A prognostic glycolysis-related gene signature in osteosarcoma: implications for metabolic programming, immune microenvironment, and drug response.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40321814", "kind": "science-record", "revision": 3, "content_hash": "sha256:94246211a9037205a78cc85a11e6bafeb8bdd204ebe15d6b4ba2014cdbd85303", "created_at": "2026-11-01T20:30:29Z", "updated_at": "2026-11-01T22:14:01Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A prognostic glycolysis-related gene signature in osteosarcoma: implications for metabolic programming, immune microenvironment, and drug response.", "doi": "10.7717/peerj.19369", "pmid": "40321814", "authors": ["Naiqiang Zhu", "Jingyi Hou", "Yu Zhang", "Ning Yang", "KaiKai Ding", "Chengbing Chang", "Yanqi Liu", "Haipeng Gu", "Bin Chen", "Xu Wei", "Liguo Zhu"], "journal": "PeerJ", "year": 2025, "abstract": "Background/Aims: Osteosarcoma (OS), a malignant tumor originating in the bone or cartilage, primarily affects children and adolescents. Notably, glycolysis is the main target for metabolic programming to ensuring the energy supply for cancer. This study aimed to establish a glycolysis-related gene (GRG) risk signature in OS to comprehensively assessing the pathogenic, prognosis, and their application in predicting drug response.\nMethods: mRNA expression profiles were acquired from the Gene Expression Omnibus (GEO, GSE16091, GSE39058, and GSE21257). Using the non-negative matrix factorization (NMF) algorithm, patients with OS were stratified into distinct subgroups based on 288 GRGs identified through univariable Cox analysis. Univariate Cox regression analysis of differentially expressed genes (DEGs) between the molecular clusters was conducted to establish a risk signature comprising GRGs in OS. The prognostic efficacy of this risk signature was assessed via Kaplan-Meier curve analysis and Cox regression, evaluating its independence as a prognostic indicator. Additionally, the predictive potential of the risk model for drug response was evaluated using the \"OncoPredict\" package. Furthermore, the distribution of immune cell types in single-cell RNA sequencing (scRNA-seq) data was examined in correlation with the four identified GRGs risk signatures, followed by validation of expression levels in vitro using RT-PCR.\nResults: Patients diagnosed with OS were categorized into two distinct molecular subgroups, exhibiting notable variations in prognosis and tumor microenvironment. Univaria te Cox regression analysis was employed to identify four GRGs, namely chondroitin sulfate glucuronyltransferase (CHPF), Ras-related GTP-binding protein D (RRAGD), nucleoprotein TPR (TPR), and versican core protein (VCAN), which constitute a prognostic signature for patients with OS. This signature demonstrated robust prognostic value, as corroborated by Kaplan-Meier, univariate, and multivariate Cox regression analyses. Significant differences in tumor microenvironment immune infiltration (such as B cells, monocytes) were observed between molecular subgroups. Moreover, a significant disparity in drug sensitivity to AZD8055, paclitaxel, and PD0325901 was noted between the high-risk and low-risk cohorts, and the established four-gene risk signature served as dependable prognostic indicators in the validation cohort, confirmed at the cellular level through external dataset validation and reverse transcription quantitative PCR (RT-qPCR) experiments.\nConclusion: A risk signature based on GRGs was established for OS, exhibiting robust predictive efficacy for prognostic assessment, and offering significant clinical utility for the prognosis of OS.", "claims": [{"id": "claim-method", "statement": "The OS samples from GSE16091 and GSE39058 were clustered using the NMF algorithm according to 288 GRGs.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40321814", "locator": "sections[4] \"Identification of glycolysis molecular subtypes in OS\" paragraph 2", "quote": "The OS samples from GSE16091 and GSE39058 were clustered using the NMF algorithm according to 288 GRGs."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The raw data are available in the Supplementary Files.The gene expression datasets are available at GEO: GSE16091, GSE39058 and GSE21257.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40321814", "locator": "sections[30] \"Additional Information and Declarations\" paragraph 4", "quote": "The raw data are available in the Supplementary Files.The gene expression datasets are available at GEO: GSE16091, GSE39058 and GSE21257."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Patients with OS were categorized into two distinct molecular subgroups using non-negative matrix factorization based on 288 glycolysis-related genes.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:40321814", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Using the non-negative matrix factorization (NMF) algorithm, patients with OS were stratified into distinct subgroups based on 288 GRGs identified through univariable Cox analysis."}, {"asset": "source:pmid:40321814", "locator": "sections[19] \"Identification of glycolysis molecular subtypes in OS\" paragraph 2", "quote": "The optimal clustering stability was achieved when K = 2 (Figs. 2A, 2B)."}, {"asset": "source:pmid:40321814", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "mRNA expression profiles were acquired from the Gene Expression Omnibus (GEO, GSE16091, GSE39058, and GSE21257)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Patients with OS were categorized into two distinct molecular subgroups using non-negative matrix factorization based on 288 glycolysis-related genes.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:40321814"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE16091"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE16091"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:40321814", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.7717/peerj.19369", "sha256": 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