALING PATHWAY
I
-3
-2
-1
0
-5 -4 -3 -2 -1
0
-4-3-2-1 0 Enrichment score
Enrichment score
Enrichment score
B cell memory_CIBERSORT
8 cell memory_CIBERSORT-ABS
8 cell naive_CIBERSORT-ABS
B cell plasma_CIBERSORT
B cell plasma_CIBERSORT-ABS 8 cell plasma_XCELL
Class-switched memory B cell_XCELL
T cell CD4+ (non-regulatory)_QUANTISEQ
T cell CD4+ (non-regulatory)_XCELL
T cell CD4+ naive CIBERSORT
T cell CD4+ naive_CIBERSORT-ABS
T cell CD4+ naive XCELL
T cell CD4+ memory_XCELL
T cell CD4+ central memory_XCELL
T cell CD4+ effector memory_XCELL
T cell CD4+ memory activated_CIBERSORT
T cell CD4+ memory activated_CIBERSORT-ABS
T cell CD4+ memory resting_CIBERSORT
T cell CD4+ memory resting_CIBERSORT-ABS
B cell_QUANTISEQ
T cell CD4+ Th1_XCELL
T cell CD4+ Th2_XCELL
T cell CD8+ MCPCOUNTER
T cell CD8+_CIBERSORT-ABS
T cell CD8+ central memory_XCELL
T cell CDS+ effector memory_XCELL
Macrophage MO_CIBERSORT
Macrophage MO_CIBERSORT-ABS
Macrophage MI_CIBERSORT
Macrophage MI_CIBERSORT-ABS
Macrophage MI_QUANTISEQ
Macrophage M2_CIBERSORT
Macrophage M2_CIBERSORT-ABS Macrophage M2_QUANTISEQ
Macrophage/Monocyte_MCPCOUNTER
Endothelial cell_MCPCOUNTER Endothelial cell_XCELL
Macrophage/Monocyte_MCPCOUNTER,
Monocyte_CIBERSORT-ABS
B cell_TIMER
8 cell_EPIC
B cell_XCELL
B cell_MCPCOUNTER
B cell memory_XCELL
B cell naive_CIBERSORT
B cell naive_XCELL
T cell CD4+_EPIC T cell CD4+ TIMER
T cell CD8+_TIMER
T cell CD8+_EPIC
T cell CD8+_CIBERSORT
T cell CD8+_QUANTISEQ
T cell CD8+_XCELL
T cell CD8+ naive_XCELL
Macrophage_EPIC
Macrophage_TIMER Macrophage_XCELL
Macrophage MI_XCELL
Macrophage M2_XCELL
Macrophage M2_TIDE
Endothelial cell_EPIC
Monocyte_CIBERSORT
Monocyte_MCPCOUNTER
Monocyte_QUANTISEQ Monocyte_XCELL
ACC (n=79)
BLCA (n=408)
BRCA (n=1100)
BRCA-Basal (n=191)
BRCA-Her2 (n=82)
BRCA-LumA (n=568)
BRCA-LumB (n=219)
CESC (n=306)
CHOL (n=36)
X
X
COAD (n=458)
DLBC (n=48)
ESCA (n=185)
GBM (n=153)
HNSC (n=522)
HNSC-HPV- (n=422)
HNSC-HPV+ (n=98)
KICH (n=66)
KIRC (n=533)
KIRP (n=290)
LGG (n=516)
LIHC (n=371)
LUAD (n=515)
LUSC (n=501)
MESO (n=87)
OV (n=303)
PAAD (n=179)
PCPG (n=181)
PRAD (n=498)
READ (n=166)
SARC (n=260)
SKCM (n=471)
SKCM-Metastasis (n=368)
SKCM-Primary (n=103)
STAD (n=415)
TGCT (n=150)
THCA (n=509)
THYM (n=120)
UCEC (n=545)
XIX
UCS (n=57)
X
X
Y
UVM (n=80)
X
X
X
B cell
CD4+ T cell
CD8+ T cell
Macrophage
Endo
Monocyte
☒
P>0.05
Neutrophil_CIBERSORT
Neutrophil_CIBERSORT. ABS Neutrophil_MCPCOUNTER
