GSTP1 as a senescence-associated diagnostic candidate in prostate cancer: integrative bioinformatics and functional validation

Introduction Prostate cancer (PCa) is a common malignancy of the male genitourinary system with high metastatic potential and poor clinical outcomes, for which effective biomarkers and therapeutic targets remain limited. Methods This study employed a multi-omics approach, integrating transcriptome profiles, age-related gene screening, and machine learning techniques, to analyze four GEO datasets, in order to identify key diagnostic biomarkers for prostate cancer (PCa). Results Differentially expressed aging-related genes were identified, with enrichment analyses revealing significant involvement in PCa. Using machine learning algorithms, a robust diagnostic model centered on GSTP1 was developed, demonstrating high predictive performance. GSTP1 depletion drove multiple oncogenic phenotypes, including enhanced proliferation, migration, invasion, and the inhibition of cellular aging and DNA damage, suggesting that GSTP1 may promote cellular senescence while restraining malignant progression. Furthermore, its expression correlated with distinct immunocyte profiles, highlighting a role in shaping the PCa immune landscape. Discussion Our findings confirm that GSTP1 functions as an aging-associated stress response gene, offering new perspectives on prostate cancer pathogenesis and showing promise for advancing therapeutic strategies that target both aging and immune associations. This study provides a comprehensive framework for leveraging aging biomarkers in precision oncology and highlights GSTP1 as a promising candidate for clinical translation in PCa management.