Comprehensive profiling of T-cell exhaustion signatures and establishment of a prognostic model in lung adenocarcinoma through integrated RNA-sequencing analysis.

Abstract

BackgroundT-cell exhaustion (TEX) in the tumor microenvironment causes immunotherapy resistance and poor prognosis.ObjectiveWe used bioinformatics to identify crucial TEX genes associated with the molecular classification and risk stratification of lung adenocarcinoma (LUAD).MethodsBulk RNA sequencing data of patients with LUAD were acquired from open sources. LUAD samples exhibited abnormal TEX gene expression, compared with normal samples. TEX gene-based prognostic signature was established and validated in both TCGA and GSE50081 datasets. Immune correlation and risk group-related functional analyses were also performed.ResultsEight optimized TEX genes were identified using the LASSO algorithm: ERG, BTK, IKZF3, DCC, EML4, MET, LATS2, and LOX. Several crucial Kyoto encyclopedia of genes and genomes (KEGG) pathways were identified, such as T-cell receptor signaling, toll-like receptor signaling, leukocytes trans-endothelial migration, Fcγ R-mediated phagocytosis, and GnRH signaling. Eight TEX gene-based risk score models were established and validated. Patients with high-risk scores had worse prognosis (P < 0.001). A nomogram model comprising three independent clinical factors showed good predictive efficacy for survival rate in patients with LUAD. Correlation analysis revealed that the TEX signature significantly correlated with immune cell infiltration, tumor purity, stromal cells, estimate, and immunophenotype score.ConclusionTEX-derived risk score is a promising and effective prognostic factor that is closely correlated with the immune microenvironment and estimated score. TEX signature may be a useful clinical diagnostic tool for evaluating pre-immune efficacy in patients with LUAD.