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

Abstract

Background. Gestational diabetes mellitus (GDM) is the most prevalent metabolic disease during pregnancy, but the diagnosis is controversial and lagging partly due to the lack of useful biomarkers. CpG methylation is involved in the development of GDM. However, the specific CpG methylation sites serving as diagnostic biomarkers of GDM remain unclear. Here, we aimed to explore CpG signatures and establish the predicting model for the GDM diagnosis. Methods. DNA methylation data of GSE88929 and GSE102177 were obtained from the GEO database, followed by the epigenome-wide association study (EWAS). GO and KEGG pathway analyses were performed by using the clusterProfiler package of R. The PPI network was constructed in the STRING database and Cytoscape software. The SVM model was established, in which the β-values of selected CpG sites were the predictor variable and the occurrence of GDM was the outcome variable. Results. We identified 62 significant CpG methylation sites in the GDM samples compared with the control samples. GO and KEGG analyses based on the 62 CpG sites demonstrated that several essential cellular processes and signaling pathways were enriched in the system. A total of 12 hub genes related to the identified CpG sites were found in the PPI network. The SVM model based on the selected CpGs within the promoter region, including cg00922748, cg05216211, cg05376185, cg06617468, cg17097119, and cg22385669, was established, and the AUC values of the training set and testing set in the model were 0.8138 and 0.7576. The AUC value of the independent validation set of GSE102177 was 0.6667. Conclusion. We identified potential diagnostic CpG signatures by EWAS integrated with the SVM model. The SVM model based on the identified 6 CpG sites reliably predicted the GDM occurrence, contributing to the diagnosis of GDM. Our finding provides new insights into the cross-application of EWAS and machine learning in GDM investigation.

Batch-effect detection, correction and characterisation in Illumina HumanMethylation450 and MethylationEPIC BeadChip array data

“…S14). Finally, in a recently published re-analysis of existing gestational diabetes mellitus (GDM) EWAS data, Liu et al [ 42 ] found cg22385669 to be one of 62 significant CpG methylation sites and 1 of the 6 probes in their SVM model predicting GDM occurrence. This probe has a more subtle batch-effect than the previous two examples but is still readily apparent (Additional file 14 : Fig.…”

Methods in DNA methylation array dataset analysis: A review

“…The selected features are subsequently fed into a Support Vector Machine (SVM) classifier for model training. This model used the β-values of CpG sites derived from EWAS, as the predictor variable for predicting the diagnosis of Gestational Diabetes Mellitus [134] . While SVMs offer advantages such as handling nonlinear relationships, robustness in high-dimensional spaces, and effectiveness with small sample sizes, they do have limitations [135] .…”

“… Cons: Limited to binary class problem and needs further optimization. [134] 10. The TCGA platform was used for downloading level 3 DNA methylation data and clinical data.…”