Neutrophil_QUANTISEQ
T cell regulatory [Tregs)_CIBERSORT
T cell regulatory [Tregs)_CIBERSORT-ABS T cell regulatory (Tregs)_QUANTISEQ
T cell regulatory (Tregs)_XCELL
Myeloid dendritic cell_TIMER
Myeloid dendritic cell_XCELL
Myeloid dendritic cell_MCPCOUNTER
Myeloid dendritic cell_QUANTISEQ
Myeloid dendritic cell activated_CIBERSORT
Myeloid dendritic cell activated_CIBERSORT-ABS
Myeloid dendritic cell activated_XCELL
Myeloid dendritic cell resting_CIBERSORT
Myeloid dendritic cell resting_CIBERSORT-ABS
Plasmacytold dendritic cell_XCELL
Cancer associated fibroblast_EPIC
Cancer associated fibroblast_MCPCOUNTER
Cancer associated fibroblast_XCELL
Ps0.05
Cancer associated fibroblast_TIDE
NK cel_MCPCOUNTER
NK cel_QUANTISEQ
NK cell activated_CIBERSORT
NK cel activated_CIBERSORT-ABS
NK cel resting_CIBERSORT
NK cel resting_CIBERSORT-ABS
T cell follicular helper_CIBERSORT
T cell follicular helper_CIBERSORT-ABS
Hematopoletic stem cell_XCELL
Common lymphoid progenitor_XCELL
Common myeloid progenitor_XCELL
Granulocyte-monocyte progenitor_XCELL
Neutrophil_XCELL
T cell gamma delta_CIBERSORT
T cell gamma delta_CIBERSORT-ABS T cell gamma delta_XCELL
Neutrophil_TIMER
Eosinophil_CIBERSORT
Eosinophil_CIBERSORT ABS
Mast cell activated_CIBERSORT ABS Mast cell resting_CIBERSORT
Eosinephil_XCELL
Mast cell activated_CIBERSORT
Mast cell resting_CIBERSORT-ABS
Partial_Cor
1
T cell NK XCELL
NK cel_EPIC
NK cel_XCELL
0
MOSC_TIDE
Mast cell_XCELL
-1
ACC (n=79)
X
X
BLCA (n=408)
XI
XIX
BRCA (n=1100)
BRCA-Basal (n=191)
BRCA-Her2 (n=82)
X
XIXIXIX
XX
BRCA-LumA (n=568)
XX
BRCA-LumB (n=219)
CESC (n=306)
CHOL (n=36)
XXXXX
X
X
X
X
X
XX [X]
COAD (n=458)
XXX
XX
DLBC (n=48)
XIXIXIX
XIXIX
X
XXIX
X
X
ESCA (n=185)
XIX
X
X
XXX
X
GBM (n=153)
X
XIXIX
XIX
XX
X
X
XX
XIX
HNSC (n=522)
HNSC-HPV- (n=422)
HNSC-HPV+ (n=98)
XIX
X
KICH (n=66)
XIX
X
XXX
KIRC (n=533)
KIRP (n=290)
X
XIX
LGG (n=516)
LIHC (n=371)
XIXIX
LUAD (n=515)
X
LUSC (n=501)
21521
MESO (n=87)
XIX
XIXIXIX
IXIX
IXIXIX
XIX
XXIXI
XXX
OV (n=303)
PAAD (n=179)
XIXIX
PCPG (n=181)
XXIX
XIX
PRAD (n=498)
READ (n=166)
XIX
XXI
XX
SARC (n=260)
X
SKCM (n=471)
SKCM-Metastasis (n=368)
SKCM-Primary (n=103)
X
XIXIXIX
X
XIXI
STAD (n=415)
TGCT (n=150)
XIXIX
XIX
XIXIX
XIX
THCA (n=509)
THYM (n=120)
UCEC (n=545)
UCS (n=57)
XI
X
XIXIX
X
X
UVM (n=80)
4
Neutrophil
Tregs
DC
CAF
NKT
NK cell
MDSC
Tfh
Hsc
Progenitor
γoT
Eos
Mast cell
A
ACC
B
ACC
BLCA
BLCA
BRCA
BRCA
CESC
