In silico analysis of non-coding RNAs and putative target genes implicated in metabolic syndrome

Regulation of gene expression is vital to maintain normal cellular functions and its dysregulation leads to molecular pathogenesis of many diseases and disorders. Non-coding RNAs regulate the expression of approximately 60% of protein-coding genes and their malfunction contribute to the development of numerous diseases. The involvement of variant forms of circulating non-coding RNAs in diseases has been established. However, their function as biomarkers or therapeutic targets in metabolic disorders are underexploited. The aim of this study was to predict therapeutic targets and construction of biomarker panel for early detection of metabolic syndrome (MS). Non-coding RNAs including circular RNAs (circRNAs), long chain non-coding RNAs (lncRNA) and micro RNAs (miRNAs) were extracted from intensive literature search and experimentally supported databases. Raw data of gene expression profiles of MS were obtained from the GEO dataset and analyzed to get differentially expressed genes (DEGs). Functional enrichment analysis, network illustration of non-coding RNAs and predicted target DEGs were performed. Furthermore, a few numbers of miRNAs targeted DEGs were subjected to homology study. The strong association of hsa-miR-548c-3p, hsa-miR-579-3p, hsa-miR-17-5p and hsa-miR-320a was observed with the pathogenesis of MS. It includes the regulation of genes in glucose and lipid homeostasis, MAPKK activity, regulation of inflammatory responses and many signaling pathways such as insulin resistance, JAK/STAT and mTOR. Finally, interactions of hsa-miR-17-5p:STAT3, hsa-miR-320:JAK2, hsa-miR-320:S6K and hsa-let-7:DVL hybrids were predicted. Results of this study suggest the designing of a biomarker panel to detect early onset and molecular approach for the management of MS.