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Machine Learning-Based Identification and Validation of PYCR1 and PYGM as Prognostic Biomarkers for Osteosarcoma
Guoyong Xu1, Chong Liu1,2, Jiang Xue1
1Guangxi Medical University, The First Clinical Medical College, Nanning, P. R. China.
Clinical Medicine Insights. Oncology
|June 1, 2026
Summary
This study identifies Pyrroline-5-carboxylate reductase 1 (PYCR1) and glycogen phosphorylase, muscle-associated (PYGM) as potential prognostic biomarkers for osteosarcoma (OS). PYCR1 is upregulated and linked to poor prognosis, while PYGM shows complex expression patterns.
Area of Science:
- Oncology
- Molecular Biology
- Biomarker Discovery
Background:
- Osteosarcoma (OS) is an aggressive bone cancer primarily affecting children and adolescents, with a poor prognosis.
- Identifying novel prognostic biomarkers is essential for improving OS diagnosis and treatment strategies.
Purpose of the Study:
- To identify and validate novel prognostic biomarkers for osteosarcoma (OS).
- To investigate the expression patterns and prognostic significance of candidate genes in OS.
Main Methods:
- Gene expression data from OS and normal tissues were analyzed using univariate Cox and LASSO regression.
- Non-negative matrix factorization (NMF) identified molecular subtypes, and Kaplan-Meier analysis assessed survival.
- Tumor microenvironment, immune cell infiltration, qRT-PCR, and histological analyses were performed for validation.
Main Results:
- Pyrroline-5-carboxylate reductase 1 (PYCR1) was consistently upregulated in OS and associated with poor prognosis.
- Glycogen phosphorylase, muscle-associated (PYGM) exhibited context-dependent expression, downregulated in bulk tissue but higher in high-risk OS groups.
- Significant immune differences were observed between high- and low-risk groups, with PYCR1 and PYGM validated at histological and protein levels.
Conclusions:
- PYCR1 and PYGM were identified and validated as potential prognostic biomarkers for OS.
- PYCR1 demonstrates a tumor-promoting role, while PYGM's expression is complex and context-dependent, highlighting metabolic biomarker intricacies in OS.
