Deciphering keys genes in Cardio-renal Syndrome using network analysis

HOME | PDF |

Title	Deciphering keys genes in Cardio-renal Syndrome using network analysis
Authors	Mohd Murshad Ahmed¹, Safia Tazyeen¹, Aftab Alam1, Anam Farooqui¹, Rafat Ali¹, Nikhat Imam¹, Naaila Tamkeen¹, Shahnawaz Ali¹, Md Zubbair Malik², Romana Ishrat^1,*
Affiliation	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; Corresponding author*
Email	Dr. Romana Ishrat, Email:rishrat@jmi.ac.in;
Article Type	Research Article
Date	Received November 2, 2020; Revised December 31, 2020; Accepted January 26, 2020, Published January 31, 2021
Abstract	Cardio-renal syndrome (CRS) is a rapidly recognized clinical entity which refers to the inextricably connection between heart and renal impairment, whereby abnormality to one organ directly promotes deterioration of the other one. Biological markers help to gain insight into the pathological processes for early diagnosis with higher accuracy of CRS using known clinical findings. Therefore, it is of interest to identify target genes in associated pathways implicated linked to CRS. Hence, 119 CRS genes were extracted from the literature to construct the PPIN network. We used the MCODE tool to generate modules from network so as to select the top 10 modules from 23 available modules. The modules were further analyzed to identify 12 essential genes in the network. These biomarkers are potential emerging tools for understanding the pathophysiologic mechanisms for the early diagnosis of CRS. Ontological analysis shows that they are rich in MF protease binding and endo-peptidase inhibitor activity. Thus, this data help increase our knowledge on CRS to improve clinical management of the disease.
Keywords	CRS; PPIN network; Module; Gene Ontology; Biomarkers; Pathways
Citation	Ahmed et al. Bioinformation 17(1): 86-100 (2021)
Edited by	P Kangueane
ISSN	0973-2063
Publisher	Biomedical Informatics
License	This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.