Design protein-protein interaction network and protein-drug interaction network for common cancer diseases: A bioinformatics approach

Background: In terms of genomics, bioinformatics conceptualizes biological processes and utilizes computer science to generate knowledge from biological data. Bioinformatics is multidisciplinary in that it merges the available methods in multiple fields, including computation, inventory, statistical analysis, collection, and processing of genetic data, to provide approaches in the fields of genetics, education, and healthcare. Aim: Our study, described herein, has attempted to analyze the relationships between genetic variants in breast, colorectal, lung, and ovarian cancer, to establish a common pathway, or PPI network and PDI network. Method: ology: A computational investigation was implemented to understand the PPI network and PDI network for breast, colorectal, lung, and ovarian cancer. Related genes were collected from the NCBI gene database for each disease. A data mining technique was utilized to discover the common genes between the four diseases. Result: Based upon our computational model, a total of 10 common genes: TP53, EGFR, TNF, APOE, VEGFA, IL6, TGFB1, MTHFR, ERBB2, and ESR1 were observed. A novel pathway was found in the investigation. This research was therefore assistive in the identification and study of the PPI network and PDI network for common cancer diseases. Numerous prior studies have also demonstrated a strong association between breast, colorectal, lung, and ovarian cancer. Conclusion: The PPI network and PDI network were developed based on common genes between four studied diseases. This information can be helpful to understand the ligand binding of the PPI network. In this area of drug design, our study therefore provides additional insight. Keywords: Bioinformatics, Data mining, Gene selection, Cancer, Protein-protein interaction, Protein-drug interaction

Abstract licence: CC BY-NC

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