CESC
CHOL
CHOL
COAD
COAD
DLBC
DLBC
ESCA
ESCA
GBM
GBM
HNSC
*P<0.05
HNSC
**
*P<0.05
KICH
KICH
KIRC
** P<0.01
KIRC
** P<0.01
KIRP
KIRP
Correlation
LAML
Correlation
LAML
LGG
1.0
LGG
1.0
LIHC
LIHC
LUAD
0.5
LUAD
0.5
LUSC
LUSC
MESO
0.0
MESO
0.0
OV
-0.5
OV
PAAD
-0.5
PAAD
PCPG
-1.0
PCPG
-1.0
PRAD
PRAD
READ
READ
SARC
SARC
SKCM
SKCM
STAD
STAD
TGCT
TGCT
THCA
THCA
THYM
THYM
UCEC
UCEC
UCS
UCS
UVM
**
UVM
**
**
CCL1
CCL2
CCL3
CCL4
CCL5
CCL7 CCL8
CCL 11
CCL13
CCL14
CCL15
CCL16
CCL 17
CCL 18
CCL19
CCL20
CCL21
CCL22
CCL23
CCL24
CCL25
CCL26
CCL27
CCL28
CX3CL1
CXCL 1
CXCL2
CXCL3
CXCL5 CXCL6
CXCL8
CXCL9
CXCL10
CXCL 11
CXCL12
CXCL13 CXCL14
CXCL 16
CXCL17
XCL1
XCL2
B2M
HLA-A
HLA-B
HLA-C
HLA-DMA
HLA-DMB
HLA-DOA HLA-DOB
HLA-DPA1 HLA-DPB1
HLA-DQA1
HLA-DQA2 HLA-DQB1
HLA-DRA
HLA-DRB1
HLA-E
HLA-F HLA-G
TAP1
TAP2
TAPBP
C
ACC
**
**
D
BLCA
ACC
**
**
BLCA
**
BRCA
CESC
BRCA
**
**
CHOL
CESC
COAD
CHOL
DLBC
**
COAD
**
**
**
**
ESCA
DLBC
**
GBM
ESCA
HNSC
*P<0.05
GBM
KICH
HNSC
** *P<0.05
KIRC
** P<0.01
KICH
** P<0.01
KIRP
**
LAML
Correlation
KIRC
KIRP
**
1.0
LAML
Correlation
LGG
**
LIHC
LGG
1.0
LUAD
0.5
LIHC
**
LUSC
LUAD
**
0.5
MESO
0.0
LUSC
0.0
OV
PAAD
-0.5
MESO
OV
-0.5
PCPG
**
PRAD
-1.0
PAAD
PCPG
**
-1.0
READ
PRAD
SARC
READ
SKCM
SARC
STAD
SKCM
TGCT
STAD
THCA
TGCT
THYM **
THCA
**
UCEC
THYM
**
UCS
UCEC
**
UVM
*
**
UCS
ADORA2A
BTLA
CD160
CD244
CD274
CD96
CSF1R
CTLA4
HAVCR2
IDO1
IL10
IL 10RB
KDR
KIR2DL1
KIR2DL3
LAG3
LGALS9
PDCD1
PDCDILG2
NECTIN2
TGFB1
TGFBR1
TIGIT
VTCN1
UVM
**
CCR1
CCR2
CCR3
CCR4
CCR5
CCR6
CCR7
CCR8
CCR9
CCR10
CXCR1
CXCR2
CXCR3
CXCR4 CXCR5
CXCR6
XCR1
CX3CR1
E
ACC
**
**
**
**
**
**
**
BLCA
BRCA
CESC
CHOL
**
COAD
DLBC
ESCA
GBM
HNSC
*P<0.05
KICH
KIRC
** P<0.01
KIRP
LAML
Correlation
LGG
1.0
LIHC
LUAD
0.5
LUSC
MESO
0.0
OV
**
PAAD
-0.5
PCPG
PRAD
-1.0
READ
SARC
SKCM
STAD
TGCT
**
THCA **
THYM
**
UCEC
**
UCS
UVM
**
**
**
**
**
BTNL2
VSIR
CD27
CD276
CD28
CD40
CD40LG
CD48
CD70
CD80
CD86
CXCL12
CXCR4
ENTPD1
HHLA2
ICOS
ICOSLG
IL2RA
IL6
IL6R
KLRC1 KLRK1
**
LTA
MICB
NTSE
PVR
RAET1E
STING1
TMIGD2
TNFRSF13B
TNFRSF13C
TNFRSF14
TNFRSF17
TNFRSF18
TNFRSF25
INFRSF4 TNFRSF8
**
TNFRSF9
TNFSF13
TNFSF13B
TNFSF14
TNFSF15
TNFSF18
TNFSF4
TNFSF9
ULBP1
A
STAD
COAD
THYM
ESCA **
B
0.4
KIRC
COAD
STAD
DLBC *
PCPG *
0.3
ACC
ACC
CHOL
CHOL
KIRP
SKCM
*
0.2
THCA
MESO
0.2
0.1
UCS
UCS
0
CESC
OV
0
THYM
TGCT
-0.2
MESO
KIRC
-0.
PRAD
-0.2
BLCA *
-0.4
KIRP
GBM
-0.3
LUAD
LAML
-0.6
LUSC
BRCA **
-0.4
ESCA
BRCA *
UVM
LIHC
KICH
