Analyzing the protein-protein interaction network and the topological properties of prostate cancer and allied diseases: A computational bioinformatics approach

• Published in: Gene reports 2020-12, Vol.21, p.100842, Article 100842
• Main Authors: Puspo, Nadira Akter, Akter, Laboni, Siddique, Sinthia, Paul, Bikash Kumar, Ahmed, Kawsar, Bhuiyan, Touhid, Islam, Md Kabirul
• Format: Article
• Language: English
• Subjects: Bladder cancer (BDC); Breast cancer (BC); Colorectal cancer (CRC); Computational bioinformatics; Prostate cancer (PC); Protein-protein interaction network
• ISSN: 2452-0144; 2452-0144
• DOI: 10.1016/j.genrep.2020.100842

Some of cancer diseases are related to each other by their metabolic structures. Literature reviews show that Prostate Cancer (PC), Breast Cancer (BC), Bladder Cancer (BDC) and Colorectal Cancer (CRC) are related. Some are shown up for affected family background in their early or grown-up age. Python programming language is used for data mining, pre-processing and sorting and finding common genes from gathered data whose are collected National Centre of Biotechnology Information (NCBI). Protein-Protein Interaction (PPIs) and Protein Disease Interaction (PDI) are displayed by using bioinformatics technology. We use identified hub genes for making co-expression and physical interaction. Interactions for selected top 8 genes are exhibited following different bioinformatics tools. The gene-miRNA interaction generates interactions with a total of 651 links between 8 genes. Where, the TF-gene Interaction creates relationships between 176 nodes and 278 edges. There are 6 seed nodes. Besides, PDI represents a subnetwork which creates relationships between 47 nodes and 46 edges. There are 1 seed nodes. In addition, PCI creates relationships between 1437 nodes and 2165 edges. There are 7 seed nodes. Furthermore, GDA creates relationships between 235 nodes and 272 edges. There are 5 seed nodes. This study will be helpful for further studies of different bioinformatics tools for designing gene network models and drugs design. These drugs can be considered for further verification by chemical experiments. •A computational analysis of PPI network is described for Prostate Cancer. .•We use identified hub genes for making co-expression and physical interaction.•Interactions for selected top 8 genes are exhibited following different bioinformatics tools•This study will be helpful for further studies of designing gene network models and drugs design.•These drugs can be considered for further verification by chemical experiments.