A Combined Machine Learning and Weighted Gene Co-expression Network Analysis Strategy for Lung Adenocarcinoma Biomarker Discovery

Identifying molecular biomarkers that predict disease progression and therapeutic outcomes is essential for advancing precision oncology in LUAD. In this study, we applied an integrative approach combining machine learning and bioinformatics to identify survival-associated genes and develop a robust prognostic risk model.

Multiple machine learning algorithms – including Cox Proportional Hazards (CoxPH), TabNet, TabNetCoxPHAdd, and TabNetCoxPHMult – were used for gene selection, while Weighted Gene Co-expression Network Analysis (WGCNA) identified hub genes linked to patient survival. Genes detected by both methods were refined through CoxPH analysis, yielding 14 key prognostic genes: PDCD5, PSMB1, AHCY, CYC1, GOLT1B, COPS9, ATP5F1C, HPRT1, COX5B, PDCD10, SDHD, NDUFB5, RP53, and RACK1.