Deciphering the scalene association among type‐2 diabetes mellitus, prostate cancer, and chronic myeloid leukemia via enrichment analysis of disease‐gene network

Abstract

The potential biological relationship between type‐2 diabetes mellitus (T2DM) has been focused in numerous studies. To investigate the molecular associations among T2DM, prostate cancer (PCa), and chronic myeloid leukemia (CML), using a biomolecular network enrichment analysis. We obtained a list of disease‐related genes and constructed disease networks. Then, GO enrichment analysis was performed to identify the significant functions and pathways of overlapping modules in the Database for Annotation, Visualization and Integrated Discovery (DAVID) database. More than 75% of these overlapping genes were found to be consistent with the findings of previous studies. In the three diseases, we found that Sarcoglycan delta (SGCD) and Rho family GTPase 3 (RND3) were the overlapping genes and identified negative regulation of apoptotic process and negative regulation of transcription from RNA polymerase II promoter RNA as the two overlapping biological functions. CML and PCa were the most closely related, with 34 overlapping genes, five overlapping modules, 27 overlapping biological functions, and nine overlapping pathways. There were 13 overlapping genes, one overlapping modules, four overlapping biological functions and one overlapping pathway (FoxO signaling pathway) were found in T2DM and CML.And T2DM and PCa were the least related pair in our study, with only six overlapping genes, five overlapping modules, and one overlapping biological function. SGCD and RND3 were the main gene‐to‐gene relationship among T2DM, CML, and PCa; apoptosis, development, and transcription from RNA polymerase II promote processes were the main functional connections among T2DM, CML, and PCa by network enrichment analysis. There is a “scalene” relationship among T2DM, CML, and PCa at gene, pathway, biological process, and module levels: CML and PCa were the most closely related, the second were T2DM and PCa, and T2DM and PCa were the least related pair in our study. Our study provides a new avenue for further studies on T2DM and cancers, which may promote the discovery and development of novel therapeutic and can be used to treat multiple diseases.

Lack of Delta-Sarcoglycan (Sgcd) Results in Retinal Degeneration

“…This assumption is based on our earlier finding of SGCD ′s polymorphisms associated with GA/AMD. [8] A new bioinformatic report described Sgcd′s involvement in tissue regeneration, developmental growth, cell proliferation, and differentiation [20]. All these functions could explain our finding of a thinner frailer retina.…”

Modular networks and genomic variation during progression from stable angina pectoris through ischemic cardiomyopathy to chronic heart failure

“…For MCODE, the parameters (Node Score Cutoff = 0.2, Node Score Threshold = 0.2, Connectivity Threshold = 2, Degree Cutoff = 2, Core Threshold K = 2, Max. Depth = 100) were used as the criteria for network module screening (Liu et al 2019 ). We calculated the entropy of the network to select approach with the minimum entropy to divide the three disease-connected networks into modules (Chen et al 2021 ).…”

Deciphering the Protein, Modular Connections and Precision Medicine for Heart Failure With Preserved Ejection Fraction and Hypertension Based on TMT Quantitative Proteomics and Molecular Docking

“…HFpEF- and HTN-DEPs were submitted to the Database for annotation, visualization, and integrated discovery for functional enrichment, including GO and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses (version 6.8) 4 . The P -value was set at <0.05 for GO and KEGG pathway enrichment, as is standard in the field ( Yuan et al, 2016 ; Liu et al, 2019 ).…”

“…depth = 100). The parameters for Cytoscape were set as default, as recommended by previous studies ( Yuan et al, 2016 ; Liu et al, 2019 ).…”

“…Functional enrichment analysis of the overlapping modules reflects the functional links among related diseases ( Dean et al, 2017 ). For example, using this method, a previous study showed that the negative regulation of transcription from RNA polymerase II promoter RNA and the negative regulation of apoptotic processes are overlapping biological functions among type-2 diabetes mellitus, prostate cancer, and chronic myeloid leukemia ( Liu et al, 2019 ). Furthermore, another study showed that atherosclerosis, cholesterol homeostasis, plasma lipoprotein particle remodeling, and oxidative stress responses are common risk factors for stroke and coronary heart disease ( Zhang et al, 2014 ).…”