Related Experiment Video
Updated: May 25, 2025
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
Published on: August 16, 2020
Identification of Mitochondrial-related Characteristic Biomarkers in Osteosarcoma using Bioinformatics and Machine
Jingyi Hou1,2, Yu Zhang1,2,3, Ning Yang1,3,4
1Department of Minimally Invasive Spinal Surgery, The Affiliated Hospital of Chengde Medical University, Chengde 067000, China.
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 via comprehensive bioinformatics analysis.
Results:
MitoDEGs in OS were significantly enriched in the pathways associated with mitochondrial function and immune regulation. Two MitoDEGs, UCP2 and PRDX4, were identified via 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, RTPCR 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.
