Evidence map›Paper›PMID 42416056›Full record

ArticleFrontiers in immunology2026

Identification and validation of biomarkers related to mismatch repair for prognosis prediction in glioma.

Jia Feng, Yuankai Si, Long Han, Yilan Huang, Longyang Jiang

Abstract read
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Article in Frontiers in immunology, 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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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Jia Feng *Department of Pharmacy, The Affiliated Hospital of Southwest Medical University, Luzhou, China.
Yuankai Si *Department of Pharmacy, The Affiliated Hospital of Southwest Medical University, Luzhou, China.
Long HanDepartment of Pharmacy, Qingdao University Affiliated Women and Children's Hospital, Qingdao, China.
Yilan HuangDepartment of Pharmacy, The Affiliated Hospital of Southwest Medical University, Luzhou, China.
Longyang JiangDepartment of Pharmacy, The Affiliated Hospital of Southwest Medical University, Luzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Gliomas are aggressive brain tumors with a dismal prognosis, and their development, progression, and responsiveness to treatment are all impacted by mismatch repair (MMR) deficiency. This work aimed to construct a predictive risk model based on MMR-related genes and confirm its clinical applicability, with a focus on identifying novel therapeutic targets. Methods: Clinical and mRNA expression data from The Cancer Genome Atlas (TCGA) and The Chinese Glioma Genome Atlas (CGGA) glioma patients were analyzed. MMR-related genes were sourced from the Molecular Signatures Database (MSigDB). A risk score model was created using multivariate Cox regression and LASSO analysis. Patients were categorized into high- and low-risk groups based on the median risk score. The model's performance was assessed using ROC curves, AUC, and Kaplan-Meier survival analysis. Immune cell infiltration was quantified using "CIBERSORT" and "QUANTISEQ". Immunotherapy potential was evaluated via TMB, TME, and TIDE scores. The half maximal inhibitory concentration (IC Results: An eight-MMR-related gene prognostic model (HMGB1, MCM8, MUTYH, PMS1, RNASEH2B, RNASEH2C, RPA3, TP73) was constructed. The risk score was an independent prognostic factor, with high-risk patients showing significantly poorer overall survival. The validity of this model has been validated in the CGGA dataset. Significant differences in immune infiltration, TME, and TMB scores were observed between risk groups. Drug sensitivity analysis revealed distinct IC Conclusion: This study establishes a robust MMR-related prognostic model for glioma that effectively stratifies patients, predicts survival, and reflects distinct immune microenvironments. Critically, we identify MCM8 as a novel and actionable biomarker that modulates TMZ sensitivity, offering a promising avenue for personalized treatment in glioma patients.

Indexed as

Biomarkers, TumorBrain NeoplasmsDNA Mismatch RepairGliomaCell Line, TumorFemaleGene Expression Regulation, NeoplasticHumansKaplan-Meier EstimatePrognosisTemozolomideTumor MicroenvironmentBiomarkers, TumorTemozolomidebioinformaticsdrug sensitivitygliomamismatch repair-related genesprognostic model

Identifiers

PMID42416056
PMCPMC13337645

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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.