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image of Identification of Mitochondrial-related Characteristic Biomarkers in Osteosarcoma using Bioinformatics and Machine Learning

Abstract

Background/Aims

Osteosarcoma (OS), a malignant tumor originating in bone or cartilage, primarily affects children and adolescents. Notably, substantial alterations in mitochondrial energy metabolism have been observed in OS; however, the specific contribution of mitochondrial-related genes (MRGs) to OS pathogenesis and prognosis remains unclear. Herein, we identified novel diagnostic biomarkers associated with mitochondrial-related processes in OS comprehensive bioinformatics analysis.

Methods

OS mRNA expression profiles were retrieved from GSE16088 and GSE19276 databases. Mitochondrial-related differentially expressed genes (MitoDEGs) were identified by integrating differentially expressed analysis with mitochondrial-localized genes. A protein-protein interaction network was constructed, and machine learning algorithms (LASSO regression analysis and SVM-RFE) identified characteristic MitoDEGs. Subsequently, immune cell infiltration, microenvironment analysis, and single-cell RNA sequencing (scRNA-seq) analyzed differences in characteristic MitoDEGs, and RT-PCR was used for verification of characteristic MitoDEGs.

Results

MitoDEGs in OS were significantly enriched in the pathways associated with mitochondrial function and immune regulation. Two MitoDEGs, UCP2 and PRDX4, were identified LASSO and SVM-RFE. Correlation analysis demonstrated a close association between UCP2 and PRDX4 expression levels and immune cell infiltration, particularly in CD8+ T and native CD4+ T cells, as observed in both immune cell and scRNA-seq analyses. Furthermore, RT-PCR confirmed the expression levels of UCP and PRDX4 at the cellular level, which was consistent with the bioinformatics results.

Conclusion

This study identified UCP2 and PRDX4 as characteristic MitoDEGs and potential diagnostic biomarkers for OS using machine learning algorithms. These findings provide novel insights into the clinical applications of these biomarkers for OS diagnosis.

© 2025 Bentham Science Publishers
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2025-02-25
2025-04-24

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Identification of Mitochondrial-related Characteristic Biomarkers in Osteosarcoma using Bioinformatics and Machine Learning
Bentham Science Publishers ; https://doi.org/10.2174/0113862073350964241203080017
/content/journals/cchts/10.2174/0113862073350964241203080017
/content/journals/cchts/10.2174/0113862073350964241203080017
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  • Article Type:     Research Article
Keywords: PRDX4 ; mitochondrial-related differentially expressed genes ; Osteosarcoma ; UCP2 ; machine learning

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