Evidence map›Paper›PMID 40405133›Full record

ArticleBMC pulmonary medicine2025

Characteristics of folic acid metabolism-related genes unveil prognosis and treatment strategy in lung adenocarcinoma.

Yanting Dong, Xiaoyan Wang, Chuanchuan Dong, Peiqi Li, Zhuola Liu, Xinrui Tian

Abstract read
In one paragraph

Article in BMC pulmonary medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

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5citing papers in PubMed
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1 · What the graph read from it

What it found

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3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Review
  2. Identification ofEndocrine, metabolic & immune disorders drug targets · 2026
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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Yanting DongDepartment of Respiratory and Critical Care Medicine, The Second Hospital of Shanxi Medical University, Taiyuan, China.
Xiaoyan WangBeijing Health Vocational College, Beijing, China.
Chuanchuan DongClinical Medicine, The Second Hospital of Shanxi Medical University, Taiyuan, China.
Peiqi LiClinical Medicine, The Second Hospital of Shanxi Medical University, Taiyuan, China.
Zhuola LiuDepartment of Respiratory and Critical Care Medicine, The Second Hospital of Shanxi Medical University, Taiyuan, China.
Xinrui TianDepartment of Geratology, The Second Hospital of Shanxi Medical University, Taiyuan, China. tianxr@126.com.

Funding

Nature Science Foundation of Shanxi Province Grant NO. 202203021211029Science Foundation for Doctoral Research of Shanxi Medical University Grant NO. BS03201633
6 · The paper itself

Abstract

backgroundLung adenocarcinoma (LUAD) is the most common subtype of lung cancer. Folic acid metabolism-related genes (FAMGs) have received increased attention because of their distinct role in DNA synthesis and repair. Nevertheless, the function of FAMGs in LUAD remains ambiguous.

methodsLUAD transcriptome data from GEO and TCGA were analyzed. Patients were classified into two clusters based on gene expression levels, revealing distinct overall survival (OS) outcomes. Common differentially expressed genes (DEGs) were identified between LUAD and normal tissues, as well as between the two clusters. A prognostic risk model was established using Cox regression analysis to predict outcomes of LUAD patients and was validated with Kaplan-Meier and ROC curve analysis. Clinical correlations and enrichment analyses were carried out to explore the functions of DEGs and their associations with clinical characteristics of LUAD patients. The tumor microenvironment and drug sensitivity were evaluated between two risk subgroups. Moreover, expression levels of prognostic genes were validated across datasets using the Wilcoxon-test.

resultsThe study identified seventy-seven common DEGs and nine prognostic genes (ANLN, PLK1, DLGAP5, PRC1, CYP4B1, MKI67, KIF23, BIRC5, TK1). The risk model could effectively predict the prognosis of LUAD patients. Clinical correlation analysis revealed that age, pathologic-T, pathologic-N, and tumor stage were significantly correlated with the risk score. Enrichment analysis showed that DEGs between the two risk subgroups were predominantly enriched in cell cycle and cellular senescence pathways. Differences in immune cell infiltration and immunotherapy markers were markedly noted between the two risk subgroups. Drug sensitivity analysis disclosed significantly diverse responses to sixty-eight drugs between the two risk subgroups. Consistent expression tendencies of prognostic genes were observed across datasets.

conclusionThe prognostic model based on FAMGs demonstrates considerable potential for guiding diagnosis and clinical management of LUAD patients.

Indexed as

Adenocarcinoma of LungFolic AcidLung NeoplasmsAgedBiomarkers, TumorFemaleGene Expression ProfilingGene Expression Regulation, NeoplasticHumansKaplan-Meier EstimateMaleMiddle AgedPrognosisTranscriptomeTumor MicroenvironmentBiomarkers, TumorFolic AcidFolic acidLung adenocarcinomaOverall survivalPrognosisRisk score

Identifiers

PMID40405133
PMCPMC12101037

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.