AUTHOR=Ahmed Mohd Murshad , Tazyeen Safia , Haque Shafiul , Alsulimani Ahmad , Ali Rafat , Sajad Mohd , Alam Aftab , Ali Shahnawaz , Bagabir Hala Abubaker , Bagabir Rania Abubaker , Ishrat Romana 

TITLE=Network-Based Approach and IVI Methodologies, a Combined Data Investigation Identified Probable Key Genes in Cardiovascular Disease and Chronic Kidney Disease

JOURNAL=Frontiers in Cardiovascular Medicine

VOLUME=Volume 8 - 2021

YEAR=2022

URL=https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2021.755321

DOI=10.3389/fcvm.2021.755321

ISSN=2297-055X

ABSTRACT=Chronic kidney disease (CKD) patients are more likely to develop renal failure, cardiovascular disease (CVD), and death. In fact, the risk of dying from CVD is significantly larger than the risk of developing end-stage renal disease (ESRD). Patients with severe CKD are often excluded from randomized controlled trials, making evidence-based therapy of comorbidities like CVD complicated. The goal of this study is to use an integrated bioinformatics method to discover differentially expressed genes (DEGs) and their related functions and pathways, in order to gain a better understanding of the molecular mechanisms driving these situations. DEGs were discovered by comparing gene expression microarray data from CVD and CKD using GEO2R/R program (version 3.6.0, 64 bit). Thereafter, the online STRING version 9.1 program was used to look for any correlations between all common/overlapping DEGs, and the results were shown using Cytoscape (version 3.8.0). Using Cystoscope’s MCODE plugin, we have identified 15 modules/clusters, 10 of which contained genes of interest, and discovered that these were primarily enriched in pathways. In these 10 studied modules, 19 key genes (11 down-regulated and 8 up-regulated) were identified. Module 1 (RPL13 RPLP0 RPS24 RPS2) and module 5 (MYC COX7B SOCS3) contain the highest number of key genes. Clue GO constructs a functionally ordered GO/pathway term network by combining Gene Ontology (GO) terms with pathways. This study led to the identification of the most influential nodes within a gene-gene network using IVI approaches and could lead to the development of novel biomarkers. In this article, we used a novel algorithm, IVI (Integrated Value of Influence) that combines the most important topological characteristics of the network to identify the key individuals within the network. Nodes with many connections (calculated by hubness score) and high spreading potential (the spreader nodes are predicted to have the greatest impact on the flow of information throughout the network) are the most influential or vital nodes in a network. Based on IVI values, hubness score and Spreading score top 20 nodes extracted, in which RPS27A non-seed gene, RPS2 (seed gene) were the most influential node in the native network.