A Bioinformatics Approach to Identify the Influences of Diabetes on the Progression of Cancers

Abstract

Diabetes is a communal illness with a tremendous impact on health and people with diabetes are at suggestively advanced risk for making many types of genetic dysfunction, particularly cancers like liver, uterine, lung, and colon cancer. To precise individuals with diabetes have specifically high levels of insulin in their blood, which could make for extra fertile grounds for cancer progression. The insulin injections intended at treating diabetes may also be influencing the hazard of cancers. To discourse these issues, we studied the computable frameworks to address the associations of diabetes and cancer datasets to identify the relationship between them. Diabetes is associated with cancers by sharing 22, 24, 15, and 25 DEGs with liver cancer, lung cancer, uterine cancer, and colon cancer respectively. Commonly DEGs, diseasome networks, pathways, ontological analysis indicate the suggestive relationship between diabetes with cancers. We investigated the datasets of transcript analyses made using microarray studies of diabetics and comorbidities as cancers, including datasets from liver, uterine, lung, and colon cancer. We erected diseasome networks and identified significant pathways, ontologies, and PPI sub-networks. This analysis validates the associations between diabetes on cancer progression and would be helpful to develop therapeutic strategies and comorbidities prediction.