Exploring relationships between tumor mutation burden with immunity and prognosis in lung adenocarcinoma based on weighted gene co‐expression network analysis

Abstract This study explores the association between tumor mutation burden (TMB) and immunity, prognosis in lung adenocarcinoma (LUAD), positing TMB as a predictive biomarker for immune checkpoint inhibitor therapy. LUAD mutation and clinical data were sourced from the TCGA database, with mRNA‐seq data from UCSC Xena. TMB calculation divided samples into high and low groups, analyzing survival, immune, and stromal scores via Kaplan–Meier and ESTIMATE algorithms. Weighted gene co‐expression network analysis (WGCNA) identified immune‐related module genes, intersecting with TMB‐differentiated genes to distinguish LUAD subtypes. With an optimal TMB cutoff of 6.46, high‐TMB patients demonstrated superior survival. Significant inverse relationships were found between TMB and both immune/stromal scores. WGCNA highlighted 3676 genes in 4 modules, with 80 hub genes identified. These defined two LUAD subtypes: one with worse prognosis, higher mutation rates, and advanced stage distribution. TMB significantly correlates with prognosis and immune contexture in LUAD. The identification of subtype‐specific hub genes offers a nuanced understanding of LUAD heterogeneity, supporting TMB's utility in predicting immunotherapy response and stratifying patient prognosis.