Statistical Bioinformatics to Uncover the Underlying Biological Mechanisms That Linked Smoking with Type 2 Diabetes Patients Using Transcritpomic and GWAS Analysis

• Published in: Molecules (Basel, Switzerland) Switzerland), 2022-07, Vol.27 (14), p.4390
• Main Authors: Ripon Rouf, Abu Sayeed Md, Amin, Md. Al, Islam, Md. Khairul, Haque, Farzana, Ahmed, Kazi Rejvee, Rahman, Md. Ataur, Islam, Md. Zahidul, Kim, Bonglee
• Format: Article
• Language: English
• Subjects: association; Beta cells; Bioinformatics; Biomarkers; Complications; Datasets; Diabetes; Diabetes mellitus; Diabetes mellitus (non-insulin dependent); Disease; DNA methylation; Gene expression; Genes; GSEA; High density lipoprotein; Insulin; Insulin resistance; Metabolic disorders; Ontology; pathway; Post-transcription; Proteins; Smoking; Statistical methods; Statistics; Transcriptomics; Triglycerides; Type-2 diabetes; Vitamin deficiency
• ISSN: 1420-3049; 1420-3049
• DOI: 10.3390/molecules27144390

Type 2 diabetes (T2D) is a chronic metabolic disease defined by insulin insensitivity corresponding to impaired insulin sensitivity, decreased insulin production, and eventually failure of beta cells in the pancreas. There is a 30–40 percent higher risk of developing T2D in active smokers. Moreover, T2D patients with active smoking may gradually develop many complications. However, there is still no significant research conducted to solve the issue. Hence, we have proposed a highthroughput network-based quantitative pipeline employing statistical methods. Transcriptomic and GWAS data were analysed and obtained from type 2 diabetes patients and active smokers. Differentially Expressed Genes (DEGs) resulted by comparing T2D patients’ and smokers’ tissue samples to those of healthy controls of gene expression transcriptomic datasets. We have found 55 dysregulated genes shared in people with type 2 diabetes and those who smoked, 27 of which were upregulated and 28 of which were downregulated. These identified DEGs were functionally annotated to reveal the involvement of cell-associated molecular pathways and GO terms. Moreover, protein–protein interaction analysis was conducted to discover hub proteins in the pathways. We have also identified transcriptional and post-transcriptional regulators associated with T2D and smoking. Moreover, we have analysed GWAS data and found 57 common biomarker genes between T2D and smokers. Then, Transcriptomic and GWAS analyses are compared for more robust outcomes and identified 1 significant common gene, 19 shared significant pathways and 12 shared significant GOs. Finally, we have discovered protein–drug interactions for our identified biomarkers.