Open Access

Bioinformatics Analysis of Immune Infiltration In Endometrial Carcinoma Tumors and its Relationship With Prognosis

LIN YAO 1 2
LIU SONGYI 3
BIAN LIHONG 1 2
LIN CHUNLIN 3
LIN PENGHANG 3
TENG ZUHONG 3
HOFFMAN ROBERT M. 4 5
ZHU GUANGWEI 3
  &  
ZHANG BIN 1 2

1Department of Obstetrics and Gynecology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, P.R. China

2National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, P.R. China

3Department of Gastrointestinal Surgery 2nd Section, Institute of Abdominal Surgery, Key Laboratory of Accurate Diagnosis and Treatment of Cancer, The First Affiliated Hospital of Fujian Medical University, Fuzhou, P.R. China

4AntiCancer, Inc., San Diego, CA, U.S.A.

5Department of Surgery, University of California, San Diego, CA, U.S.A.

Cancer Diagnosis & Prognosis Jul-Aug; 3(4): 416-427 DOI: 10.21873/cdp.10234
Received 07 February 2023 | Revised 21 July 2024 | Accepted 03 April 2023
Corresponding author
Dr. Guangwei Zhu, Department of Gastrointestinal Surgery 2nd Section, Institute of Abdominal Surgery, Key Laboratory of Accurate Diagnosis and Treatment of Cancer, The First Affiliated Hospital of Fujian Medical University, 20th, Chazhong Road, 350005 Fuzhou, Fujian, PR China. E-mail: zgwzsy@126.com and Dr. Bin Zhang, Department of Obstetrics and Gynecology, The First Affiliated Hospital of Fujian Medical University, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, 20th, Chazhong Road, 350005 Fuzhou, Fujian, PR China. E-mail: 2212457407@qq.com

Abstract

Background/Aim: Endometrial carcinoma (EC) is the most common gynecological cancer, but lacks specific targetable markers. In order to explore the immune-related molecules that affect the progression and prognosis of EC, we analyzed the differential expression of genes in different histological grades of the disease. Materials and Methods: EC-related gene-expression data of different histological grades were downloaded from TCGA and GEO databases. The list of immune-related genes was obtained from the ImmPort database. In order to identity differentially-expressed genes (DEGs), differential-expression analysis was performed. The intersection of DEGs and immune-related genes was termed immune-related differentially-expressed genes (IRDEGs). IRDEGs were enriched in cancer-related functional pathways by gene-correlation analysis and GSEA-enrichment analysis. The association of IRDEGs with immune-cell tumor infiltration and gene polymorphisms was analyzed using IRDEG mRNA and protein-expression data in EC from TCGA and THPA databases. Results: Three IRDEGs, TNFSF15, SEMA3E and TNFSF10, were involved in the analysis of the prognosis of EC patients. IRDEGs were not only related to clinical characteristics but could also affect the prognosis of patients. Gene-correlation and GSEA-enrichment analysis of IRDEGs showed that TNFSF15 and TNFSF10 were co-enriched in the IL2-STAT5 functional pathway. IRDEGs had a significant correlation with a variety of immune-cell types infiltrating EC tumors and were related to EC prognosis. IRDEG mRNA- and protein-expression levels were increased in EC compared to normal tissues. Conclusion: TNFSF15, SEMA3E and TNFSF10 may regulate the progression and prognosis of EC patients by affecting immune-cell infiltration of EC tumors.
Keywords: Endometrial carcinoma, bioinformatics, gene databases, differential gene expression, histological grade, correlation, diagnosis, immune-cell infiltration, prognosis

Gynecological malignancies mainly include cervical cancer, endometrial carcinoma (EC) and ovarian cancer. EC is the most common gynecological cancer in the world (1,2). It mainly occurs in post-menopausal women with an average age of 60 at diagnosis (3). At present, EC lacks specific targetable markers, and the treatment efficacy of advanced cancer is poor (4).

