BACKGROUND Gestational diabetes mellitus (GDM) has a high prevalence in the period of pregnancy. However, the lack of gold standards in current screening and diagnostic methods posed the biggest limitation. Regulation of gene expression caused by DNA methylation plays an important role in metabolic diseases. In this study, we aimed to screen GDM diagnostic markers, and establish a diagnostic model for predicting GDM. METHODS First, we acquired data of DNA methylation and gene expression in GDM samples (N = 41) and normal samples (N = 41) from the Gene Expression Omnibus (GEO) database. After pre-processing the data, linear models were used to identify differentially expressed genes (DEGs). Then we performed pathway enrichment analysis to extract relationships among genes from pathways, construct pathway networks, and further analyzed the relationship between gene expression and methylation of promoter regions. We screened for genes which are significantly negatively correlated with methylation and established mRNA-mRNA-CpGs network. The network topology was further analyzed to screen hub genes which were recognized as robust GDM biomarkers. Finally, the samples were randomly divided into training set (N = 28) and internal verification set (N = 27), and the support vector machine (SVM) ten-fold cross-validation method was used to establish a diagnostic classifier, which verified on internal and external data sets. RESULTS In this study, we identified 465 significant DEGs. Functional enrichment analysis revealed that these genes were associated with Type I diabetes mellitus and immunization. And we constructed an interactional network including 1091 genes by using the regulatory relationships of all 30 enriched pathways. 184 epigenetics regulated genes were screened by analyzing the relationship between gene expression and promoter regions' methylation in the network. Moreover, the accuracy rate in the training data set was increased up to 96.3, and 82.1% in the internal validation set, and 97.3% in external validation data sets after establishing diagnostic classifiers which were performed by analyzing the gene expression profiles of obtained 10 hub genes from this network, combined with SVM. CONCLUSIONS This study provided new features for the diagnosis of GDM and may contribute to the diagnosis and personalized treatment of GDM.

中文翻译：

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基于转录组基因表达和甲基化相关性分析的妊娠糖尿病患者诊断生物标志物的鉴定。

背景技术妊娠期糖尿病（GDM）在怀孕期间具有很高的患病率。然而，当前的筛选和诊断方法缺乏金标准构成最大的限制。DNA甲基化引起的基因表达调控在代谢性疾病中起重要作用。在这项研究中，我们旨在筛选GDM诊断标记，并建立用于预测GDM的诊断模型。方法首先，我们从Gene Expression Omnibus（GEO）数据库中获得了GDM样本（N = 41）和正常样本（N = 41）中DNA甲基化和基因表达的数据。在对数据进行预处理之后，使用线性模型来识别差异表达基因（DEG）。然后，我们进行了途径富集分析，从途径中提取基因之间的关系，构建途径网络，并进一步分析了基因表达与启动子区域甲基化之间的关系。我们筛选了与甲基化显着负相关的基因，并建立了mRNA-mRNA-CpGs网络。对网络拓扑结构进行了进一步分析，以筛选被认为是强大的GDM生物标记物的集线器基因。最后，将样本随机分为训练集（N = 28）和内部验证集（N = 27），并使用支持向量机（SVM）十倍交叉验证方法建立诊断分类器，从而对样本进行验证。内部和外部数据集。结果在这项研究中，我们确定了465个重要的DEG。功能富集分析表明，这些基因与I型糖尿病和免疫相关。通过利用所有30条富集途径的调控关系，我们构建了一个包含1091个基因的相互作用网络。通过分析网络中基因表达与启动子区域甲基化之间的关系，筛选了184个表观遗传学调控的基因。此外，在通过分析获得的10个集线器的基因表达谱进行诊断分类后，训练数据集的准确率提高到96.3％，内部验证集的准确率提高到82.1％，外部验证数据集的准确率提高到97.3％该网络中的基因与SVM相结合。结论本研究为GDM的诊断提供了新的功能，可能有助于GDM的诊断和个性化治疗。通过分析网络中基因表达与启动子区域甲基化之间的关系，筛选了184个表观遗传学调控的基因。此外，在通过分析获得的10个集线器的基因表达谱进行诊断分类后，训练数据集的准确率提高到96.3％，内部验证集的准确率提高到82.1％，外部验证数据集的准确率提高到97.3％该网络中的基因与SVM相结合。结论本研究为GDM的诊断提供了新的功能，可能有助于GDM的诊断和个性化治疗。通过分析网络中基因表达与启动子区域甲基化之间的关系，筛选了184个表观遗传学调控的基因。此外，在通过分析获得的10个集线器的基因表达谱进行诊断分类后，训练数据集的准确率提高到96.3％，内部验证集的准确率提高到82.1％，外部验证数据集的准确率提高到97.3％该网络中的基因与SVM相结合。结论本研究为GDM的诊断提供了新的功能，可能有助于GDM的诊断和个性化治疗。建立诊断分类器后，内部验证集中的数据将占内部验证数据集中的1％，外部验证数据集中的数据将达到97.3％，这些数据是通过分析从该网络中获得的10个集线器基因的基因表达谱并结合SVM来进行的。结论本研究为GDM的诊断提供了新的功能，可能有助于GDM的诊断和个性化治疗。建立诊断分类器后，内部验证集中的数据将占内部验证数据集中的1％，外部验证数据集中的数据将达到97.3％，这些数据是通过分析从该网络中获得的10个集线器基因的基因表达谱并结合SVM来进行的。结论本研究为GDM的诊断提供了新的功能，可能有助于GDM的诊断和个性化治疗。