Abstract
OBJECTIVES: Prostate cancer (PCa) is a prevalent malignancy in males, triggered by multiple factors. This study aimed to identify PCa-specific key genes with clinical significance and clarify their roles in PCa progression. METHODS: To screen PCa-specific key genes, a comprehensive analytical strategy was adopted by integrating weighted gene co-expression network analysis (WGCNA) for mining highly correlated important genes, Cox regression analysis for evaluating clinical relevance, and multiple machine learning techniques. Functional validation experiments were further conducted, including CCK-8 assay to assess cell proliferation, transwell assay, and wound healing assay to detect cell invasion and migration abilities after ANO5 overexpression in PCa cells. In addition, a model was constructed using machine learning to systematically clarify the role of ANO family genes in the occurrence of PCa. RESULTS: Anoctamin 5 (ANO5) was identified as a PCa-specific key gene through the integrated analytical approach. Clinical data analysis revealed that higher ANO5 expression was significantly correlated with favorable clinical status and longer survival time of PCa patients. Functional experiments confirmed this finding: the overexpression of ANO5 in PCa cells has an inhibitory effect on the behavior of tumor cells. Transwell and wound healing experiments further confirmed that ANO5 can inhibit the migration of PCa cells. CONCLUSION: ANO5 is a PCa-specific key gene that correlates with favorable clinical outcomes and regulates PCa cell invasion, suggesting its potential as a prognostic biomarker and therapeutic target. In comparison, the systematic exploration of ANO family genes enriches the understanding of PCa oncogenesis mechanisms.