Development of a Risk Score Model for Osteosarcoma Based on DNA Methylation-Driven Differentially Expressed Genes

Area of Science:

• Oncology
• Molecular Biology
• Genetics

Background:

• Osteosarcoma (OS) is a prevalent bone cancer in adolescents, associated with poor prognosis including metastasis and treatment resistance.
• Understanding the molecular mechanisms, particularly epigenetic alterations like DNA methylation, is crucial for improving OS patient outcomes.

Purpose of the Study:

• To investigate the association between aberrant DNA methylation patterns and differentially expressed genes in osteosarcoma.
• To identify novel methylation-driven genes that can predict survival rates in osteosarcoma patients.

Main Methods:

• Utilized GEO datasets (GSE36002, GSE12865) for identifying differentially methylated genes.
• Performed functional and pathway enrichment analyses, constructed protein-protein interaction networks, and used TCGA TARGET-OS data for Cox regression analyses.
• Applied univariate and LASSO Cox regression to identify key risk genes for osteosarcoma.

Main Results:

• Identified 1191 hypermethylation-downregulated genes and 127 hypomethylation-upregulated genes involved in various cellular processes and cancer pathways.
• Univariate Cox analysis revealed 638 significant genes, leading to 54 aberrant methylation-driven genes via LASSO Cox regression.
• A risk score model was constructed using three methylation-regulated genes: COL13A1, MXI1, and TBRG1, demonstrating a correlation with osteosarcoma patient outcomes.

Conclusions:

• DNA methylation-driven genes, specifically COL13A1, MXI1, and TBRG1, are significantly associated with osteosarcoma patient survival.
• These findings offer new insights into the pathological mechanisms of osteosarcoma and suggest potential therapeutic targets.