Understanding how to express genes involved in diabetes and study them with the help of bioinformatics

Abstract

Diabetes is a metabolic disease that results from the cessation of insulin production or its dysfunction in the body. The cause of this disease has not been discovered yet, but it is believed that favorable genetic background and environmental factors such as obesity have an effective role in the development of this disease. Based on a defect in insulin secretion or a defect in insulin function, the disease is divided into type 1 diabetes (T1D) and type 2 diabetes (T2D). In T1D, the pancreatic cells that produce insulin are destroyed and the body is unable to There is no insulin and the pancreas does not secrete any insulin. Bioinformatics is a powerful way to comprehensively analyze disease-related genes to identify genes in the disease. With the help of bioinformatics, we extracted common genes between samples with T1D and T2D. To do this, we first downloaded two datasets from the geo site and then processed them in the R environment. Then, to better understand the specificity of the genes, we performed KEGG GEO and network analyzes and identified the molecular mechanisms in which the genes were involved. In system biology, the network is drawn based on the genes that have the most expression changes in the disease. In terms of increasing expression and decreasing expression. These genes actually control the path of the disease. These genes are known as biomarkers in the diagnosis of diabetes.