Integrative analysis identifies candidate biomarkers for bladder cancer: evidence from genomic and clinical validation.

BACKGROUND

Bladder cancer (BCa) is a highly prevalent urological malignancy and one of the most frequently occurring cancers worldwide, necessitating the development of diagnostic and therapeutic biomarkers. This study aimed to explore candidate genes that may be involved in the carcinogenesis of BCa.

MATERIALS AND

METHODS

BCa gene expression data from the Gene Expression Omnibus (GEO) database were analyzed. Both differential expression analysis and weighted gene co-expression network analysis (WGCNA) were performed on datasets (GSE236932, GSE133624) to identify the key modules and hub genes. We subsequently extracted genes from the top modules positively correlated with tumor traits identified via WGCNA and intersected them with differentially expressed genes (DEGs) in each dataset. Finally, the overlapping genes were screened for their therapeutic potential as biomarkers in bladder cancer, and quantitative Real-Time PCR (qRT-PCR) was executed to validate the expression patterns of the most promising candidate genes in clinical tissue samples.

RESULTS

Integrative analysis revealed a set of hub genes potentially involved in bladder cancer, including PSMG3, ESRP1, GRHL2, MAL2, CDH1, AP1M2, PAFAH1B3, PRKCZ, MAPK13, and RAB25. Among these, PAFAH1B3 and PSMG3 emerged as notable novel candidates. qRT-PCR validation further underscored the significant overexpression of these genes in BCa samples, with approximately two-fold changes relative to normal tissues.

CONCLUSIONS

This study suggests that PAFAH1B3 and PSMG3 may serve as valuable biomarkers and potential therapeutic targets in bladder cancer. However, further investigations are needed to establish their biological function and clinical relevance.