Machine Learning–Based Identification and Validation of PYCR1 and PYGM as Prognostic Biomarkers for Osteosarcoma.
Background: Osteosarcoma (OS) is a malignant tumor originating in the bones, predominantly affecting children and adolescents, characterized by high aggressiveness and poor prognosis. Identifying new prognostic biomarkers is crucial for improving the diagnosis and treatment of OS. Methods: In this s...
| Publicado en: | Clinical Medicine Insights: Oncology Vol. 20; pp. 1 - 19 |
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| Autores principales: | , , , , , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
5/27/2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=194089745&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 194089745 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 11795549 B3KT jtl: Clinical Medicine Insights: Oncology issn: 11795549 maglogo: Y pubinfo: dt: 5/27/2026 vid: 20 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 194089745 194089745 194089745 10.1177/11795549261452567 194089745 ppf: 1 ppct: 18 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Machine Learning–Based Identification and Validation of PYCR1 and PYGM as Prognostic Biomarkers for Osteosarcoma. aug: au: Xu, Guoyong Liu, Chong Xue, Jiang Chen, Jiarui Zou, Zhuan Mo, Sen Zhou, Zhongxian Zhan, Xinli affil: Guangxi Medical University, The First Clinical Medical College, Nanning, P. R. China sug: subj: Osteosarcoma Prognosis Tumor Markers, Biological Genes Genetic Markers Machine Learning Algorithms Evaluation Prediction Models Evaluation Human Funding Source Univariate Statistics Cox Proportional Hazards Model Regression Kaplan-Meier Estimator Survival Analysis Neoplastic Processes Neoplasm Invasiveness Gene Expression Profiling Reverse Transcriptase Polymerase Chain Reaction Staining and Labeling Immunohistochemistry Fluorescent Antibody Technique Transferases ab: Background: Osteosarcoma (OS) is a malignant tumor originating in the bones, predominantly affecting children and adolescents, characterized by high aggressiveness and poor prognosis. Identifying new prognostic biomarkers is crucial for improving the diagnosis and treatment of OS. Methods: In this study, we collected gene expression data from 88 OS samples from the UCSC Xena platform and normal tissue expression data from 396 Genotype-Tissue Expression (GTEx) samples. Prognosis-related genes were first screened by univariate Cox regression and then further selected using the Least Absolute Shrinkage and Selection Operator (LASSO) regression. Based on these candidate genes, non-negative matrix factorization (NMF) was used for molecular subtype identification, and the Kaplan-Meier analysis was applied to compare survival among subtypes. Tumor microenvironment and immune cell infiltration analyses were performed to characterize differences between risk groups. In addition, the expression patterns of key genes were validated by quantitative real-time polymerase chain reaction (qRT-PCR), hematoxylin-eosin staining, immunohistochemistry, and immunofluorescence. Results: Pyrroline-5-carboxylate reductase 1 (PYCR1) was consistently upregulated in OS and was associated with poor prognosis. In contrast, glycogen phosphorylase, muscle-associated (PYGM) showed analysis-level-dependent expression patterns: it was downregulated at the bulk transcriptomic and tumor cell levels compared with normal controls, whereas within the OS cohort, relatively higher PYGM expression was observed in the high-risk group. Tumor microenvironment and immune cell infiltration analyses revealed significant immune differences between high- and low-risk groups. Histological and protein-level assays further confirmed the presence and cellular localization of PYCR1 and PYGM in OS tissues. Conclusion: This study systematically identified and validated PYCR1 and PYGM as potential prognostic biomarkers for OS using integrated statistical and machine learning approaches. The PYCR1 showed a consistently tumor-promoting expression pattern, whereas PYGM demonstrated context-dependent expression changes across bulk tissue, risk-stratified tumor samples, and tumor cell lines, highlighting the biological complexity of metabolic biomarkers in OS. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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