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.

基因表达的调节对于维持正常的细胞功能至关重要，其失调导致许多疾病和病症的分子发病机理。非编码RNA调节大约60％的蛋白质编码基因的表达，其功能失常导致多种疾病的发展。已经确定了循环非编码RNA的变体形式与疾病的关系。但是，它们在代谢疾病中作为生物标志物或治疗靶标的功能尚未得到充分利用。这项研究的目的是预测代谢综合征（MS）的早期检测的治疗目标和生物标志物面板的构建。非编码RNA，包括环状RNA（circRNA），长链非编码RNA（lncRNA）和微小RNA（miRNA）从大量文献检索和实验支持的数据库中提取。MS的基因表达谱的原始数据是从GEO数据集中获得的，并进行分析以获得差异表达的基因（DEG）。进行功能富集分析，非编码RNA的网络图示和预测的目标DEG。此外，对一些靶向DEG的miRNA进行了同源性研究。观察到hsa-miR-548c-3p，hsa-miR-579-3p，hsa-miR-17-5p和hsa-miR-320a与MS的发病密切相关。它包括调节葡萄糖和脂质稳态中的基因，MAPKK活性，调节炎症反应以及许多信号传导途径，例如胰岛素抵抗，JAK / STAT和mTOR。最后，hsa-miR-17-5p的相互作用：MS的基因表达谱的原始数据是从GEO数据集中获得的，并进行分析以获得差异表达的基因（DEG）。进行功能富集分析，非编码RNA的网络图示和预测的目标DEG。此外，对一些靶向DEG的miRNA进行了同源性研究。观察到hsa-miR-548c-3p，hsa-miR-579-3p，hsa-miR-17-5p和hsa-miR-320a与MS的发病密切相关。它包括调节葡萄糖和脂质体内稳态的基因，MAPKK活性，调节炎症反应以及许多信号传导途径，例如胰岛素抵抗，JAK / STAT和mTOR。最后，hsa-miR-17-5p的相互作用：MS的基因表达谱的原始数据是从GEO数据集中获得的，并进行分析以获得差异表达的基因（DEG）。进行功能富集分析，非编码RNA的网络图示和预测的目标DEG。此外，对一些靶向DEG的miRNA进行了同源性研究。观察到hsa-miR-548c-3p，hsa-miR-579-3p，hsa-miR-17-5p和hsa-miR-320a与MS的发病机理密切相关。它包括调节葡萄糖和脂质稳态中的基因，MAPKK活性，调节炎症反应以及许多信号传导途径，例如胰岛素抵抗，JAK / STAT和mTOR。最后，hsa-miR-17-5p的相互作用：进行功能富集分析，非编码RNA的网络图示和预测的目标DEG。此外，对一些靶向DEG的miRNA进行了同源性研究。观察到hsa-miR-548c-3p，hsa-miR-579-3p，hsa-miR-17-5p和hsa-miR-320a与MS的发病密切相关。它包括调节葡萄糖和脂质稳态中的基因，MAPKK活性，调节炎症反应以及许多信号传导途径，例如胰岛素抵抗，JAK / STAT和mTOR。最后，hsa-miR-17-5p的相互作用：进行功能富集分析，非编码RNA的网络图示和预测的目标DEG。此外，对一些靶向DEG的miRNA进行了同源性研究。观察到hsa-miR-548c-3p，hsa-miR-579-3p，hsa-miR-17-5p和hsa-miR-320a与MS的发病密切相关。它包括调节葡萄糖和脂质稳态中的基因，MAPKK活性，调节炎症反应以及许多信号传导途径，例如胰岛素抵抗，JAK / STAT和mTOR。最后，hsa-miR-17-5p的相互作用：在MS的发病机理中观察到了hsa-miR-17-5p和hsa-miR-320a。它包括调节葡萄糖和脂质体内稳态的基因，MAPKK活性，调节炎症反应以及许多信号传导途径，例如胰岛素抵抗，JAK / STAT和mTOR。最后，hsa-miR-17-5p的相互作用：在MS的发病机制中观察到了hsa-miR-17-5p和hsa-miR-320a。它包括调节葡萄糖和脂质稳态中的基因，MAPKK活性，调节炎症反应以及许多信号传导途径，例如胰岛素抵抗，JAK / STAT和mTOR。最后，hsa-miR-17-5p的相互作用：预测到STAT3，hsa-miR-320：JAK2，hsa-miR-320：S6K和hsa- let- 7：DVL杂种。这项研究的结果表明，设计一种生物标志物检测小组来检测MS的早期发作和分子治疗方法。