Evidence map›Paper›PMID 42272640›Full record

ArticleFrontiers in cell and developmental biology2026

Heterogeneity-based stratification identifies CKMT2 as a prognostic marker in osteosarcoma.

Xuesong Li, Guanghao Li, Tianchou Peng, Junjun Qiu, Shengwei Li, Jingfu Wang, Jin Zhang

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Article in Frontiers in cell and developmental biology, 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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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Xuesong Li *Department of Pediatric Oncology, Shandong Cancer Hospital and Institute, Jinan, Shandong, China.
Guanghao Li *Department of Urology, Shandong Cancer Hospital and Institute, Jinan, China.
Tianchou Peng *Department of Spine, East Hospital, Tongji University School of Medicine, Shanghai, China.
Junjun Qiu *Department of Orthopedic Surgery, Institute of Digital Medicine, Nanjing First Hospital, Nanjing Medical University, Nanjing, China.
Shengwei LiDepartment of Interventional Radiology, Shandong Cancer Hospital and Institute, Jinan, Shandong, China.
Jingfu WangDepartment of Pediatric Oncology, Shandong Cancer Hospital and Institute, Jinan, Shandong, China.
Jin ZhangDepartment of Orthopedics, Nanjing First Hospital, Nanjing Medical University, Nanjing, Jiangsu, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Osteosarcoma (OS) is the most common primary malignant bone tumor. Intratumoral heterogeneity (ITH) contributes to therapy resistance and disease spread. Understanding the molecular structure and cellular groups contributing to ITH, as well as their clinical impacts, remains limited. Methods: To build our investigation, we started by integrating single-cell transcriptomic data with multi-cohort transcriptomic datasets sourced from public repositories. Our bioinformatics pipeline was then designed to systematically unpack this information: first, we identified malignant cells by inferring their copy number variation (CNV); next, we assessed the degree of heterogeneity and defined distinct molecular subtypes; and finally, we conducted an in-depth analysis of the functional pathways mediating these heterogeneous characteristics. To sharpen the prognostic model linked to ITH, we employed machine learning to develop and validate a prognostic risk model for ten critical genes and performed both clinical and functional assays. Meanwhile, we validated the pro-tumorigenic role and therapeutic viability of CKMT2 through immunohistochemistry (IHC) and a series of Results: Analysis of transcriptomic data from single-cells highlighted ten primary cell types in osteosarcoma. The dominant malignant population as per CNV analysis has been identified as osteoblastic cells (OBs). The subsequent subclassification of OB led to the discovery of five malignant subtypes, resulting in stratification of high and low ITH. Consensus clustering, driven by the differential expression genes discovered in ITH, successfully divided the population into two distinct molecular subtypes. These subtypes exhibited a robust correlation with clinical prognosis and immune infiltration in multiple independent cohorts. Through machine learning modeling, we identified a highly promising therapeutic target, Creatine Kinase Mitochondrial 2 (CKMT2), and performed both clinical and functional assays. IHC validated high CKMT2 expression in tumor tissues corresponding to clinical stage, and Conclusion: This study provides a systematic map of osteosarcoma's ITH landscape, identifying distinct molecular subtypes with varying clinical outcomes and immune profiles. CKMT2 is established as a key driver of osteosarcoma progression, serving both as a prognostic biomarker and a potential therapeutic target.

Indexed as

biomarkerCKMT2intratumoral heterogeneitymolecular dynamics simulationosteosarcomaprognosis

Identifiers

PMID42272640
PMCPMC13247562

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