Evidence map›Paper›PMID 40340863›Full record

ArticleJournal of translational medicine2025

ProgModule: A novel computational framework to identify mutation driver modules for predicting cancer prognosis and immunotherapy response.

Xiangmei Li, Bingyue Pan, Xilong Zhao, Yinchun Su, Jiyin Lai, Siyuan Li, Yalan He, Jiashuo Wu, Junwei Han

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Article in Journal of translational medicine, 2025. 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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5 · Who and what money

Authors and funding

9 authors.

Xiangmei Li *College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
Bingyue Pan *College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
Xilong Zhao *College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
Yinchun SuDepartment of Neurobiology, Harbin Medical University, Harbin, 150081, China.
Jiyin LaiCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
Siyuan LiCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
Yalan HeCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
Jiashuo WuCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
Junwei HanCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China. hanjunwei@ems.hrbmu.edu.cn.ORCID 0000-0002-3276-0819

Funding

National Natural Science Foundation of China 62072145National Natural Science Foundation of China 62372143Natural Science Foundation of Heilongjiang Province LH2019C042
6 · The paper itself

Abstract

backgroundCancer originates from dysregulated cell proliferation driven by driver gene mutations. Despite numerous algorithms developed to identify genomic mutational signatures, they often suffer from high computational complexity and limited clinical applicability.

methodsHere, we presented ProgModule, an advanced computational framework designed to identify mutation driver modules for cancer prognosis and immunotherapy response prediction. In ProgModule, we introduced the Prognosis-Related Mutually Exclusive Mutation (PRMEM) score, which optimizes the balance between exclusive mutation coverage and the incorporation of mutation combination mechanisms critical for cancer prognosis.

resultsApplying to BLCA and HNSC cohorts, ProgModule successfully identified driver modules that stratify patients into distinct prognostic subgroups, and the combination of these modules could serve as an effective prognostic biomarker. Extending our method to diverse cancers, ProgModule presented robust prognostic performance and stability across model parameters, including stopping criteria and network topology. Moreover, our analysis suggested that driver modules can predict immunotherapeutic benefit more effectively than existing signatures. Further analyses based on published CRISPR data indicated that genes within these modules may serve as potential therapeutic targets.

conclusionsAltogether, ProgModule emerges as a powerful tool for identifying mutation driver modules as prognostic and immunotherapy response biomarkers, and genes within these modules may be used as potential therapeutic targets for cancer, offering new insights into precision oncology.

Indexed as

Computational BiologyImmunotherapyMutationNeoplasmsAlgorithmsHumansPrognosisTreatment OutcomeDriver moduleImmune checkpoint inhibitorsMutually exclusive mutationPrognostic biomarkersTherapeutic targets

Identifiers

PMID40340863
PMCPMC12063272

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