Table_4_Identification of a Multi–Long Noncoding RNA Signature for the Diagnosis of Type 1 Diabetes Mellitus.XLSX

Due to the increasing prevalence of type 1 diabetes mellitus (T1DM) and its complications, there is an urgent need to identify novel methods for predicting the occurrence and understanding the pathogenetic mechanisms of the disease. Accumulated data have demonstrated the potential of long noncoding RNAs (lncRNAs), as biomarkers in establishing diagnosis and predicting prognosis of numerous diseases. Yet, little is known about the expression patterns and regulatory roles of lncRNAs in the pathogenesis of T1DM and whether they can be used as diagnostic biomarkers for the disease. To further explore these questions, in the present study, we conducted a comparative analysis of the expression patterns of lncRNAs between 20 T1DM patients and 42 health controls by retrospectively analyzing a published microarray data set. Our results indicate that, compared with healthy controls, diabetic patients had altered levels of lncRNAs. Then, we used three time cross-validation strategy and support vector machine to propose a specific 26-lncRNA signature (termed 26LncSigT1DM). This 26LncSigT1DM signature can be used to effectively distinguish between healthy and diabetic individuals (area under the curve = 0.825) of a validation cohort. After the 26LncSigT1DM was prospectively validated, we used Pearson correlation to identify 915 mRNAs, whose expression levels were positively correlated with those of the 26 lncRNAs. According to their Gene Ontology annotations, these mRNAs participate in processes including cellular response to stimulus, cell communication, multicellular organismal process, and cell motility. Kyoto Encyclopedia of Genes and Genomes analysis demonstrated that the genes encoding the 915 mRNAs may be associated with the NOD-like receptor signaling pathway, transforming growth factor β signaling pathway, and mineral absorption, suggesting that the deregulation of these lncRNAs may mediate inflammatory abnormalities and immune dysfunctions, which jointly promote the pathogenesis of T1DM. Thus, our study identifies a novel diagnostic tool and may shed more light on the molecular mechanisms underlying the pathogenesis of T1DM.

鉴于1型糖尿病（type 1 diabetes mellitus, T1DM）及其并发症的患病率持续攀升，当前亟需探索新方法以预测该疾病的发生并阐明其致病机制。现有研究积累的数据表明，长链非编码RNA（long noncoding RNA, lncRNA）作为生物标志物，在多种疾病的诊断建立与预后预测中具有应用潜力。然而，目前对于lncRNA在1型糖尿病发病过程中的表达模式与调控作用，以及其能否作为该疾病的诊断生物标志物，仍知之甚少。为进一步探究上述问题，本研究通过回顾性分析已公开的微阵列数据集，对20名1型糖尿病患者与42名健康对照者的lncRNA表达模式开展对比分析。研究结果显示，与健康对照者相比，糖尿病患者体内的lncRNA表达水平存在显著异常。随后，本研究采用三次交叉验证策略与支持向量机，构建了一款特异性26长链非编码RNA标志物（命名为26LncSigT1DM）。该26LncSigT1DM标志物可在验证队列中有效区分健康个体与糖尿病患者（曲线下面积=0.825）。在对26LncSigT1DM完成前瞻性验证后，本研究通过皮尔逊相关分析筛选出915个mRNA，其表达水平与该26个lncRNA的表达呈正相关。根据基因本体（Gene Ontology, GO）注释信息，这些mRNA参与的生物学过程包括细胞对刺激的应答、细胞通讯、多细胞生物过程以及细胞运动。京都基因与基因组百科全书（Kyoto Encyclopedia of Genes and Genomes, KEGG）富集分析显示，编码这915个mRNA的基因可能与NOD样受体信号通路、转化生长因子β信号通路以及矿物质吸收过程相关。这表明上述lncRNA的表达失调可能介导炎症异常与免疫功能紊乱，二者共同促进1型糖尿病的发病进程。综上，本研究鉴定出一款新型诊断工具，可为阐明1型糖尿病发病的潜在分子机制提供新的视角。