KICH
PAAD
PAAD
LUSC
LGG
LIHC
READ
THCA
PCPG
PRAD
TGCT
SKCM
HNSC
DLBC
LAML
SARC
LUAD
UCEC
SARC
LGG
BLCA
OV
READ
GBM
CESC
UCEC
HNSC
UVM
Correlation (TMB)
Correlation (MSI)
most cancers. The correlation analyses revealed strong connections between TPGS2 and specific cancer types, such as HNSC, LIHC, PAAD, PRAD, SARC, THYM, and UM. Additionally, TPGS2 was positively correlated with most of the immunomodulatory factors in COAD, HNSC, KIRP, LIHC, PAAD, PRAD, READ, and UVM, but negatively correlated with ESCA and SARC.
TMB and MSI analysis
The TMB and MSI are two well-known biomarkers that predict immune therapy responses across different cancers (41,42). Most scholars believe that patients with a high TMB and MSI have increased response rates to immunotherapy and display better outcomes to immunotherapy treatments. Therefore, we assessed the correlation with TMB and MSI to evaluate the efficacy of TPGS2 in predicting ICIs therapy outcomes in pan-cancer. As Figure 12 shows, TPGS2 expression was positively correlated with the TMB in BRCA, BLCA, ACC, SKCM, STAD, and COAD, and TPGS2 expression was negatively correlated with the TMB in ESCA, KIRC, THCA, and THYM (Figure 12A). Additionally, TPGS2 expression was positively correlated with MSI in BRCA, COAD, and STAD, and TPGS2 expression was negatively correlated with MSI in DLBC and PCPG (Figure 12B). Thus, our analyses indicate that TPGS2 could have a potential role in predicting the effectiveness of ICIs in a number of cancers.
Discussion
Currently, many immune checkpoint molecules have been applied to pharmacotherapy, such as CTLA-4, PD-1, PD-L1, T cell immunoreceptors with Ig and immunoreceptor tyrosine-based inhibitory motif domains (TIGIT), and lymphocyte activating 3 (43-47). CTLA-4 and PD-L1/PD-1 have received the greatest attention; thus, anti-PD-1/PD-L1 and anti-CTLA-4 techniques have been applied to the immunotherapy of a number of cancers (46). However, at present, the most serious challenges for such treatments are related to their inapparent efficacy and side effects (48). As a result, there is a pressing need to investigate novel immunological checkpoints and methods to estimate the effects of cancer immunotherapy (48,49). Our results showed that TPGS2 is a promising biomarker of cancer, which provides a vital clue for further research on the potential role of TPGS2 in prognosis and tumor immunity.
