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

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:34104645", "kind": "science-record", "revision": 7, "content_hash": "sha256:1fae3536aa5532c9f958ffbe451f0f2b4b00ae127a4543c3b126f319cbc4c517", "created_at": "2026-11-01T15:41:38Z", "updated_at": "2026-11-01T22:03:55Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of Diagnostic CpG Signatures in Patients with Gestational Diabetes Mellitus via Epigenome-Wide Association Study Integrated with Machine Learning.", "doi": "10.1155/2021/1984690", "pmid": "34104645", "authors": ["Yan Liu", "Hui Geng", "Bide Duan", "Xiuzhi Yang", "Airong Ma", "Xiaoyan Ding"], "journal": "BioMed research international", "year": 2021, "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.\nMETHODS: 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.\nRESULTS: 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.\nCONCLUSION: 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.", "claims": [{"id": "claim-method", "statement": "The epigenome-wide association study was carried out to investigate the relationship of methylation levels of CpGs and the GDM by using a CpGassoc R package [35, 36]. Briefly, The CpGassoc function established fixed or mixed-effects models between the GDM and methylation of individual CpG sites across the genome using a matrix or data frame of β-values as input. The significance was assessed by chi-squared tests, and the P values were calculated.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34104645", "locator": "sections[5] \"2.3. Epigenome-Wide Association Study (EWAS)\" paragraph 2", "quote": "The epigenome-wide association study was carried out to investigate the relationship of methylation levels of CpGs and the GDM by using a CpGassoc R package [35, 36]. Briefly, The CpGassoc function established fixed or mixed-effects models between the GDM and methylation of individual CpG sites across the genome using a matrix or data frame of β-values as input. The significance was assessed by chi-squared tests, and the P values were calculated."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Our data, DNA methylation data, and related clinical information were obtained from the GEO database (http://www.ncbi.nlm.nih.gov/geo/).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34104645", "locator": "sections[18] \"Data Availability\" paragraph 2", "quote": "Our data, DNA methylation data, and related clinical information were obtained from the GEO database (http://www.ncbi.nlm.nih.gov/geo/)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": null, "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:34104645"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE102177"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE102177"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:34104645", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1155/2021/1984690", "sha256": 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