Evidence map›Paper›PMID 41217578›Full record

ArticleClinical and experimental medicine2025

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

Zijian Hu, Yajie Zhou, Lei Xie, Wenxiong Zhang, Haiwei Rao

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Article in Clinical and experimental medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Zijian HuDepartment of Thoracic Surgery, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, No.1 Minde Road, Nanchang, 330006, China.
Yajie ZhouDepartment of Thoracic Surgery, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, No.1 Minde Road, Nanchang, 330006, China.
Lei XieDepartment of Thoracic Surgery, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, No.1 Minde Road, Nanchang, 330006, China.
Wenxiong ZhangDepartment of Thoracic Surgery, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, No.1 Minde Road, Nanchang, 330006, China. Ndefy01261@ncu.edu.cn.
Haiwei RaoDepartment of Critical Care Medicine, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, No.1 Minde Road, Nanchang, 330006, China. Ndefy12418@ncu.edu.cn.

Funding

Natural Science Foundation of Jiangxi Province 20212BAB206050
6 · The paper itself

Abstract

backgroundThe 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.

methodsDifferentially 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.

resultsThe 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.

conclusionsThis 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.

Indexed as

Adenocarcinoma of LungLung NeoplasmsMitogen-Activated Protein KinasesBiomarkers, TumorFemaleGene Expression ProfilingGene Expression Regulation, NeoplasticHumansMaleMiddle AgedNomogramsPrognosisTranscriptomeBiomarkers, TumorMitogen-Activated Protein KinasesLung adenocarcinomaMitogen-activated protein kinaseMulti omicsPrognostic modelSingle-cell RNA sequencing

Identifiers

PMID41217578
PMCPMC12605378

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