The goal of immunotherapy is to reverse the immune-escape mechanism of tumors and restore the local immune response to cancer cells (5,6). The influence of immune-cell infiltration in tumors is very important, because the type of immune-cell infiltration in tumors mainly determines whether the balance tends to an anti-tumor or tumor-promotion immune response (7). Since the first PD-1/PD-L1 inhibitors were approved by the Food and Drug Administration (FDA) in 2014, many immune-checkpoint inhibitors have been used in clinical practice (8). Therefore, exploring the key immune- regulation genes and signaling pathways related to EC may clarify the critical molecular mechanisms of EC and improve the level of diagnosis and immunotherapy.

In the present study, The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) and ImmPort databases were used to screen for immune-related differentially-expressed genes (IRDEGs) in EC, and identity their relationships with clinico-pathological parameters and prognosis. TIMER and cBiopPortal databases were used to analyze the correlation between IRDEGs and immune-cell types present in EC tumors as well as gene polymorphisms, in order to provide a basis for the discovery of new immune targets for EC.

Materials and Methods

Data acquisition. All gene-expression profile data and clinical data of EC patients containing histological-grading information were downloaded and collated from TCGA database (https://cancergenome.nih.gov/). 541 EC samples were included, of which 218 samples were G1-G2 grade and 323 samples in G3 grade. We combined EC gene-expression data with histological information from the GEO database, GSE115810, including 18 samples of G1-G2 grade and 6 samples of G3 grade. The ImmPort database (https//www.immport.org/home) was used to identify immune-related gene groups.

Screening for IRDEGs. The differentially-expressed genes (DEGs) in G1-G2 and G3 grades of EC were analyzed with the “DESeq2” package of R software (V 4.0.4). The screening thresholds were log2 fold change (FC) > | 0.5 | and p-value <0.05. The “ggplot2” package of R was used to visualize DEGs. In addition, the intersection of DEGs and immune-related genes in the ImmPort database was obtained with the “VennDiagram” program package.

The relationship between IRDEG and clinico-pathological parameters and prognosis of endometrial carcinoma. The clinical data of patients were obtained from TCGA database, and the relationship between IRDEGs was determined from the above-mentioned intersection and clinical stage, histological type and tumor invasion of EC patients. The survival data in TCGA database were used to analyze the relationship of IRDEGs to the prognosis of EC patients, including overall survival (OS), disease-specific survival (DSS) and platinum-free interval (PFI).

Gene Set Enrichment Analysis (GSEA). GSEA is a computational method that can be used to analyze the potential functional pathways of a single gene. First, we used TCGA database to analyze the IRDEGs, and then sorted the relevant genes as the input gene set, in order to determine their enrichment in different biological functions and signal pathways.

The “ClusterProfiler” package of R software, select “h.all.v7.2.symbols.gmt” was used as the annotation gene set, and selected 1000 replacement times. In the results, a normalized enrichment score (NES) >1 and adjp<0.05 were considered to be statistically significant.

Relationship between IRDEGs and immune-cell infiltration in EC tumors. The “GSVA” package of R software was used to analyze the relationship between IRDEGs and immune cells infiltrating EC tumors. The “ssGSEA” algorithm built into the “GSVA” package was used as the immune-infiltration algorithm. TIMER2.0 (http://timer.cistrome.org/) was used to provide comprehensive analysis and visualization of EC tumor-infiltrating immune cells, which was used to study the association between tumor immune infiltration and genetic or clinical characteristics (9). We further analyzed the relationship between IRDEGs and immune cells in EC tumors; and between immune cells in EC tumors and EC prognosis using the TIMER2.0 database.

Relationship between IRDEGs and gene polymorphisms. The cBioPortal for Cancer Genomics (http://cbioportal.org) provides a Web resource for exploring, identifying, and analyzing multi-dimensional cancer-genomics data (10). The cBioPortal database was used to analyze the relationship between IRDEGs and gene polymorphisms.

