Molecular Subtyping Based on EGFR Mutation-Associated Genes and the Prognostic Role of TRAF2 in Lung Adenocarcinoma

This study is aimed at systematically identifying key genes associated with EGFR mutations and developing a molecular classification model in lung adenocarcinoma (LUAD) using integrative bioinformatics approaches. Multi-omics datasets derived from cBioPortal and The Cancer Genome Atlas (TCGA) LUAD cohort were interrogated to identify genes correlated with EGFR mutational status. A core set of 18 genes exhibiting significant associations with both EGFR mutation frequency and patient prognosis was identified. Based on this gene signature, a two-cluster molecular subtype stratification was established. These subtypes demonstrated statistically significant divergence in overall survival, immune cell infiltration profiles, and predicted responsiveness to immunotherapeutic intervention. Further analyses, including machine learning algorithms, multivariate Cox regression, and molecular docking, identified TRAF2 as a key prognostic gene closely associated with EGFR. In vitro experiments demonstrated that TRAF2 promotes proliferation, migration, and invasion of LUAD cells. Additional analyses suggested that TRAF2 may contribute to tumor progression through epigenetic regulation and associated signaling pathways. Collectively, these findings provide novel insights into the molecular heterogeneity of EGFR-mutant LUAD and offer potential targets for precision prognostic assessment and combination therapeutic strategies.