Bioinformatics approach to analyze gene expression profile and comorbidities of gastric cancer

Gastric cancer is a cancer in which malignant cell develops in the inner lining of stomach. The mortality risk of gastric cancer increases when several related diseases arise with it as comorbidity. Nowadays, the severities of gastric cancer and its comorbidity is a global health concern. To deal with this issue we implemented a standard analytical approach to identify the genetic profiling of gastric cancer and its comorbidities. For analyzing the genetic profile we used the mRNA-seq and gene expression microarray data from gastric cancer, diabetes, liver cancer, stroke, kidney disease infected tissues and control datasets. We constructed gene disease relationship networks, analyzed pathways, ontologies, protein-protein interaction network of the differentially expressed genes employing neighborhood base benchmarking and multilayer network topology, and finally, we validated our work using two gold benchmark databases (OMIM and dbGAP). We observed that gastric cancer shared 29, 29, 51, and 65 differentially expressed genes with diabetes, liver cancer, stroke, and kidney disease respectively. After analyzing gene expressions, pathways, ontologies and protein-protein interactions, it can be said that there is an association of gastric cancer with diabetes, liver cancer, stroke and kidney disease as comorbidities. This study may be helpful to identify the accurate comorbidities and it can raise awareness about the threatening consequences of gastric cancer among the peoples.