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Network Biology, 2021, 11(3): 194-206
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Article

Identification of driver genes in renal stress condition using network clustering approach

Mohd Murshad Ahmed1, Safia Tazyeen1, Rafat Ali1, Aftab Alam1, Md Zubbair Malik2, Romana Ishrat1
1Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi 110025, India
2School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi 1100067, India

Received 25 February 2021;Accepted 30 March 2021;Published 1 September 2021
IAEES

Abstract
Chronic kidney disease (CKD) is a non-contagious, ageing-related and covert disease. It is a (chronic) disease of the kidneys leading to renal failure, huge public health problem worldwide and common comorbidity with type 2 diabetes mellitus (T2DM). Its presence and severity influence disease prognosis significantly. Identification of driver genes which regulate the CKD network is one of the main challenges in understanding its biological significance. We have analyzed microarray dataset and compare the gene expression profile of the patient with healthy control. Besides, we studied the gene regulatory networks that may help to understand the molecular mechanism in CKD. Further, after a comparative analysis of CKD and DKD. We proposed five driver genes, namely ALB, WT1, IL7R, PTPRC and DOCK2, that play an essential role in the pathogenesis of CKD and could serve as biomarkers. In the present study, we have mapped and analyzed the interactions of these five genes in the form of network and in addition to this we also tracked down the other essential, i.e. driver genes responsible for the modular nature of the network. The proposed study is based on network analysis approaches to predict some unknown CKD-associated genes, which can be validated as reliable candidates for further in vitro/in vivo experiment.

Keywords CKD;microarray data analysis;DEGs;driver gene;DKD.


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