Computational-based identification and analysis of globally expressed differential genes in high-grade serous ovarian carcinoma cell lines
Paediatrics and Child Health
Ovarian Cancer (OVCA) is the most occurring gynecological cancer worldwide, often diagnosed at a later stage and ultimate results in a high death rate. To overcome this serious health concern, it is important to understand the molecular mechanisms and equally significant to identify the putative biomarkers as well as the therapeutic drug targets for the early diagnosis and treatment of OVCA. In doing so, a strategy is designed to study the most frequently diagnosed cases of OVCA called as High-Grade Serous Ovarian Carcinoma (HGSOC) cell lines with the combination of computational biology, biostatistics and cancer informatics approaches. This study is directed to investigate the global gene expression profiling, and to perform the analyses of identified global Differently Expressed Genes (DEGs) of OVCA. The microarray dataset (GSE71524) is comprised of tumor and cell line samples of OVCA and it was used for the identification of DEGs in the current study. The STRING database was used to construct Protein-Protein Interaction (PPI) network of DEGs, and hub genes were identified by the CytoHubba. In addition, a functional enrichment analysis of up- and down-regulated DEGs was performed by a bioinformatics database called as DAVID. The microRNAs (miRNAs) and transcription factors (TFs) analyses were conducted with the aid of biological tools, MAGIA and GenCOdis3, respectively. As a result, the genes comprised of CSF1R, TYROBP, PLEK, FGR, ACLY, ACACA, LAPTM5, C1 or f162, IL10RA and CD163 were identified as hub genes. Additionally, miRNA analysis resulted in finding an association of zinc finger protein with OVCA comes out after implementing different algorithms. On the other hand, in the TFs analysis resulted in various DEGs that were enriched by NFAT, NF1 and GABP TFs. In this study, it was observed that ACACA, ACLY and CSF1R DEGs showed significant occurrence in different steps, and therefore, these genes were studied, precisely. Nevertheless, the results may help to discover the potential biomarkers with deep understanding of molecular mechanisms. However, further validation is required to explain the OVCA pathogenesis.
Computational Biology and Chemistry
(2020). Computational-based identification and analysis of globally expressed differential genes in high-grade serous ovarian carcinoma cell lines. Computational Biology and Chemistry, 88, 107333.
Available at: https://ecommons.aku.edu/pakistan_fhs_mc_women_childhealth_paediatr/939