Evidence map›Paper›PMID 41463320›Full record

ArticleBiomolecules2025

Integrative Single-Cell and Machine Learning Analysis Develops a Glutamine Metabolism-Based Prognostic Model and Identifies MSMO1 as a Therapeutic Target in Osteosarcoma.

Hui Ma, Haiyang Zhang, Johny Bajgai, Md Habibur Rahman, Thu Thao Pham, Chaodeng Mo, Buchan Cao, Yeong-Eun Choi, Cheol-Su Kim, Kyu-Jae Lee

Abstract read
In one paragraph

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

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Hui MaDepartment of Global Medical Science, Wonju College of Medicine, Yonsei University Graduate School, Wonju 26426, Republic of Korea.ORCID 0009-0001-2638-3789
Haiyang ZhangDepartment of Global Medical Science, Wonju College of Medicine, Yonsei University Graduate School, Wonju 26426, Republic of Korea.
Johny BajgaiDepartment of Convergence Medicine, Wonju College of Medicine, Yonsei University, Wonju 26426, Republic of Korea.ORCID 0000-0002-7228-8053
Md Habibur RahmanDepartment of Convergence Medicine, Wonju College of Medicine, Yonsei University, Wonju 26426, Republic of Korea.
Thu Thao PhamDepartment of Convergence Medicine, Wonju College of Medicine, Yonsei University, Wonju 26426, Republic of Korea.
Chaodeng MoDepartment of Global Medical Science, Wonju College of Medicine, Yonsei University Graduate School, Wonju 26426, Republic of Korea.
Buchan CaoDepartment of Global Medical Science, Wonju College of Medicine, Yonsei University Graduate School, Wonju 26426, Republic of Korea.
Yeong-Eun ChoiDepartment of Convergence Medicine, Wonju College of Medicine, Yonsei University, Wonju 26426, Republic of Korea.
Cheol-Su KimDepartment of Convergence Medicine, Wonju College of Medicine, Yonsei University, Wonju 26426, Republic of Korea.ORCID 0000-0002-9927-9990
Kyu-Jae LeeDepartment of Global Medical Science, Wonju College of Medicine, Yonsei University Graduate School, Wonju 26426, Republic of Korea.ORCID 0000-0001-8748-8122

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Although metabolic pathways profoundly influence disease behavior, osteosarcoma (OS) still lacks a glutamine metabolism-based framework for patient stratification. By integrating single-cell RNA sequencing with bulk cohorts, we delineated a glutamine-associated transcriptional program and translated it into an externally validated, clinically oriented risk model. After rigorous quality control and doublet removal, 19 clusters were annotated into 10 cell types. Glutamine metabolism-related gene (GRG) scores, quantified by five orthogonal algorithms (AUCell, UCell, singscore, ssGSEA, and AddModuleScore), revealed pronounced intratumoral heterogeneity, particularly within osteoblastic cells. A composite GRG score correlated with 641 genes, defining 188 differentially expressed genes; intersecting positively correlated and up-regulated genes yielded 91 candidates. Through a 10-fold cross-validated benchmark of 10 machine-learning algorithms and 101 combinations, Step-Cox [forward] + Ridge emerged as the optimal pipeline, producing a five-gene prognostic model (GPX7, COL11A2, CPE, MSMO1, SGMS2) with moderate yet reproducible performance in independent cohorts. Functionally, stable MSMO1 knockdown in U2OS cells suppressed proliferation, migration, and invasion; increased apoptosis; altered GS, GLS, and α-ketoglutarate; and dampened Wnt/β-catenin signaling. Clinically, the model stratifies OS patients into molecular risk subgroups with distinct outcomes, supporting identification of high-risk individuals and informing personalized glutamine-targeted or combination therapies. Mechanistically, glutamine metabolism shapes the OS tumor microenvironment by modulating immune-evasion and angiogenic cues, underscoring its dual role in metabolic adaptation and immune-metabolic crosstalk. Collectively, this study establishes a single-cell-anchored, glutamine-coupled state in OS, introduces an externally validated prognostic tool with translational promise but modest discriminative power, and positions MSMO1 as a metabolic-signaling node warranting further mechanistic and in-vivo investigation.

Indexed as

Bone NeoplasmsGlutamineMachine LearningMitochondrial ProteinsOsteosarcomaSingle-Cell AnalysisCell Line, TumorCell ProliferationFemaleGene Expression Regulation, NeoplasticHumansPrognosisGlutamineMitochondrial Proteinsglutamine metabolismMSMO1osteosarcomaprognostic modelsingle-cell RNA sequencingtumor microenvironmentWnt/β-catenin signaling

Identifiers

PMID41463320
PMCPMC12731238

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Registered trials

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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.