The analysis of TPGS2 expression based on the GTEx and TCGA databases revealed that TPGS2 was abnormally upregulated in 22 cancers and downregulated in five cancers (Figure 1A). Moreover, the expression of TPGS2 was notably downregulated in TGCT tissue compared with normal testicular tissue. Data about RNA was used to cluster genes based on their expression in single-cell types. Based on the single-cell type expression cluster from HPA, we found that TPGS2 was mainly expressed in spermatids.
Thus, we speculate that the specific expression in normal testicular tissue is connected with the lower expression of TPGS2 in TGCT. However, it is not yet known why TPGS2 is unevenly expressed across cancers.
Next, the IHC results from the HPA were consistent with our preliminary conclusions (Figure 2). According to the analysis of the association between TPGS2 and various subtypes, TPGS2 may be associated with molecular and immune subtypes across human cancers (Figure 3). This suggests that TPGS2 can be used to differentiate among molecular and immune types of tumors. Since most genetic alteration proportions of TPGS2 in cancers are less than 5%, there appears to be no significant correlation between TPGS2 expression and genomic alterations (Figure 4A).
In addition, we evaluated the clinical prognosis of patients who were grouped according to TPGS2 expression levels. There were differences among the various survival measures (i.e., OS, PFI, and DSS); however, the expression of TPGS2 was still significantly associated with survival (Figures 5-7). According to the Kaplan-Meier and univariate Cox regression analyses, the upregulated expression of TPGS2 was associated with a poor prognosis in patients with SARC, LIHC, and LGG, while the high expression of TPGS2 was associated with a better OS prognosis in patients with OV and THYM. Thus, TPGS2 is likely to be an important biomarker for predicting the prognosis of cancer patients.
The single-cell analysis showed that TPGS2 expression was associated with a number of functional states, including the cell cycle, metastasis, invasion, inflammation, DNA damage, and stemness, in various cancers (Figure 8). These results suggest that TPGS2 is associated with multiple cancer functional states in many human cancers.
According to the GSEA, TPGS2 was closely related to some immune response processes, such as Ig production, B cell-mediated immunity, the humoral immune response mediated by circulating Ig, the regulation of the humoral immune response, and the B cell receptor signaling pathway (Figure 9). Therefore, it is very likely that TPGS2 is involved in the functions related to B cells.
Recently, the importance of B cells has been found in tumor immunity. Since 2020, three research teams from the United States, France, and Sweden have analyzed a large sample of clinical cohort studies, and reported a positive correlation between B-cell infiltration and the formation of TLSs and the response to immunotherapy in a variety of cancer types (50-52). B cells also express a number of checkpoint molecules, including PD-1, PD-L1/2, and
CTLA-4B (15). Patients who responded to ICIs therapy were reported that more memory B cells, C-X-C motif chemokine receptor 3+ cells, and germinal center-like B cells were found in their TMEs than patients who did not response to ICIs therapy (52).
In addition, studies (21,53) have shown that the Igs in many antibody-secreting B cells, which mainly secrete IgG and IgA, are tumor-dependent. These Igs are correlated with the tumorigenesis site, and higher proportions of IgG have been observed in thyroid, testicular, and skin tumors, while higher proportions of IgA been observed in kidney, ovarian, pancreatic, and colorectal cancers. This is consistent with our GSEA finding that a higher number of Ig-related biological states were enriched.
Another important finding of our research is that TPGS2 plays a pivotal role in cancer immunity. In recent years, many studies have shown that the immune status of cancers is closely correlated to the cell composition of and infiltration concentration around the tumor (54-56). TPGS2 was found to be positively correlated with the infiltrating levels of multiple immune cells, such as macrophages, CD4+ T cells, CD8+ T cells, B cells, and neutrophils (Figure 10), which suggests that TPGS2 is likely to influence development and prognosis of various cancers by affecting the TME.
