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.2019 Dec 27;17(1):112.
doi: 10.1186/s12958-019-0556-x.

Identification of diagnostic biomarkers in patients with gestational diabetes mellitus based on transcriptome gene expression and methylation correlation analysis

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Identification of diagnostic biomarkers in patients with gestational diabetes mellitus based on transcriptome gene expression and methylation correlation analysis

Enchun Li et al. Reprod Biol Endocrinol..

Abstract

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.

Keywords: Diagnostic markers; GDM; Methylation; Pathway; SVM.

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Conflict of interest statement

The authors declare no conflicts of interest in the paper.

Figures

Fig. 1
Fig. 1
The workflow of the present study
Fig. 2
Fig. 2
Identification of DEGs between GDM and healthy controls samples. (a) The box plot depicts the overall gene expression level of each sample after normalization (blue bars: normal sample, orange bar: GDM sample). (b) The volcano plot of DEGs. (c) The expression heatmap of DEGs
Fig. 3
Fig. 3
Functional enrichment analysis of 465 DEGs. (a) Enriched GO terms in the “biological process” category. (b) Enriched GO terms in the “cellular component” category. (c) Enriched GO terms in the “molecular function” category. (d) Enriched KEGG biological pathways. The x-axis represents the proportion of DEGs, and the y-axis represents different categories. The different colors indicate different properties, and the different sizes represent the number of DEGs
Fig. 4
Fig. 4
KEGG pathway gene interaction network analysis. (a) KEGG pathway gene interaction network. The colors indicated different fold-change. (b) The distribution of network degree. (c) The distribution of network methylation CpG sites in the promoter region
Fig. 5
Fig. 5
Identification of key epigenetics driven genes in GDM. (a) Gene-gene-CpG interaction network, in which the pink dot was methylated CpG, the blue dot represented the gene. (b) The degree distribution of the network. (c) The network Closeness distribution. (d) The network Betweenness distribution
Fig. 6
Fig. 6
Construction of diagnostic models and validation. (a) The classification result of the diagnostic model in the training data set, verification data set and GSE128381 data set. (b) The ROC curve of diagnostic model in the training data set, verification data set and GSE128381 data set. (c) The number of normal samples predicted by the prediction model in a thousand random normal samples. (d) Age distribution difference of pre-pregnancy between GDM samples and normal samples, and t-test was used to calculate the p value. (e) BMI distribution difference of pre-pregnancy between GDM samples and normal samples, and t-test was used to calculate thep value. (f) Relationship between model prediction results and OGTT diagnostic results
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