Identification of diagnostic biomarker in patients with gestational diabetes mellitus based on transcriptome‐wide gene expression and pattern recognition

Abstract

Gestational diabetes mellitus (GDM) is becoming a growing threat for all pregnancies. In this study, we set up an automatic screening method combining both transcriptomic databases and support vector machine (SVM)-based pattern recognition to select biomarkers that can be used in predicting and preventing GDM for gravidas. We screened 63 samples (32 GDM samples and 31 normal controls) in GEO database for the GDM-specific biomarkers. Differentially expressed genes between patients with GDM and normal controls were picked out using edgeR package. Enrichment analysis was performed using database for annotation, visualization, and integrated discovery. The regulatory gene network was constructed based on the KEGG pathway database. Genes in the hub of the network were selected as specific biomarkers of GDM and further validated through document investigation. Finally, the GDM prediction model was verified using the SVMs. In total, 189 probes corresponding to 69 genes that differentially expressed between GDM and controls were screened out by edgeR package. Nineteen pathways were clustered by KEGG enrichment analysis and were integrated into a regulatory network containing 572 nodes and 1874 edges. The intersection of 50 hub genes extracted from the network and 69 differential genes picked out by edgeR was a collection of six genes, including members of HLA superfamily. In the SVM model, the six genes had a good capacity of predicting GDM in both the training data set (area under curve [AUC] is 0.781) and the testing data set (AUC is 0.710) and had been reported to be associated with GDM. We found that the collection of six genes can be potentially applied as a biomarker for GDM diagnosis.

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

“…Therefore, early diagnosis and personalized medical intervention of GDM are of great significance. Previously, Wang et al [41] has established a diagnostic model by using six gene expression profiles, but the AUC was relatively low. In this study, the SVM was used which based on 10 hub genes for GDM.…”

Identification of potential gene markers in gestational diabetes mellitus

“…Wang et al carried out a bioinformatics analysis based on a gene expression profile from GDM samples. Consequently, they established a promising screening approach for GDM and multiple members of human leukocyte antigen (HLA) superfamily were associated with GDM 7 . Deng et al 8 pointed out that antigen processing pathway and immune‐associated genes played key roles in GDM progression according to an integrated analysis of gene expression and methylation data which was generated from visceral omental adipose tissue of numerous Chinese pregnancies.…”

Identification of Diagnostic CpG Signatures in Patients with Gestational Diabetes Mellitus via Epigenome-Wide Association Study Integrated with Machine Learning

“…Previous studies showed that SVM is widely used in the prognosis and diagnosis of multiple diseases such as cancer and diabetes mellitus [29][30][31]. Moreover, it has been revealed that SVM is applied in the investigation of diagnostic markers in GDM patients based on transcriptome-wide gene expression [32]. However, the practices of SVM models in the identification of CpG methylation biomarkers in the diagnosis of GDM remain unreported.…”