Construction of a novel miRNA regulatory network and identification of target genes in gestational diabetes mellitus by integrated analysis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36544488", "kind": "science-record", "revision": 4, "content_hash": "sha256:73c30a1d706c77e6adb27a78f2742f93a56ea1f4ed2fb792d80c749aa75bf358", "created_at": "2026-11-01T19:01:04Z", "updated_at": "2026-11-01T22:50:53Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Construction of a novel miRNA regulatory network and identification of target genes in gestational diabetes mellitus by integrated analysis.", "doi": "10.3389/fgene.2022.966296", "pmid": "36544488", "authors": ["Liyan Ding", "Yi Shen", "Anqi Wang", "Changlian Lu", "Xuefeng Gu", "Liying Jiang"], "journal": "Frontiers in genetics", "year": 2022, "abstract": "Backgrounds: Given the roles of microRNA (miRNA) in human diseases and the high incidence of gestational diabetes mellitus (GDM), the aim of the study was to examine miRNA signatures and crucial pathways, as well as possible biomarkers for GDM diagnosis. Methods: We conducted a two-stage study to explore functional miRNA and those target genes. Twelve participants (6 GDM and 6 non-GDM) were first enrolled and performed RNA sequencing analysis. The overlapped candidate genes were further screened in combination with differentially expressed genes (DEGs) of GEO datasets (GSE87295, GSE49524 and GSE19649) and potential target genes of DEMs. Candidate genes, critical pathways, small molecular compounds and regulatory networks were identified using bioinformatic analysis. The potential candidate genes were then investigated using the GEO dataset (GSE103552) of 19 participants in the validation stage (11 GDM and 8 non-GDM women). Results: Briefly, blood samples were sequenced interrogating 50 miRNAs, including 20 upregulated and 30 downregulated differentially expressed microRNAs(DEMs) in our internal screening dataset. After screening GEO databases, 123 upregulated and 70 downregulated genes were overlapped through DEGs of GEO datasets and miRNA-target genes. MiR-29b-1-5p-TGFB2, miR-142-3p-TGFB2, miR-9-5p-FBN2, miR-212-5p-FBN2, miR-542-3p-FBN1, miR-9-5p-FBN1, miR-508-3p-FBN1, miR-493-5p-THBS1, miR-29b-3p-COL4A1, miR-432-5p-COL5A2, miR-9-5p-TGFBI, miR-486-3p-SLC7A5 and miR-6515-5p-SLC1A5 were revealed as thirteen possible regulating pathways by integrative analysis. Conclusion: Overall, thirteen candidate miRNA-target gene regulatory pathways representing potentially novel biomarkers of GDM diseases were revealed. Ten chemicals were identified as putative therapeutic agents for GDM. This study examined a series of DEGs that are associated with epigenetic alternations of miRNA through an integrated approach and gained insight into biological pathways in GDM. Precise diagnosis and therapeutic targets of GDM would be further explored through putative genes in the future.", "claims": [{"id": "claim-method", "statement": "We identified differentially expressed genes (DEGs) for each of three datasets separately by using linear models for microarray (LIMMA) approach through GEO2R online tool (https://www.ncbi.nlm.nih.gov/geo/geo2r/).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36544488", "locator": "sections[6] \"Data collection and identification of overlapped candidate genes\" paragraph 3", "quote": "We identified differentially expressed genes (DEGs) for each of three datasets separately by using linear models for microarray (LIMMA) approach through GEO2R online tool (https://www.ncbi.nlm.nih.gov/geo/geo2r/)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in the study are publicly available. This data can be found here: https://www.ncbi.nlm.nih.gov/geo/ accession number: GSE218696.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36544488", "locator": "sections[21] \"Data availability statement\" paragraph 2", "quote": "The original contributions presented in the study are publicly available. This data can be found here: https://www.ncbi.nlm.nih.gov/geo/ accession number: GSE218696."}], "state": "complete"}, {"id": "claim-target-result", "statement": "After screening internal samples and three GEO datasets, 193 overlapped candidate genes were identified, including 123 upregulated and 70 downregulated genes.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36544488", "locator": "sections[14] \"Identification of candidate genes\" paragraph 3", "quote": "After screening of internal 12 samples and 3 GEO databases, the comprehensive datasets shared 193 overlapped candidate genes (123 upregulated genes and 70 downregulated genes)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "After screening internal samples and three GEO datasets, 193 overlapped candidate genes were identified, including 123 upregulated and 70 downregulated genes.", "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:36544488"], "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. 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