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 (CR) 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 the Gene Expression Omnibus and 306 (290 chemo-sensitive and 16 chemo-resistant) samples from The Cancer Genome Atlas (TCGA) database. DMGs associated with chemotherapy sensitivity and chemotherapy resistance were identified by several packages of R software. Pathway enrichment and protein-protein interaction (PPI) network 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. The determination of 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) DMGs correlated with chemo resistance. Twenty-nine hub genes were identified and further validated. Three genes, anterior gradient 2 (AGR2), heat shock-related 70-kDa protein 2 (HSPA2), and acetyltransferase 2 (ACAT2), showed a significantly negative correlation between their methylation levels and mRNA expressions, which also corresponded to prognostic significance. A polygenic methylation prediction model (0.5253 cutoff value) was established and validated with 0.659 sensitivity and 0.911 specificity.

Keywords: ACAT2; AGR2; DNA methylation; HSPA2; bioinformatics; chemotherapy response; ovarian cancer; prediction model.

Graphical abstract

Figure 1

Regional distribution in the gene context, CpG-island neighborhood, and chromosome between hypermethylated and hypomethylated genes (A) The proportion of hypermethylated and hypomethylated genes among all 457 differentially methylated genes. (B) Regional distribution in the gene context of 182 differentially hypermethylated genes. (C) Regional distribution in the CpG-island neighborhood of 182 differentially hypermethylated genes. (D) Regional distribution in the gene context of 275 differentially hypomethylated genes. (E) Regional distribution in the CpG-island neighborhood of 275 differentially hypomethylated genes. (F) Regional distribution in the chromosome of 182 differentially hypermethylated genes.

Figure 2

Enrichment pathway and PPI network analysis by Metascape (A) Enriched ontology clusters colored by cluster ID. (B) Heatmap of top 10 GO pathways. (C) PPI MCODE components.

Figure 3

The DNA methylation levels, RNA expressions, and survival curves among three key genes (A) RNA expression levels between the sensitive and resistant group about gene AGR2. (B) DNA methylation levels between the sensitive and resistant group about gene AGR2. (C) Survival curves based on gene expression level and survival time (PFS) about gene AGR2. (D) RNA expression levels between the sensitive and resistant group about gene HSPA2. (E) DNA methylation levels between the sensitive and resistant group about gene HSPA2. (F) Survival curves based on gene expression level and survival time (PFS) about gene HSPA2. (G) RNA expression levels between the sensitive and resistant group about gene ACAT2. (H) DNA methylation levels between the sensitive and resistant group about gene ACAT2. (I) Survival curves based on gene expression level and survival time (PPS) about gene ACAT2.

Figure 4

Receiver operating characteristic (ROC) curves of three models with training set and validation set (A) Three ROC curves of three models with training set. (B) Three ROC curves of three models with validation set.