Title:Unearthing Insights into Metabolic Syndrome by Linking Drugs, Targets,
and Gene Expressions Using Similarity Measures and Graph Theory
Volume: 20
Issue: 6
Author(s): Alwaz Zafar, Bilal Wajid*, Ans Shabbir, Fahim Gohar Awan, Momina Ahsan, Sarfraz Ahmad, Imran Wajid, Faria Anwar and Fazeelat Mazhar
Affiliation:
- Ibn Sina Research & Development Division, Sabz-Qalam, Lahore, 54000, Pakistan
- Department of Electrical Engineering,
University of Engineering and Technology, Lahore, 54000, Pakistan
Keywords:
Metabolic syndrome, diabetes, cardiovascular disease, drugs, graph theory, gene regulatory networks.
Abstract:
Aims and Objectives: Metabolic syndrome (MetS) is a group of metabolic disorders
that includes obesity in combination with at least any two of the following conditions, i.e., insulin
resistance, high blood pressure, low HDL cholesterol, and high triglycerides level. Treatment of
this syndrome is challenging because of the multiple interlinked factors that lead to increased
risks of type-2 diabetes and cardiovascular diseases. This study aims to conduct extensive in silico
analysis to (i) find central genes that play a pivotal role in MetS and (ii) propose suitable
drugs for therapy. Our objective is to first create a drug-disease network and then identify novel
genes in the drug-disease network with strong associations to drug targets, which can help in increasing
the therapeutical effects of different drugs. In the future, these novel genes can be used to
calculate drug synergy and propose new drugs for the effective treatment of MetS.
Methods: For this purpose, we (i) investigated associated drugs and pathways for MetS, (ii) employed
eight different similarity measures to construct eight gene regulatory networks, (iii) chose
an optimal network, where a maximum number of drug targets were central, (iv) determined central
genes exhibiting strong associations with these drug targets and associated disease-causing
pathways, and lastly (v) employed these candidate genes to propose suitable drugs.
Results: Our results indicated (i) a novel drug-disease network complex, with (ii) novel genes
associated with MetS.
Conclusion: Our developed drug-disease network complex closely represents MetS with associated
novel findings and markers for an improved understanding of the disease and suggested therapy.
