Hastalığa neden olan genlerin ağ analizi ile bulunması

Identifying the common molecular mechanisms for metabolic disorders is crucial for early diagnosis and targeted drug therapies. However, the bioinformatics studies aiming to reveal shared disease genes remained limited because of the challenges arose from the complexity of the metabolic pathways. In this respect, we suggested an integrative bioinformatics model that combines multiple biological data sources and computational methods to identify shared disease genes in metabolic syndrome (MS), type 2 diabetes (T2D), and coronary artery disease (CAD). We constructed weighted gene co-expression networks for each disease group by integrating peripheral blood gene expression data of 29 subjects, protein-protein interactions from STRING and INet, and Gene Ontologies. We clustered 90 disease networks, which are constructed using different parameters, by using MCL, SPICi, and Linkcomm algorithms and detected the disease modules. After comparatively evaluating the clustering results, we overlapped the networks providing the highest biological validity, and thus we obtained the common disease modules. Our analyses revealed 22 shared genes in total for MS–CAD and T2D–CAD pairs. Moreover, 19 out of these 22 genes are directly or indirectly associated with relevant diseases in the previous medical studies. This integrative network based gene-disease association study on MS, T2D, and CAD offers potential insights into the common genetic mechanisms of the metabolic and cardiometabolic disorders.