A data-driven biocomputing pipeline with meta-analysis on high throughput transcriptomics to identify genome-wide miRNA markers associated with type 2 diabetes

Abstract

Discovered a miRNA meta-signature associated with T2D via a data-driven pipeline. Validated in-silico findings against existing evidence and via downstream analyses. Meta-signature could help decode etiologic mechanisms and therapeutic targets of T2D. Broader utility of the pipeline for biomedical evidence synthesis is envisioned.

From Euglycemia to Recent Onset of Type 2 Diabetes Mellitus: A Proof-of-Concept Study on Circulating microRNA Profiling Reveals Distinct, and Early microRNA Signatures

“…Specifically, individual miRNA-disease association studies often suffer from insufficient statistical power due to small sample sizes, are susceptible to many kinds of experimental biases, and may lead to inconsistent results. Very recently, meta-analytic approaches operating on public high-throughput transcriptomic datasets in the NCBI Gene Expression Omnibus (GEO) repository have identified hsa-miR-1260a and hsa-miR-146a-5p as two out of nine potential circulating miRNA markers for incident T2D [61]. The findings we have observed in our study are partially congruent with the small signature of miRNAs that has been derived from the aforementioned meta-analysis [61].…”

“…Very recently, meta-analytic approaches operating on public high-throughput transcriptomic datasets in the NCBI Gene Expression Omnibus (GEO) repository have identified hsa-miR-1260a and hsa-miR-146a-5p as two out of nine potential circulating miRNA markers for incident T2D [61]. The findings we have observed in our study are partially congruent with the small signature of miRNAs that has been derived from the aforementioned meta-analysis [61]. However, meta-analytical investigations on circulating miRNAs are unavoidably influenced by the diverse characteristics of available high-throughput miRNA profiling datasets and the unequal quantities of dysregulated miRNAs in T2D patients compared to healthy subjects across different studies.…”

Gene expression analysis reveals diabetes-related gene signatures

“…Genomics strategies are put in place to overcome this limitation by integrating two or more types of genomic data to create a global network of biological interactions [ 6 ]. Data-driven research based on integrative genomics has the potential to unravel disease mechanisms and has become indispensable for the deep understanding of the physiological processes and complex mechanisms underlying disease onset and progression of heterogeneous diseases such as diabetes [ 9 , 12 , 13 ]. Large-scale studies of data compiled from different genomic studies can alleviate the biases of individual studies and unveil possible disease biomarkers and molecular targets amenable to therapeutic intervention [ 6 ].…”

“…Integrated genomics studies are still scarce in the context of diabetes [ 14 ], mainly in T1D. Concerning T2D, several studies using individual genomics approaches have been reported; however, these tend to be underpowered due to small sample sizes and lack of consensus on the protocols used, yielding inconsistent findings [ 9 ]. Notwithstanding, further integrative studies are still needed to compile data from complementary omics to add more pieces to the puzzle of the molecular mechanisms underlying diabetes pathogenesis [ 9 , 15 ].…”

“…They are involved in the regulation of different processes, including the cell cycle and cellular differentiation, with possible roles in multiple physiological and pathological processes, such as cell interactions, immune responses, and other cellular functions, via post-translational modifications. Recent studies have linked the role of some miRNAs and lncRNAs with diabetes pathogenesis in cell functions associated with insulin function, β-cell activity, and glucose metabolism [8][9][10]. Moreover, studies suggest that ncRNAs may serve as modulators and diagnostic markers of diabetic cardiovascular disease [11].…”