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Machine learning-based identification of hub genes and prognostic biomarkers in prostate cancer
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DOI：10.3389/fgene.2026.1877595.png)
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Abstract
En 中文
BackgroundProstate cancer (PCa) is the second most prevalent malignancy in men worldwide; and accurate stratification of biochemical recurrence (BCR) risk remains challenging using conventional clinicopathological parameters alone. Identification of robust molecular biomarkers and integrated prognostic models is therefore of high clinical priority.MethodsRNA-seq count data and clinical annotations for 554 TCGA-PRAD samples were obtained and normalized to log2(CPM+1). Weighted gene co-expression network analysis (WGCNA) identified co-expression modules correlated with Gleason score; PSA; and pathologic T stage. Protein-protein interaction (PPI) network analysis with CytoHubba topological scoring defined consensus hub genes. Four machine learning algorithms - LASSO Cox regression; random forest; SVM; and XGBoost - were applied to construct and validate a prognostic risk model. Immune cell infiltration was quantified and a prognostic nomogram was constructed and evaluated by decision curve analysis. Hub gene expression was experimentally validated by qRT-PCR and ELISA in prostate cancer and normal prostatic epithelial cell lines.ResultsFive hub genes - EZH2; CDK1; AURKA; TOP2A; and CCNB1 - were identified within the turquoise WGCNA module; which showed the strongest correlations with Gleason score (r = 0.78); PSA (r = 0.68); and pathologic T stage (r = 0.62). LASSO Cox regression and random forest consensus selected EZH2; CDK1; and AURKA for a three-gene risk score (Risk Score = 0.312xEZH2 + 0.285xCDK1 + 0.241xAURKA). High-risk patients demonstrated markedly inferior BCR-free survival (HR = 3.21; 95% CI: 2.05–5.03; log-rank P < 0.0001); with time-dependent AUCs of 0.821; 0.842; and 0.836 at 1; 3; and 5 years; respectively. Multivariate Cox regression confirmed the risk score as an independent prognostic factor (HR = 2.87; P < 0.001). A nomogram integrating the risk score with clinical parameters showed superior net benefit by decision curve analysis. Hub-high tumors exhibited reduced CD8+ T cell infiltration; elevated M2 macrophage abundance; and upregulated immune checkpoints (PD-L1; CTLA4; TIM-3; LAG3). All hub genes were confirmed overexpressed at both mRNA and protein levels in PCa cell lines by qRT-PCR and ELISA.ConclusionEZH2; CDK1; and AURKA constitute an internally validated prognostic risk signature in PCa that links cell cycle dysregulation to an immunosuppressive tumor microenvironment. This signature provides clinically actionable risk stratification and highlights candidate therapeutic targets in prostate cancer.
Keywords:
prostate cancer
machine learning
immune infiltration
EZH2
prognostic biomarker
CDK1
LASSO Cox regression
AURKA
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