Convergence and divergence of genetic and modular networks between diabetes and breast cancer

Association between diabetes status and breast cancer in US adults: findings from the US National Health and Nutrition Examination Survey

Objectives

The aim of this study was to investigate the association between diabetes status and the risk of breast cancer among adult Americans, exploring the impact of BMI, age, and race on this relationship.

Methods

A cross-sectional analysis of 8,249 individuals from the National Health and Nutrition Examination Survey (NHANES) was conducted. Diabetes was categorized as type 2 diabetes and prediabetes, with both conditions diagnosed according to the ADA 2014 guidelines. The association between diabetes status and breast cancer risk was explored using multiple logistic regression analysis.

Results

Patients with diabetes had higher odds of breast cancer (OR: 1.51; 95% CI 1.00 to 2.28), Using the two-piecewise linear regression model, it was observed that there is a threshold effect in the risk of breast cancer occurrence at the age of 52 years. Specifically, the risk of breast cancer is relatively low before the age of 52 but increases significantly after this age.

Conclusions

This study identified a significant association between diabetes status and breast cancer risk among adult Americans. We also found a threshold effect in breast cancer occurrence at the age of 52. Age was significantly associated with breast cancer risk in both Non-Hispanic White and Non-Hispanic Black individuals. These findings underscore the importance of diabetes management, maintaining a healthy BMI, and age-related risk considerations in reducing breast cancer risk.

Revealing the Modular Similarities and Differences Among Alzheimer's Disease, Vascular Dementia, and Parkinson's Disease in Genomic Networks

Alzheimer's disease (AD), vascular dementia (VD), and Parkinson's disease (PD) exert increasingly lethal or disabling effects on humans, but the associations among these diseases at the molecular level remain unclear. In our research, lists of genes related to these three diseases were acquired from public databases. We constructed gene-gene networks of the lists of disease-related genes using the STRING database and selected the plug-in MCODE as the most suitable method to divide the three disease-associated networks into modules through an entropy calculation. Notably, 1173 AD-related, 203 VD-related, and 722 PD-related genes as well as 72 overlapping genes were observed among the three diseases. By dividing the modules from the gene network, we divided the AD-related gene network into 27 modules, the VD-related gene network into 8 modules, and the PD-related gene network into 17 modules. After the enrichment analysis of each disease-related gene, 146 overlapping biological processes and 32 overlapping pathways were identified. Ultimately, through similarity analysis of the genes, biological processes, and pathways, we found that AD and VD were the most closely related at the biological process and pathway levels, with similarity coefficients of 0.2784 and 0.3626, respectively. After analyzing the overlapping gene network, we found that INS might play an important role in the network and that insulin and its signaling pathways may play a key role in these neurodegenerative diseases. Our research illustrates a new method for in-depth research on the three diseases, which may accelerate the progress of developing new therapeutics and may be applied to prevent neurodegenerative diseases.

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

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.