Overexpression of CDC20 Confer a Poorer Prognosis in Bladder Cancer Identified by Gene Co-Expression Network Analysis.

Background/

Objectives

Bladder cancer (BCa) ranks as the tenth most prevalent malignancy worldwide, characterized by high morbidity and mortality rates. Despite advancements in understanding its pathogenesis, the identification of robust prognostic biomarkers remains critical for improving clinical outcomes. This study aims to identify and validate novel prognostic markers for BCa through integrated bioinformatics and experimental approaches.

Methods

Gene expression data and clinical information were obtained from the GEO (GSE13507) and TCGA databases. Differential gene expression analysis and weighted gene co-expression network analysis (WGCNA) were employed to identify overlapping genes. Functional enrichment analysis was performed to explore biological functions, followed by protein-protein interaction (PPI) network construction and survival analysis. Key candidate genes were screened using the CytoHubba plugin in Cytoscape. CDC20 expression was validated through RT-qPCR, and its functional role in BCa cells was assessed in vitro.

Results

Eight candidate hub genes (TROAP, TPX2, TOP2A, KIF2C, AURKA, CDC20, PRC1, and AURKB) were identified. Survival analysis revealed that high CDC20 expression was significantly associated with decreased overall survival in BCa patients. Mechanistic investigations demonstrated that CDC20 promotes tumor invasion and growth by modulating mitosis and cell cycle progression, while also influencing the tumor microenvironment through immune cell regulation. Experimental validation confirmed the tumor-promoting role of CDC20 in BCa cells.

Conclusions

This study identifies CDC20 as a key prognostic biomarker for bladder cancer, providing novel insights for early diagnosis, clinical treatment, and prognosis assessment. The findings highlight the potential of CDC20 as a therapeutic target and underscore the value of integrated bioinformatics and experimental validation in biomarker discovery.