Pro-inflammatory mediators, including chemokines, cytokines, and prostaglandins, have been found in the TME, which affects tumor initiation, progression, and metastasis (57-59). Our analyses of TPGS2 expression and pan-cancer immunomodulatory factors suggested that TPGS2 is co-expressed with genes that encode the immunoinhibitor, immunostimulator, MHC, chemokine, and chemokine receptor proteins, especially in THYM and HNSC (Figure 11). These results suggest that TPGS2 is likely to be involved in the progression and prognosis of cancers by interacting with the TME.
Further, the TMB is a promising prognostic and predictive biomarker for immunotherapy in human cancers (60-63). Research has demonstrated that patients, suffering from melanoma (64,65) or urothelial carcinoma (66,67) with a high TMB achieve better clinical outcomes from ICIs. Similarly, MSI also plays a vital role as a predictive biomarker for tumor immunotherapy (68). The Food and Drug Administration has authorized MSI-high status or deficient mismatch repair as prognostic biomarkers for directing the therapeutic application of ICIs in certain malignancies (69). According to our analyses, the TMB in 10 types of cancers and MSI in five types of cancers were
significantly correlated with the expression of TPGS2. Thus, TPGS2 is likely to act as a predictor of the efficacy of immunotherapy in many cancers.
Wang’s study demonstrated that the cir-TPGS2 (derived from TPGS2)-related axis promoted breast cancer cell motility by the TME (30). Another study demonstrated that TPGS2 could be a potential gene in renal cell carcinoma (29). Together with our findings, such results suggest that TPGS2 could serve as a potential biomarker in anti-tumor immunity treatments.
However, our research had a number of limitations. First, while we showed that TPGS2 is a promising predictor of prognosis and immunotherapy in many cancers, the mechanism by which this occurs remains unknown, and we have no evidence of any direct interaction. Second, our data were mainly obtained from open databases, and no clinical cohort was used for verification, which inevitably led to various biases and decreased the credibility of the results. Third, TPGS2 has rarely been studied in human tumors, and in-depth studies need to be conducted to verify its role in cancer prognosis prediction and immunotherapy. Fourth, our research revealed a promising direction for tumor research in TPGS2; however, this study was a descriptive study based on bioinformatics, and the mechanism related to both TPGS2 and the development and therapy of specific tumors need to be further explored, and experimental verification in vitro and in vivo is required. In the future, we intend to conduct further experiments to examine the mechanism of TPGS2 across human cancers.
Conclusions
In summary, we performed a comprehensive pan-cancer analysis that demonstrated that the aberrant expression of TPGS2 was associated with the prognosis, immune cell infiltration, TME, some immune response processes, and various function states across human cancers. Thus, TPGS2 could serve not only as a biomarker for predicting clinical outcomes, but also as a promising biomarker for evaluating and developing new approaches to immunotherapy in many types of cancers, especially COAD and STAD.
Acknowledgments
We greatly appreciate the TCGA database for providing useful data, as well as bioinformatics tools for data analyses. Funding: This work was supported by Zhejiang Province Medicine and Health Science and Technology Plan Project
(Nos. 2022KY966 and 2021KY250).
Footnote
Reporting Checklist: The authors have completed the REMARK reporting checklist. Available at https://tcr. amegroups.com/article/view/10.21037/tcr-23-113/rc
Peer Review File: Available at https://tcr.amegroups.com/ article/view/10.21037/tcr-23-113/prf
Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups. com/article/view/10.21037/tcr-23-113/coif). The authors have no conflicts of interest to declare.
Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013).
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Cite this article as: Ding Z, Ding Q, Li H. The prognostic biomarker TPGS2 is correlated with immune infiltrates in pan-cancer: a bioinformatics analysis. Transl Cancer Res 2024;13(3):1458-1478. doi: 10.21037/tcr-23-113