Analysis of mRNA and protein expression levels of IRDEGs in EC. TCGA database was used to further analyze the differential expression of IRDEG mRNA in EC. The human-protein expression atlas (THPA) (https://www. protein atlas. org/) provides the expression maps of more than 24000 human proteins in various pathological and normal tissues. Through this database, the protein-level expression of IRDEGs in EC and normal tissues was analyzed.

Results

Identification of IRDEGs in EC. Differential gene-expression was investigated in 218 cases of G1-G2 and 313 cases of G3 EC in the TCGA database. Based on log2FC and p-values, 4,565 differentially-expressed mRNAs were obtained, including 2,822 up-regulated and 1,743 down-regulated DEGs (Figure 1A). Similarly, according to threshold screening, 1,309 DEGs were obtained from the GSE89102 dataset, of which 608 were up-regulated and 701 were down-regulated (Figure 1B). In the data from TCGA and GSE89102, there are 130 common DEGs, of which 3 genes are IRDEGs: TNFSF15, SEMA3E and TNFSF10 (Figure 1C).

Correlation of IRDEGs with clinico-pathological parameters of EC. By analyzing the relationship between IRDEGs and EC clinico-pathological parameters, it was found that TNFSF15 and SEMA3E were significantly correlated with clinical stage, histological type, and tumor invasion; TNFSF10 was significantly correlated with clinical stage and histological type, but not with tumor invasion (Figure 2).

IRDEGs correlate with EC prognosis. Kaplan-Meier survival analysis showed that TNFSF15, SEMA3E and TNFSF10 corrected with the overall survival (OS) of EC patients. The higher the expression of TNFSF15 and SEMA3E, the longer the survival of patients (Figure 3A-B). In contrast, the higher the expression of TNFSF10, the worse the prognosis of patients (Figure 3C). In addition, both TNFSF15 and TNFSF10 correlated with the DSS and PFI of EC patients, while SEMA3E only correlated with the DSS of EC patients and has no significant effect on PFI (Figure 3E-I).

Gene set enrichment analysis (GSEA). Through gene correlation analysis of IRDEGs, it was found that the top five genes most positively related to TNFSF15 are TMEM144, PLEKHA6, GCNT3, TLR3 and CCL20, respectively. The top five genes most negatively related to TNFSF15 are BMP7, ZHX1-C8orf76, ZDHHC19, NECAB3 and SNTA1, respectively (Figure 4A). The top five genes most positively related to SEMA3E are PKHD1l1, HPGD, ADAMTS6, BMPR1B and PIK3R1, respectively. The top five genes with greatest negative correlation to SEMA3E, are respectively: RAPEF3, B3GNT3, XDH, S100A1 and PRRX2 (Figure 4B). The top five genes most positively related to TNFSF10 are RTP4, NCEH1, SP100, PLSCR1 and VTCN1, respectively. The top five most negatively correlated genes related to TNFSF10 are ZFPM1, NME4, ATP5F1D, RPS15 and MRPL41, respectively (Figure 4C).

GSEA enrichment analysis of all the above-related genes as input gene sets showed that TNFSF15 is significantly related to apoptosis and the IL2-STAT5 signaling pathway. SEMA3E is significantly related to an early estrogen-response and protein-secretion function. TNFSF10 is significantly related to IL2-STAT5 and the KRAS-signaling pathways (Figure 4D-I).

IRDEGs are associated with immune-cell infiltration in EC tumors. The abundance of six types of immune cells in EC tumors in the high- and low-expression groups of IRDEGs was further compared. Combined with the results of the “ssGSEA” algorithm and TIMER2.0 database, it was found that neutrophils and B-cells in the tumors in the TNFSF15 high-expression group were higher than those in the low-expression group, and the difference was statistically significant (Figure 5A-B). TNFSF10 is mainly positively correlated with the extent of macrophages, B cells and neutrophils in EC tumors (Figure 5E-F). SEMA3E was not found to be significantly related to immune-cell types in EC tumors from the intersection of the two modes (Figure 5C-D).

