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

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We investigated the potential connection between abnormal methylation differentially expressed genes and the survival rate of osteosarcoma patients. GSE36002 and GSE12865 datasets of GEO database were utilized for abnormal methylation differentially expressed genes, followed by function and pathway enrichment analyses, the protein-protein interaction network in the STRING database, and cluster analysis in the MCODE app of Cytoscape. The RNA-seq and clinical data from the TARGET-OS project of TCGA were used for univariate and least absolute shrinkage and selection operator (LASSO) Cox regression analyses to predict the risk genes of osteosarcoma. 1191 hypermethylation-downregulated genes might function through plasma membrane, negative regulation of transcription from the RNA polymerase II promoter, and pathways, including transcriptional misregulation in cancer. 127 hypomethylation-upregulated genes were enriched in proteolysis, negative regulation of the canonical Wnt signaling pathway, and metabolic signaling pathways. The univariate Cox analysis revealed 638 genes (P < 0.01), including 50 hypermethylation-downregulated genes and 4 hypomethylation-upregulated genes, subsequently based on LASSO Cox regression analysis for 54 aberrant methylation-driven genes, and three genes (COL13A1, MXI1, and TBRG1) were selected to construct the risk score model. The three genes (COL13A1, MXI1, and TBRG1) regulated by DNA methylation were identified to relate with the outcomes of OS patients, which might provide a new insight to the pathological mechanism of osteosarcoma.", "claims": [{"id": "claim-method", "statement": "Subsequently, we used univariate Cox regression to screen for genes associated with survival (P < 0.01).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35602303", "locator": "sections[7] \"2.5. 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