2026-10-14T08:39:06Z https://api.figshare.com/v2/oai

oai:figshare.com:article/34018651 2026-09-28T23:40:03Z category_7 category_13 category_14 category_734 category_931 category_811 category_64 portal_87 item_type_6 month_year_09_2026

A Combined Machine Learning and Weighted Gene Co-expression Network Analysis Strategy for Lung Adenocarcinoma Biomarker Discovery Cheng-Hong Yang (14933) Guan-Cheng Lin (18601082) Huang-Chih Chang (598326) Hung-Yi Chuang (65132) Medicine Genetics Molecular Biology Biological Sciences not elsewhere classified Information Systems not elsewhere classified Mathematical Sciences not elsewhere classified Cancer variable treatment responses related deaths worldwide predict disease progression personalized treatment strategies identifying molecular biomarkers functional interactions among combined machine learning weighted gene co nsclc ), remains expression network analysis coxph ), tabnet disease .</ p advancing precision oncology precision oncology coxph analysis gene selection therapeutic outcomes tabnetcoxphmult – string database patient survival lung adenocarcinoma luad ), leading cause largely due identify survival early detection common subtype <p>Lung adenocarcinoma (LUAD), the most common subtype of non-small cell lung cancer (NSCLC), remains a leading cause of cancer-related deaths worldwide, largely due to difficulties in early detection and variable treatment responses. 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. Functional interactions among these genes were explored through a protein–protein interaction network constructed using the STRING database. The resulting risk prediction model showed high predictive accuracy and reproducibility when validated on independent Gene Expression Omnibus (GEO) datasets. These results identify potential prognostic biomarkers for LUAD and provide a validated framework for personalized treatment strategies, supporting the advancement of precision oncology in this disease.</p> 2026-09-28T23:40:03Z Text Journal contribution 10.6084/m9.figshare.34018651.v1 https://figshare.com/articles/journal_contribution/A_Combined_Machine_Learning_and_Weighted_Gene_Co-expression_Network_Analysis_Strategy_for_Lung_Adenocarcinoma_Biomarker_Discovery/34018651 CC BY 4.0