Screening of ulcerative colitis biomarkers and potential pathways based on weighted gene co-expression network, machine learning and ceRNA hypothesis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36419192", "kind": "science-record", "revision": 4, "content_hash": "sha256:f955901a439fc12f18a3b8d814aae7d6553f8562174df14f19bfb7eac2ffd8f1", "created_at": "2026-11-01T19:00:53Z", "updated_at": "2026-11-01T22:50:39Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Screening of ulcerative colitis biomarkers and potential pathways based on weighted gene co-expression network, machine learning and ceRNA hypothesis.", "doi": "10.1186/s41065-022-00259-4", "pmid": "36419192", "authors": ["Ying Li", "Mengyao Tang", "Feng Jun Zhang", "Yihan Huang", "Jing Zhang", "Junqi Li", "Yunpeng Wang", "Jinguang Yang", "Shu Zhu"], "journal": "Hereditas", "year": 2022, "abstract": "BACKGROUND: Ulcerative colitis (UC) refers to an intractable intestinal inflammatory disease. Its increasing incidence rate imposes a huge burden on patients and society. The UC etiology has not been determined, so screening potential biomarkers is critical to preventing disease progression and selecting optimal therapeutic strategies more effectively.\nMETHODS: The microarray datasets of intestinal mucosal biopsy of UC patients were selected from the GEO database, and integrated with R language to screen differentially expressed genes and draw proteins interaction network diagrams. GO, KEGG, DO and GSEA enrichment analyses were performed to explore their biological functions. Through machine learning and WGCNA analysis, targets that can be used as UC potential biomarkers are screened out. ROC curves were drawn to verify the reliability of the results and predicted the mechanism of marker genes from the aspects of immune cell infiltration, co-expression analysis, and competitive endogenous network (ceRNA).\nRESULTS: Two datasets GSE75214 and GSE87466 were integrated for screening, and a total of 107 differentially expressed genes were obtained. They were mainly related to biological functions such as humoral immune response and inflammatory response. Further screened out five marker genes, and found that they were associated with M0 macrophages, quiescent mast cells, M2 macrophages, and activated NK cells in terms of immune cell infiltration. The co-expression network found significant co-expression relationships between 54 miRNAs and 5 marker genes. According to the ceRNA hypothesis, NEAT1-miR-342-3p/miR-650-SLC6A14, NEAT1-miR-650-IRAK3, and XIST-miR-342-3p-IRAK3 axes were found as potential regulatory pathways in UC.\nCONCLUSION: This study screened out five biomarkers that can be used for the diagnosis and treatment of UC, namely SLC6A14, TIMP1, IRAK3, HMGCS2, and APOBEC3B. Confirmed that they play a role in the occurrence and development of UC at the level of immune infiltration, and proposed a potential RNA regulatory pathway that controls the progression of UC.", "claims": [{"id": "claim-method", "statement": "DEGs were obtained by filtering the sample data using the R language limma package (http://www.bioconductor.org/packages/release/bioc/html/limma.html).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36419192", "locator": "sections[4] \"Correction and screening of differentially expressed genes\" paragraph 2", "quote": "DEGs were obtained by filtering the sample data using the R language limma package (http://www.bioconductor.org/packages/release/bioc/html/limma.html)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The code and raw data used in the research have been uploaded to the Github database, https://github.com/782678245/Screening-of-ulcerative-colitis-biomarkers.git", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36419192", "locator": "sections[31] \"notes\" paragraph 1", "quote": "The code and raw data used in the research have been uploaded to the Github database, https://github.com/782678245/Screening-of-ulcerative-colitis-biomarkers.git"}], "state": "complete"}, {"id": "claim-target-result", "statement": "Two datasets GSE75214 and GSE87466 were integrated for screening, and a total of 107 differentially expressed genes were obtained.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36419192", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Two datasets GSE75214 and GSE87466 were integrated for screening, and a total of 107 differentially expressed genes were obtained."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Two datasets GSE75214 and GSE87466 were integrated for screening, and a total of 107 differentially expressed genes were obtained.", "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:36419192"], "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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