By analyzing the relationship between six types of immune cells in EC tumors and the prognosis of EC patients, we found that the higher the infiltration degree of B-cells, CD8+ T cells and CD4+ T cells, in EC tumors, the better the prognosis of patients (Figure 6).

Analyzing the mutations in IRDEGs, demonstrated that the mutation rates of TNFSF15, SEMA3E and TNFSF10 genes were 2.1%, 4% and 8% in EC, respectively. The mutation types of the TNFSF15 gene in EC are deep deletions, truncating mutations, and missense mutations. The mutation types of the SEMA3E gene in EC are amplifications, deep deletions, truncating mutations, inframe mutations and missense mutations, while most of the mutation types of the TNFSF10 gene in EC are gene amplifications, and only one case had a truncating mutation (Figure 7A).

In addition, we also found that patients with uterine endometrioid carcinoma were more likely to have TNFSF15 mutations. However, SEMA3E and TNFSF10 had mutations in uterine endometrioid carcinoma, uterine serous carcinoma, and uterine papillary serous carcinoma (Figure 7B-D).

IRDEGs mRNA and protein expression in EC and normal tissue. TNFSF15, SEMA3E and TNFSF10 mRNA expression in EC is significantly higher than in normal tissues (Figure 8A-C). THPA database was used to analyze the expression of TNFSF15, SEMA3E and TNFSF10 proteins, and the results were consistent with mRNA expression: The protein expression of TNFSF15, SEMA3E and TNFSF10 proteins are increased in EC compared to normal tissue(Figure 8D).

Discussion

EC is a common malignancy of the female reproductive system, and its incidence rate is rising (11). In addition, although some patients with EC can be successfully treated by hysterectomy after early diagnosis, younger patients may need to retain fertility, while others are too old to tolerate surgery or its side effects, so routine surgery is not the best option (12,13). At present, many studies have shown that there is a significant relationship between host immunity and solid tumors (14), but its complex interaction relationship still needs to be further explored. Gene-expression and immune-related gene libraries provide rich information for exploring genomic characteristics of tumor progression (15). Immune-related biomarkers that can predict prognosis and that can be used in clinical treatment are needed. In the present study, IRDEGs with differential expression in EC grade G3, compared with grades G1-G2, were screened through TCGA, GEO and ImmPort databases, and their relationship with clinico-pathological parameters and prognosis of EC was further analyzed. Gene-expression correlation and GSEA-enrichment analysis to identify the potential functional pathways of IRDEGs and analyze their association with six immune-cell types in EC tumors and gene polymorphisms, provide important information for clarifying the molecular basis of EC development and identifying potential immuno-therapeutic targets.

TNFSF15, SEMA3E, and TNFSF10 were identified by analyzing EC IRDEGs. TNFSF15 is an endogenous angiogenesis-inhibitor and a key component of the negative-control mechanism that plays a role in normal ovaries, but is missing in ovarian cancer (16). SEMA3E is highly expressed in metastatic cancer cells, which can drive the invasive and metastatic spread of human cancer cells in mice (17), and can also predict lymph-node metastasis of tongue squamous-cell carcinoma (18). In addition, SEMA3E is highly expressed in human high-grade ovarian EC, and through the Plexin-D1 receptor, enhances cell migration and the epithelial-mesenchymal transformation (19). It was reported that TNFSF10 mediates apoptosis of EC cells by binding with death receptors, which may be an effective target for the treatment of EC and other female reproductive cancer (20), which indirectly confirms the results of the present study.

