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

Frontiers in Cardiovascular Medicine

Hala Abubaker Bagabir¹  Mohd Murshad Ahmed²  Mohd Sajad²  Aftab Alam²  Safia Tazyeen²  Shahnawaz Ali³  Rafat Ali⁴  Romana Ishrat⁵  Ahmad Sulimani⁶  Rania Abubaker Bagabir⁷  Shafiul Haque⁸ 

[1]Regenerative Medicine, KING' College London, Guy's Hospital, London, United Kingdom

[2]Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi, India

[3]

[4]Centre for Stem Cell &Department of Bioscience, Jamia Millia Islamia, New Delhi, India

[5]Department of Hematology and Immunology, College of Medicine, Umm-Al-Qura University, Mecca, Saudi Arabia

[6]Department of Medical Laboratory Technology, College of Applied Medical Sciences, Jazan University, Jazan, Saudi Arbia

[7]Department of Medical Physiology, Faculty of Medicine, King Abdulaziz University, Rabigh, Saudi Arabia

[8]Research and Scientific Unit, College of Nursing and Allied Health Science, Jazan University, Jazan, Saudi Arabia

关键词: CVD;    CKD;    PPIN network;    IVI;    hubness score;    spreading score;   

DOI  :  10.3389/fcvm.2021.755321

来源: DOAJ

【 摘 要 】

In fact, the risk of dying from CVD is significant when compared to the risk of developing end-stage renal disease (ESRD). Moreover, patients with severe CKD are often excluded from randomized controlled trials, making evidence-based therapy of comorbidities like CVD complicated. Thus, the goal of this study was to use an integrated bioinformatics approach to not only uncover Differentially Expressed Genes (DEGs), their associated functions, and pathways but also give a glimpse of how these two conditions are related at the molecular level. We started with GEO2R/R program (version 3.6.3, 64 bit) to get DEGs by comparing gene expression microarray data from CVD and CKD. Thereafter, the online STRING version 11.1 program was used to look for any correlations between all these common and/or overlapping DEGs, and the results were visualized using Cytoscape (version 3.8.0). Further, we used MCODE, a cytoscape plugin, and identified a total of 15 modules/clusters of the primary network. Interestingly, 10 of these modules contained our genes of interest (key genes). Out of these 10 modules that consist of 19 key genes (11 downregulated and 8 up-regulated), Module 1 (RPL13, RPLP0, RPS24, and RPS2) and module 5 (MYC, COX7B, and SOCS3) had the highest number of these genes. Then we used ClueGO to add a layer of GO terms with pathways to get a functionally ordered network. Finally, to identify the most influential nodes, we employed a novel technique called Integrated Value of Influence (IVI) by combining the network's most critical topological attributes. This method suggests that the nodes with many connections (calculated by hubness score) and high spreading potential (the spreader nodes are intended to have the most impact on the information flow in the network) are the most influential or essential nodes in a network. Thus, based on IVI values, hubness score, and spreading score, top 20 nodes were extracted, in which RPS27A non-seed gene and RPS2, a seed gene, came out to be the important node in the network.

【 授权许可】

Unknown