AUTHOR=Ghiam Shokoofeh , Eslahchi Changiz , Shahpasand Koorosh , Habibi-Rezaei Mehran , Gharaghani Sajjad 

TITLE=Exploring the role of non-coding RNAs as potential candidate biomarkers in the cross-talk between diabetes mellitus and Alzheimer’s disease

JOURNAL=Frontiers in Aging Neuroscience

VOLUME=Volume 14 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/aging-neuroscience/articles/10.3389/fnagi.2022.955461

DOI=10.3389/fnagi.2022.955461

ISSN=1663-4365

ABSTRACT=Background:
Recently, the progression of Diabetes Mellitus (DM) to Alzheimer's Disease (AD), also known as brain diabetes, has been investigated. Insulin resistance plays a major role in this cross-talk. Recent studies have focused on dysregulated proteins to overcome this relationship. Although protein dysregulation is a key factor in the development of many diseases, non-coding RNAs (ncRNAs) also play a significant role, as they not only encode the majority of the human genome but also influence the gene expression process via a variety of mechanisms. As a result, finding significant ncRNAs that could be used as biomarkers may support the early detection of this cross-talk. Computational-based methods, on the other hand, are very popular nowadays because they save time and money.
Method:
In this study, we retrieved Genome Wide Association Study (GWAS) results using searching keywords “Alzheimer” and “Diabetes Mellitus” from UK Biobank database. Statistical analysis was then performed and adjusted p-values were calculated after excluding low confidence variants, and 127 significant shared Single Nucleotide Polymorphism (SNP) were chosen. Next, using the Linkage Disequilibrium method, the SNP-SNP interaction network was built, and main cliques were extracted as signatures. By mapping each signature to the reference genome, genes associated with the selected SNPs were retrieved. Then, protein-microRNA (miRNA) and miRNA-long non-coding RNA (lncRNA) bipartite networks were built and significant miRNAs and lncRNAs were retrieved. After the validation process, by applying the scoring function, the final protein-miRNA-lncRNA tripartite network was constructed and significant miRNAs and lncRNAs were identified.
Result:
Hsa-miR-199a-5p, hsa-miR-199b-5p, hsa-miR-423-5p, and hsa-miR-3184-5p, the most significant miRNAs, as well as NEAT1, XIST, and KCNQ1OT1, the most significant lncRNAs, and their interacting proteins in the final tripartite network have been proposed as new candidate biomarkers for detecting the co-occurrence of DM and AD in early stages. The literature review also validates the obtained ncRNAs. In addition, miRNA/lncRNA pairs; hsa-miR-124-3p/KCNQ1OT1, hsa-miR-124-3p/NEAT1, and hsa-miR-124-3p/XIST, all expressed in the brain, and their interacting proteins in our final network are suggested for future research.