A system biological approach to investigate the genetic profiling and comorbidities of type 2 diabetes

Background: Type 2 diabetes (T2D) is an interminable illness that makes many types of genetic dysfunction in the human body, particularly to the nerves, veins, kidneys, liver and uterus. T2D existence in a longer period of time may create the risk of building some comorbidities, however the indication of this risk is inadequately comprehended. To address this issue, we studied the transcriptomic data to recognize the link between T2D and its major comorbidities. Methods: We developed a quantitative model to investigate the genetic links between T2D and its significant comorbidities. We analyzed gene expression omnibus (GEO) microarray data from T2D, liver cancer (LC), endometrial cancer (EC), xanthoma (Xa), myocardial infarction (MI), embolic stroke (ES), kidney failure (KF), xerostomia (Xe) and control datasets. We constructed gene-disease association networks (GDN), identified signaling and ontological pathways, formed protein-protein interaction (PPI) network employing neighborhood based benchmarking and multilayer network topology. Results: We observed, T2D shared 22, 15, 18, 21, 12, 14 and 13 differentially expressed genes (DEGs) with LC, EC, Xa, MI, ES, KF, and Xe respectively. DEG investigation, signaling and ontological pathways, and PPI network suggest significant links between T2D and the advancement of LC, EC, Xa, MI, ES, KF, and Xe. Conclusions: Our systematic approach to identify the genetic links of T2D with the progression of LC, EC, Xa, MI, ES, KF, and Xe by understanding the causal influences of T2D may be helpful to develop therapeutic strategies for T2D and those comorbidities. This study will also be useful to predict comorbidities occurrence and build human awareness against the dangerous effects of T2D.