OncoImmune machine-learning model predicts immune response and prognosis in leiomyosarcoma.

Jingrong Deng, Changfa Shu, Dong Wang, Richard Nimbona, Xingping Zhao, Dabao Xu

Abstract read

In one paragraph

Article in Biomolecules & biomedicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed

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1 · What the graph read from it

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1 citing paper in PubMed.

Cells · 2026
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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Jingrong DengDepartment of Obstetrics and Gynecology, The Third Xiangya Hospital of Central South University, Changsha, Hunan, China.

Changfa ShuDepartment of Obstetrics and Gynecology, The Third Xiangya Hospital of Central South University, Changsha, Hunan, China; Branch of National Clinical Research Center for Obstetrics and Gynecology, The Third Xiangya Hospital of Central South University, Changsha, Hunan, China; Center for Gynecological Disease and Reproductive Health, Furong Laboratory, Changsha, Hunan, China.

Dong WangDepartment of Orthopedics, The Third Xiangya Hospital of Central South University, Changsha, Hunan, China.

Richard NimbonaDepartment of Obstetrics and Gynecology, The Third Xiangya Hospital of Central South University, Changsha, Hunan, China.

Xingping ZhaoDepartment of Obstetrics and Gynecology, The Third Xiangya Hospital of Central South University, Changsha, Hunan, China; Branch of National Clinical Research Center for Obstetrics and Gynecology, The Third Xiangya Hospital of Central South University, Changsha, Hunan, China; Center for Gynecological Disease and Reproductive Health, Furong Laboratory, Changsha, Hunan, China.

Dabao XuDepartment of Obstetrics and Gynecology, The Third Xiangya Hospital of Central South University, Changsha, Hunan, China; Branch of National Clinical Research Center for Obstetrics and Gynecology, The Third Xiangya Hospital of Central South University, Changsha, Hunan, China; Center for Gynecological Disease and Reproductive Health, Furong Laboratory, Changsha, Hunan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Leiomyosarcoma (LMS) is one of the most aggressive tumors originating from smooth muscle cells, characterized by a high recurrence rate and frequent distant metastasis. Despite advancements in targeted therapies and immunotherapies, these interventions have failed to significantly improve the long-term prognosis for LMS patients. Here, we identified OncoImmune differential expressed genes (DEGs) that influence monocytes differentiation and the progression of LMS, revealing varied immune activation states of LMS patients. Using a machine learning approach, we developed a prognostic model based on OncoImmune hub DEGs, which offers a moderate accuracy in predicting risk levels among LMS patients. Mechanistically, we found that ATRX mutation may regulate coiled-coil domain-containing protein 69 (CCDC69) expression, leading to functional alterations in mast cells and immune unresponsiveness through the modulation of various immune-related signaling pathways. This machine learning-based prognostic model, centered on seven OncoImmune hub DEGs, along with ATRX gene status, represents promising biomarkers for predicting prognosis, molecular characteristics, and immune features in LMS.

Indexed as

LeiomyosarcomaMachine LearningBiomarkers, TumorGene Expression Regulation, NeoplasticHumansMutationPrognosisX-linked Nuclear ProteinATRX protein, humanBiomarkers, TumorX-linked Nuclear Protein

Identifiers

PMID40495762

DOI10.17305/bb.2025.12342

PMCPMC12451976

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