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Immune Network Construction and Prognostic Evaluation of Checkpoint Genes in Endometrial Cancer Using STRING, MCODE,
Rafael Everton Assunção Ribeiro DA Costa1, Luiz Carlos Zeferino2, Júlio César Teixeira3
1Obstetrics and Gynecology, Faculty of Medical Sciences, State University of Campinas, Campinas, Brazil.
Background/Aim:
Endometrial cancer (EC) is an important health issue among women, with immunotherapy emerging as a promising option for advanced cases. Tumor-infiltrating lymphocytes (TILs) and immune checkpoints, including PDCD1, CD274, and PDCD1LG2, are increasingly recognized as prognostic markers. This study aimed to construct an in silico immune network and assess the prognostic impact of checkpoint genes in EC using STRING, MCODE, and GEPIA2.
Materials And Methods:
Retrospective analysis used TCGA-UCEC (The Cancer Genome Atlas - Uterine Corpus Endometrial Carcinoma) and GTEx datasets and reported immune-related genes. Genes were analyzed in STRING v12.0 (confidence ≥0.7; up to 10 neighbors per node) to generate a protein-protein interaction (PPI) network, exported to Cytoscape v3.10.2, and processed with MCODE to identify functional clusters. Hub genes were evaluated for expression and overall survival (OS) in GEPIA2 using median-based stratification and log-rank tests (p<0.05). Six immune signatures were assessed in TIMER2.0. PDCD1 and CD274 showed strong interactions with other immune effectors.
Results:
CD40 and LGALS9 were down- and upregulated, respectively, without affecting OS. Combined overexpression of CTLA4, PDCD1, TIGIT, CD8A, CD8B, GZMB, PRF1, TBX21, FOXP3, CXCL9, CD28, and ICOS correlated with improved OS, suggesting direct immune effects and enhanced responses to targeted therapies.
Conclusion:
This in silico immune network highlights checkpoint centered hubs and coordinated immune programs with prognostic relevance in endometrial cancer, providing a rationale for biomarker guided immunotherapy development and patient stratification. Validation in independent cohorts and correlation with clinicopathologic and treatment response data are needed to support clinical translation.
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