Gene-correlation analysis of IRDEGs showed that TMEM144, PLEKHA6, GCNT3, TLR3 and CCL20 are most positively correlated with TNFSF15 expression, respectively. The top genes-most negatively-correlating with TNFSF15 are BMP7, ZHX1-C8orf76, ZDHHC19, NECAB3 and SNTA1, respectively. CCL20 and TNFSF15 are involved in osteoblast biology (21). However, whether these two molecules interact in cancer is still unclear, which needs to be further explored. Both BMP7 and TNFSF15 can stimulate epithelial-mesenchymal transformation of intestinal fibrosis associated with chronic colitis (22). At present, the other 8 genes have not been reported to be associated with TNFSF15, which is worthy of further research. More studies are needed on the top 10 genes significantly related to SEMA3E and TNFSF10. GSEA-enrichment analysis showed that TNFSF15 and TNFSF10 are enriched in the IL2-STAT5 signal pathway. TNFSF15 promotes T-cell-mediated immune function by enhancing the differentiation and pathogenicity of IL9-producing T-cells through IL-2- and STAT5-dependent mechanisms (23), which directly indicates that the TNFSF family plays an important role in tumor immunity. TNFSF15 and TNFSF10 are also enriched in apoptosis and KRAS-signaling pathways, respectively. The TNFSF family is closely related to cancer-cell apoptosis (24-26). Silencing KRAS in pancreatic-ductal- adenocarcinoma cells increased TNFSF10-induced apoptosis (27).

The immune microenvironment, including, but not limited to tumor-infiltrating lymphocytes, regulatory T-cells and tumor-related macrophages, can affect the progression of solid tumors, such as breast cancer, ovarian cancer and kidney cancer (28). The immune microenvironment, comprising cancer cells, stromal cells and immune cells, is considered the “fertile soil” for malignant transformation (29), but there are also many immune factors that can promote or inhibit tumor growth (30). The present study clarified that TNFSF15 and TNFSF10, both members of the TNFSF family, can drive B-cells and CD8+ T-cells to regulate the immunogenicity of EC and affect its prognosis. SEMA3E can affect macrophages to influence the progression of EC. TNFSF family members regulate cell differentiation, survival, and programmed death, but their most critical functions are related to the immune system. Both innate and adaptive immune cells are controlled by members of the TNFSF family, and are crucial for coordinating the various mechanisms of co-stimulation or co-inhibition that drive the immune response (31), which is basically consistent with the present findings. In addition, SEMA3E also regulates a variety of immune-cell types to control systemic inflammatory responses (32-34). The present study found that these three genes have various types of mutations in EC, which may be important to its pathogenicity.

The present study found that TNFSF15, SEMA3E and TNFSF10 can not only affect the prognosis of EC patients, but also correlate with a variety of immune-cell types infiltrating EC tumors that may be able to participate in the regulation of tumor immune-response or escape, thus also affecting the prognosis of patients. TNFSF15 promotes macrophage differentiation and polarization to the M1 phenotype to inhibit tumor growth (35). SEMA3E is a biomarker of sensitivity of melanoma immune-checkpoint therapy (36). TNFSF10 plays a crucial role in regulating multiple interactions between cancer cells and the tumor microenvironment (37), which indirectly confirms the present results, but further research is necessary.

Conclusion

In conclusion, the present study, using TCGA, GEO and ImmPort databases, has shown three IRDEGs, TNFSF15, SEMA3E and TNFSF10, have altered expression in EC at different histological grades. These three IRDEGs can significantly affect the prognosis of patients with EC. These three genes may also regulate the progression and prognosis of EC by affecting tumor immune-cell infiltration. This provides a novel immune target and direction for EC research to improve the outcome of this recalcitrant disease.

Conflicts of Interest

The Authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Authors’ Contributions

Y.L. and S.L. designed this experiment. Y.L., L.B., C.L. and P.L. analyzed data. S.L. and Z.T. explained the experimental results. Y.L. and S.L. prepared these figures. Y.L., S.L. and P.L. wrote this manuscript. R.M.H, G.Z., and B.Z. contributed to manuscript editing. R.M.H revised the manuscript. All Authors participated in reading and discussing the manuscript.

Funding

This work was supported by the Fujian Provincial Health Commission (Award NO. 2021TG0101) and the Qi Hang foundation of Fujian Medical University (Award NO. 2019QH1073).

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