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.

Keywords

immune characteristics / lung adenocarcinoma / prognosis / tumor mutation burden / weighted gene co-expression network analysis

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RIGHTS & PERMISSIONS

2025 The Author(s). Precision Medical Sciences published by John Wiley & Sons Australia, Ltd on behalf of Nanjing Medical University Affiliated Cancer Hospital & Jiangsu Cancer Hospital.