Just like a well-choreographed dance, the intricate interaction between diabetes mellitus (DM) and Alzheimer’s disease (AD) involves a crucial player called insulin resistance. Recent research has turned its attention to a new set of dancers – non-coding RNAs (ncRNAs). These molecular partners, found throughout the human genome, regulate gene expression through various mechanisms. By pinpointing significant ncRNAs, scientists hope to detect early stages of this tangled dance between DM and AD. To unravel their moves, computational-based methods are being used to map out relationships between different molecules and spark new wet laboratory experiments. A study utilizing Genome-Wide Association Study (GWAS) data from the United Kingdom Biobank database identified shared single nucleotide polymorphisms (SNPs) and built an intricate protein-microRNA-lncRNA network. The stars of this dance floor included miRNAs such as hsa-miR-199a-5p and hsa-miR-423-5p, as well as lncRNAs like NEAT1 and XIST. These molecules, along with their accomplice proteins, have been proposed as potential biomarkers for the DM and AD cross-talk. With further exploration, other brain-specific miRNA/lncRNA pairs may uncover new dimensions to this complex dance routine.

BackgroundRecent research has investigated the connection between Diabetes Mellitus (DM) and Alzheimer’s Disease (AD). Insulin resistance plays a crucial role in this interaction. Studies have focused on dysregulated proteins to disrupt this connection. Non-coding RNAs (ncRNAs), on the other hand, play an important role in the development of many diseases. They encode the majority of the human genome and regulate gene expression through a variety of mechanisms. Consequently, identifying significant ncRNAs and utilizing them as biomarkers could facilitate the early detection of this cross-talk. On the other hand, computational-based methods may help to understand the possible relationships between different molecules and conduct future wet laboratory experiments.Materials and methodsIn this study, we retrieved Genome-Wide Association Study (GWAS, 2008) results from the United Kingdom Biobank database using the keywords “Alzheimer’s” and “Diabetes Mellitus.” After excluding low confidence variants, statistical analysis was performed, and adjusted p-values were determined. Using the Linkage Disequilibrium method, 127 significant shared Single Nucleotide Polymorphism (SNP) were chosen and the SNP-SNP interaction network was built. From this network, dense subgraphs 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 ncRNAs were extracted. 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.ResultsHsa-miR-199a-5p, hsa-miR-199b-5p, hsa-miR-423-5p, and hsa-miR-3184-5p, the four most significant miRNAs, as well as NEAT1, XIST, and KCNQ1OT1, the three most important lncRNAs, and their interacting proteins in the final tripartite network, have been proposed as new candidate biomarkers in the cross-talk between DM and AD. 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 investigation.ConclusionThis study identified 127 shared SNPs, 7 proteins, 15 miRNAs, and 11 lncRNAs involved in the cross-talk between DM and AD. Different network analysis and scoring function suggested the most significant miRNAs and lncRNAs as potential candidate biomarkers for wet laboratory experiments. Considering these candidate biomarkers may help in the early detection of DM and AD co-occurrence.

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