Unveiling diagnostic biomarkers and therapeutic targets in lung adenocarcinoma using bioinformatics and experimental validation.

ABSTRACT: Lung adenocarcinoma (LUAD) is a major challenge in oncology due to its complex molecular structure and generally poor prognosis. The aim of this study was to find diagnostic markers and therapeutic targets for LUAD by integrating differential gene expression analysis, weighted gene co-expression network analysis (WGCNA), and machine learning methods. Differentially expressed genes (DEGs) were identified through the analysis of gene expression data from the Gene Expression Omnibus (GEO) database. Ten of the gene co-expression modules constructed by WGCNA were identified, with the red module having the most significant correlation with clinical features. In addition, a machine learning model constructed based on Stepglm[backward] with the random forest algorithm achieved the highest C-index

SUBMITTER: Wu S 

PROVIDER: S-EPMC12218858 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature