Bioinformatics-Based Discovery of Key Genes for Prostate Cancer Diagnosis

Objective: Prostate cancer is a heterogeneous malignancy that requires reliable molecular biomarkers for improved diagnosis and clinical classification. This study intended to uncover hub genes with diagnostic value through an integrative bioinformatics method.Methods: GSE46602 and GSE200879 profiles underwent bioinformatic evaluation to extract a signature of genes significantly dysregulated in prostate cancer relative to normal tissue. Prostate cancer-associated genes were also retrieved from TCGA (The Cancer Genome Atlas) together with GTEx (Genotype-Tissue Expression) via GEPIA (Gene Expression Profiling Interactive Analysis) and from GeneShot. Overlapping genes were determined through comparative dataset integration. Functional categorization of the genes was performed within the Gene Ontology environment, whereas pathway-level associations were inferred through Kyoto Encyclopedia of Genes and Genomes (KEGG) resources. Furthermore, network-based analyses were applied to prioritize key genes according to centrality measures. Finally, the expression levels of the prioritized genes were validated across the GEPIA2 and UALCAN web-based platforms.Results: Ten hub genes (SNAI2, GSTP1, GPX3, GATA3, KRT5, CLU, CAV1, CCK, TWIST1, and GSTM1) were consistently identified across datasets. Most of these genes exhibited reduced expression in prostate cancer tissues, whereas TWIST1 levels were elevated. Enrichment analyses indicated associations with transcriptional regulation, epithelial differentiation, oxidative stress response, and cancer-related pathways. Validation analyses confirmed consistent differential expression across Gleason score groups and nodal metastasis status.Conclusion: This integrative analysis identified a robust set of hub genes with dysregulation in prostate cancer, highlighting their potential as molecular markers for distinguishing tumor from normal prostate tissue.