Identification of Immune-Associated Hub Genes Related to the G2/M Checkpoint Pathway in Bladder Cancer Through Integrated Bioinformatics Analysis
Abstract
Objectives Bladder cancer is a highly heterogeneous malignancy characterized by complex immune interactions that influence disease progression and patient outcomes. Identifying immune-related molecular determinants with prognostic and therapeutic relevance is therefore critical. This study aimed to systematically investigate immune-related genes involved in bladder cancer and to identify key predictive molecular prognostic biomarkers, with particular emphasis on cell-cycle–associated immune mechanisms. Methods RNA expression profiles from bladder tumor and corresponding normal tissues were analyzed across five independent cohorts (GSE13507, GSE19915, GSE40355, GSE42089, and TCGA-Bladder Cancer). Common differentially expressed immune-related genes (DEIRGs) were identified using GEO2R and TCGA analyses. Functional enrichment and pathway analyses were performed using Metascape and DAVID, while hub genes were identified through protein–protein interaction networks using MCODE, CytoHubba, and ClueGO. Expression patterns, prognostic relevance, and immune associations were validated using UALCAN, the Human Protein Atlas, TIMER2.0, and PrognoScan databases. Results A total of 300 common DEIRGs were identified across datasets. Network analysis revealed 21 significant immune-related hub genes, several of which were consistently enriched in the G2M checkpoint pathway. Eleven hub genes were significantly associated with reduced overall survival in bladder cancer patients. Notably, six G2M checkpoint–related genes ( TOP2A, CCNA2, CDK1, BIRC5, PLK1, and E2F1 ) demonstrated strong prognostic relevance across multiple cohorts. Conclusions This comprehensive in silico analysis highlights the critical role of G2M checkpoint–associated immune-related genes in bladder cancer progression. These genes represent predictive molecular prognostic immune biomarkers and potential therapeutic targets, providing a foundation for the development of personalized immunotherapeutic strategies in bladder cancer.
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BibTeX
@article{kumari2026identification,
title = {{Identification of Immune-Associated Hub Genes Related to the G2/M Checkpoint Pathway in Bladder Cancer Through Integrated Bioinformatics Analysis}},
author = {Ranbala Kumari and Ashish Bhushan and Jasleen Kaur and Pradeep Kumar Nagar and Mishi Wasson and Usha Agrawal},
journal = {Cancer Informatics},
year = {2026},
month = oct,
doi = {10.1177/11769351261500680},
url = {https://doi.org/10.1177/11769351261500680},
note = {CC BY},
}
RIS
TY - JOUR
TI - Identification of Immune-Associated Hub Genes Related to the G2/M Checkpoint Pathway in Bladder Cancer Through Integrated Bioinformatics Analysis
AU - Ranbala Kumari
AU - Ashish Bhushan
AU - Jasleen Kaur
AU - Pradeep Kumar Nagar
AU - Mishi Wasson
AU - Usha Agrawal
PY - 2026
DA - 2026/10/23
JO - Cancer Informatics
DO - 10.1177/11769351261500680
UR - https://doi.org/10.1177/11769351261500680
AB - Objectives Bladder cancer is a highly heterogeneous malignancy characterized by complex immune interactions that influence disease progression and patient outcomes. Identifying immune-related molecular determinants with prognostic and therapeutic relevance is therefore critical. This study aimed to systematically investigate immune-related genes involved in bladder cancer and to identify key predictive molecular prognostic biomarkers, with particular emphasis on cell-cycle–associated immune mechanisms. Methods RNA expression profiles from bladder tumor and corresponding normal tissues were analyzed across five independent cohorts (GSE13507, GSE19915, GSE40355, GSE42089, and TCGA-Bladder Cancer). Common differentially expressed immune-related genes (DEIRGs) were identified using GEO2R and TCGA analyses. Functional enrichment and pathway analyses were performed using Metascape and DAVID, while hub genes were identified through protein–protein interaction networks using MCODE, CytoHubba, and ClueGO. Expression patterns, prognostic relevance, and immune associations were validated using UALCAN, the Human Protein Atlas, TIMER2.0, and PrognoScan databases. Results A total of 300 common DEIRGs were identified across datasets. Network analysis revealed 21 significant immune-related hub genes, several of which were consistently enriched in the G2M checkpoint pathway. Eleven hub genes were significantly associated with reduced overall survival in bladder cancer patients. Notably, six G2M checkpoint–related genes ( TOP2A, CCNA2, CDK1, BIRC5, PLK1, and E2F1 ) demonstrated strong prognostic relevance across multiple cohorts. Conclusions This comprehensive in silico analysis highlights the critical role of G2M checkpoint–associated immune-related genes in bladder cancer progression. These genes represent predictive molecular prognostic immune biomarkers and potential therapeutic targets, providing a foundation for the development of personalized immunotherapeutic strategies in bladder cancer.
SN - 1176-9351
ER -