Identification and external validation of a prognostic signature based on MAPK-related genes to evaluate survival prognosis and treatment efficacy in lung adenocarcinoma.

BACKGROUND: The mitogen-activated protein kinase (MAPK) pathway plays a pivotal role in tumorigenesis and immune regulation. However, its prognostic significance in lung adenocarcinoma (LUAD) remains poorly defined. This study aimed to construct a robust MAPK-related gene (MRG) signature by integrating multi-omics data to enhance risk stratification and therapeutic guidance in LUAD. METHODS: Differentially expressed MRGs were identified from TCGA-LUAD transcriptomic data and prioritized through Mendelian randomization (MR) analysis. A prognostic model was constructed using the random survival forest (RSF) algorithm and validated across three independent Gene Expression Omnibus (GEO) cohorts. Additional analyses were developed including pathway enrichment, drug sensitivity prediction, reverse transcription quantitative PCR (RT-qPCR) validation, and single-cell RNA sequencing (scRNA-seq) to uncover its mechanistic basis and clinical value. RESULTS: The MRG-based model effectively stratified patients into high-risk (HRG) and low-risk groups (LRG) with significant differences in overall survival (P < 0.001). The nomogram-derived risk score outperformed clinical factors in predicting outcomes, reflecting strong prognostic capability of the model. HRG exhibited elevated tumor mutational burden (TMB), enrichment of PI3K-Akt signaling, both of which may be associated with its poorer prognosis. Drug sensitivity profiling suggested that LRGs were more responsive to PI3K/mTOR inhibitors, whereas HRGs favored tyrosine kinase inhibitors. scRNA-seq analysis revealed that MRGs were mainly enriched in endothelial cell populations, implicating their role in immune modulation and angiogenesis. CONCLUSIONS: This integrative multi-omics-based prognostic model provides robust predictive power and novel biological insights, serving as a practical tool for personalized prognosis evaluation and targeted therapeutic decision-making in LUAD.