Title of article :
Prioritizing Candidate Genes for Type 2 Diabetes Mellitus using Integrated Network and Pathway Analysis
Author/Authors :
Prakash, Tejaswini Department of Studies in Genetics and Genomics - Genetics and Genomics Lab - University of Mysore - Manasagangothri - Mysuru – 570 006, Karnataka, India , Ramachandra, Nallur B Department of Studies in Genetics and Genomics - Genetics and Genomics Lab - University of Mysore - Manasagangothri - Mysuru – 570 006, Karnataka, India
Pages :
8
From page :
239
To page :
246
Abstract :
Background: Type 2 Diabetes Mellitus (T2DM) has emerged as a major threat to global health that fosters life-threatening clinical complications, taking a huge toll on our society. More than 65 million Indians suffer from T2DM, making it one of the lead-ing causes of death. T2DM and associated complications have to be constantly moni-tored and managed which reduces the overall quality of life and increases socioeco-nomic burden. Therefore, it is crucial to develop specific treatment and management strategies. In order to achieve this, it is essential to understand the underlying genetic causes and molecular mechanisms. Methods: Integrated gene network and ontology analyses facilitate prioritization of plausible candidate genes for T2DM and also aid in understanding their mechanistic pathways. In this study, T2DM-associated genes were subjected to sequential interac-tion network and gene set enrichment analysis. High ranking network clusters were derived and their interrelation with pathways was assessed. Results: About 23 significant candidate genes were prioritized from 615 T2DM-associ-ated genes which were overrepresented in pathways related to insulin resistance, type 2 diabetes, signaling cascades such as insulin receptor signaling pathway, PI3K signal-ing, IGFR signaling pathway, ERBB signaling pathway, MAPK signaling pathway and their regulatory mechanisms. Conclusion: Of these, two tyrosine kinase receptor genes-EGFR and IGF1R were identi-fied as common nodes and can be considered to be significant candidate genes in T2DM.
Keywords :
Gene ontology , Hub genes identification , In silico analysis , Text mining , Type 2 diabetes mellitus
Journal title :
AJMB Avicenna Journal of Medical Biotechnology
Serial Year :
2022
Record number :
2729663
Link To Document :
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