ArticleReproductive biology and endocrinology : RB&E2019
Identification of diagnostic biomarkers in patients with gestational diabetes mellitus based on transcriptome gene expression and methylation correlation analysis.
Article in Reproductive biology and endocrinology : RB&E, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed, 19 citations in OpenAlex.
- Demethylation of leptin promoter in gestational diabetes mellitus: evidence from a mouse model.Frontiers in cell and developmental biology · 2026Article
- Artificial Intelligence in Gestational Diabetes Care: A Systematic Review.Journal of diabetes science and technology · 2025Review
- SDC1 and ITGA2 as novel prognostic biomarkers for PDAC related to IPMN.Scientific reports · 2023Article
- Epigenetic marks associated with gestational diabetes mellitus across two time points during pregnancy.Clinical epigenetics · 2023Article
- Evidence for human milk as a biological system and recommendations for study design-a report from "Breastmilk Ecology: Genesis of Infant Nutrition (BEGIN)" Working Group 4.The American journal of clinical nutrition · 2023Article
- Article
- UHPLC-MS/MS-Based Metabolomics and Clinical Phenotypes Analysis Reveal Broad-Scale Perturbations in Early Pregnancy Related to Gestational Diabetes Mellitus.Disease markers · 2022Article
- A comprehensive survey on computational learning methods for analysis of gene expression data.Frontiers in molecular biosciences · 2022Review
- Reproductive history and blood cell DNA methylation later in life: the Young Finns Study.Clinical epigenetics · 2021Article
- Study on the relationship between DNA methylation of target CpG sites in peripheral blood and gestational diabetes during early pregnancy.Scientific reports · 2021Article
- Data-Driven Modeling of Pregnancy-Related Complications.Trends in molecular medicine · 2021Review
- Identification of Key Candidate Genes and Chemical Perturbagens in Diabetic Kidney Disease Using Integrated Bioinformatics Analysis.Frontiers in endocrinology · 2021Article
- Identification of Diagnostic CpG Signatures in Patients with Gestational Diabetes MellitusBioMed research international · 2021Article
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundGestational 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.
methodsFirst, 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.
resultsIn 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.
conclusionsThis study provided new features for the diagnosis of GDM and may contribute to the diagnosis and personalized treatment of GDM.
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