A Polygenic Methylation Prediction Model Associated with Response to Chemotherapy in Epithelial Ovarian Cancer

Abstract To identify potential aberrantly differentially methylated genes (DMGs) correlated with chemotherapy response and establish a polygenic methylation prediction model of CR in epithelial ovarian cancer (EOC). We accessed 177 (47 chemo-sensitive and 130 chemo-resistant) samples corresponding to three DNA-methylation microarray datasets from Gene Expression Omnibus and 306 (290 chemo-sensitive and 16 chemo-resistant) samples from The Cancer Genome Atlas (TCGA) database. DMGs associated with chemo-sensitivity and chemo-resistance were identified by several packages of R software. Pathway enrichment and protein-protein interaction (PPI) networks analyses were constructed by Metascape software. The key genes containing mRNA expressions associated with methylation levels were validated from the expression dataset by the GEO2R platform. Determining the prognostic significance of key genes was performed by the Kaplan Meier-plotter database. The key genes-based polygenic methylation prediction model was established by binary logistic regression. Among accessed 483 samples, 457 (182 hypermethylated and 275 hypomethylated) differentially methylated genes correlated with chemo-resistance. Twenty-nine hub genes were identified and further validated. Three genes, AGR2, HSPA2, and ACAT2, showed a significantly negative correlation between their methylation levels and mRNA expressions, which also corresponded prognostic significance. A polygenic methylation prediction model (0.5253 cutoff value) was established and validated with 0.659 sensitivity and 0.911 specificity.