Evidence map›Paper›PMID 41799759›Full record

ArticleInternational journal of medical sciences2026

Identification of Key Genes via Integrated Multi-Omics and Machine Learning Uncovers Tumor Biological Features and Prognostic Biomarkers in Uterine Leiomyosarcoma.

Wei Lu, Susu Jiang, Qiran Sun, Yating Huang, Ying Yang, Xiaoqin Wang, Liwen Zhang, Yi Guo, Rujun Chen

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Article in International journal of medical sciences, 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

9 authors.

Wei LuDepartment of Gynecology and Obstetrics, Shanghai East Hospital, Tongji University, Shanghai, China.
Susu JiangDepartment of Gynecology and Obstetrics, Shanghai Fifth People's Hospital, Fudan University, Shanghai, China.
Qiran SunDepartment of Gynecology and Obstetrics, Shanghai Fifth People's Hospital, Fudan University, Shanghai, China.
Yating HuangDepartment of Gynecology and Obstetrics, Shanghai Fifth People's Hospital, Fudan University, Shanghai, China.
Ying YangDepartment of Gynecology and Obstetrics, Shanghai Fifth People's Hospital, Fudan University, Shanghai, China.
Xiaoqin WangDepartment of Gynecology and Obstetrics, Shanghai Fifth People's Hospital, Fudan University, Shanghai, China.
Liwen ZhangDepartment of Gynecology and Obstetrics, Shanghai Fifth People's Hospital, Fudan University, Shanghai, China.
Yi GuoDepartment of Gynecology and Obstetrics, Shanghai East Hospital, Tongji University, Shanghai, China.
Rujun ChenDepartment of Gynecology and Obstetrics, Shanghai Fifth People's Hospital, Fudan University, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Uterine leiomyosarcoma (ULMS) is a rare, aggressive uterine malignancy with high misdiagnosis rates, poor prognosis, and limited molecular biomarkers. Its pathogenesis, links between specific genes and the tumor immune microenvironment (TIME), and applications of machine learning (ML) and Mendelian randomization (MR) remain understudied. Methods: Multi-cohort data (4 GEO datasets, TCGA-SARC, single-cell sequencing) were integrated. Differentially expressed genes (DEGs) and WGCNA-derived key modules identified "InteGenes". 113 ML algorithms were compared to build a diagnostic model (top: GBM, core genes = "Mgenes"). CIBERSORT analyzed TIME; MR explored Mgenes-ULMS causal links. Results: 96 InteGenes enriched in cell cycle/p53/DNA repair pathways. The GBM model had training AUC = 1 and validation accuracy 92.3-100%; 36 Mgenes (e.g., TRIP13, AUC = 0.972) showed diagnostic value. Mgenes correlated with TIME (upregulated Mgenes ↔ M2 TAMs/Tregs; downregulated ↔ effector cells). MR found no genetic causality between Mgenes and ULMS. Conclusion: InteGenes reflect ULMS pathogenesis; the GBM model and Mgenes are promising diagnostic tools. Mgenes modulate ULMS's TIME, offering immunotherapeutic targets. This study advances ULMS molecular/immune understanding for translational research.

Indexed as

Biomarkers, TumorLeiomyosarcomaMachine LearningUterine NeoplasmsFemaleGene Expression ProfilingGene Expression Regulation, NeoplasticHumansMultiomicsPrognosisTumor MicroenvironmentBiomarkers, Tumordiagnostic modelmachine learningsingle-cell sequencingtumor immune microenvironmentuterine leiomyosarcoma

Identifiers

PMID41799759
PMCPMC12964573

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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.