Decoding methylation patterns in ovarian cancer using publicly available Next-Gen sequencing data

Ovarian cancer (OC), one of the most frequent forms of cancer among women all over the world, inflicts a substantial danger to the health of human beings. An in-depth comprehension of its latent processes at the molecular level is the answer to evolving successful earmarked therapies. For an effective genomic assay, deep transcriptional sequencing has been used via Next-generation sequencing tools which are beneficial in studying OC and its related counterparts. ChIP-Seq data for a malignant and benign specimen of OC underwent comparison, and identification of differential peaks were carried out based on fold change and peaks were then annotated. BioGRID was employed to perform protein-protein interaction (PPI) network analysis, which was then constructed with Cytoscape. Highly connected genes from the constructed network were then screened. Utilizing the additional data sets from other OC cell lines gave two new classes of genes for which there is no documented role in the progression of the disease. PAX2, PAX5, FOXP1 and KLF16 are some of the promising genes whose presence among differential peaks led to the positive conclusion of their role in OC. Recent literature studies of these genes are also in conformity with the findings. Potential OC-related genes identified through our findings increase the interpretation of OC and thus provide direction for conducting research in near future.