Evidence map›Paper›PMID 41981105›Full record

ArticleScientific reports2026

Exploration of m7G-related immune genes and construction of a prognostic risk model in gastric cancer.

Nan Li, Jie Yin, Yue Zhao, Yao Fan, Jinmei Wei, Zhankun Gao, Ruixing Zhang

Abstract read
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Nan LiDepartment of Gastroenterology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei Province, China.
Jie YinDepartment of Gastroenterology, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei Province, China.
Yue ZhaoDepartment of Gastroenterology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei Province, China.
Yao FanDepartment of Gastroenterology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei Province, China.
Jinmei WeiDepartment of Gastroenterology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei Province, China.
Zhankun GaoDepartment of Gastroenterology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei Province, China.
Ruixing ZhangDepartment of Gastroenterology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei Province, China. zrx@medmail.com.cn.

Funding

Partial financial support was received from Hebei Natural Science Foundation H2025206271
6 · The paper itself

Abstract

Gastric cancer (GC) is a lethal digestive malignancy with poor outcomes. Although N7-Methylguanosine (m7G) modification and immune-related genes (IRGs) individually affect GC prognosis and the tumor microenvironment, their mechanistic crosstalk remains unclear. Differentially expressed genes (DEGs) in GC were identified and used alongside m7G-related genes (m7G-RGs) to perform consensus clustering, defining molecular subtypes and yielding additional DEGs. Intersection of these gene sets identified m7G-related immune genes. Prognostic genes were selected via Least Absolute Shrinkage and Selection Operator (LASSO), and Cox regression analysis to construct a validated prognostic risk model. Associations between clinicopathological features and risk scores were assessed, identifying independent prognostic factors and enabling nomogram development. The risk-stratified groups underwent gene set enrichment analysis (GSEA), immune infiltration, tumor immune dysfunction and exclusion (TIDE), tumor purity, drug sensitivity, and tumor mutation burden (TMB) analyses. Using GC and normal samples, this study identified 4,458 DEGs. Intersection with subtype-specific DEGs (2,098) and immune genes (4,172) yielded 193 m7G-associated immune genes. Six prognostic genes (ELANE, ASCL2, APOA1, GRP, CD36, MUC15) were used to construct a prognostic risk model that stratified patients into high- and low-risk groups with significant survival differences and strong predictive accuracy. Age, stage, and risk score were independent prognostic factors, and a nomogram was developed. High-risk patients showed enrichment in tumor-promoting pathways (e.g., calcium signaling), immunosuppressive microenvironments, higher stromal scores, and TP53 mutations. Low-risk patients had activated DNA repair pathways, lower TIDE scores, higher immune infiltration, and TTN mutations. Drug sensitivity analysis identified 130 compounds with differential responses between groups (p < 0.05). This study developed a robust prognostic risk model based on six prognostic genes, which effectively reveals molecular mechanisms and immune features of GC, offering a foundation for individualized therapy.

Indexed as

Biomarkers, TumorGuanosineStomach NeoplasmsFemaleGene Expression ProfilingGene Expression Regulation, NeoplasticHumansMaleNomogramsPrognosisTumor MicroenvironmentBiomarkers, TumorGuanosineGastric cancerImmune-related genesm7-methylguanosinePrognostic risk modelTumor microenvironment

Identifiers

PMID41981105
PMCPMC13237151

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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.