Identification of Potential Biomarkers and Immune Infiltration Characteristics in Ulcerative Colitis by Combining Results from Two Machine Learning Algorithms.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35959356", "kind": "science-record", "revision": 5, "content_hash": "sha256:ca722c2545fcaf7ce03e54eb37abc2654a61235b4b84709c50a1e8a0425331bb", "created_at": "2026-11-01T18:15:04Z", "updated_at": "2026-11-01T22:49:46Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of Potential Biomarkers and Immune Infiltration Characteristics in Ulcerative Colitis by Combining Results from Two Machine Learning Algorithms.", "doi": "10.1155/2022/5412627", "pmid": "35959356", "authors": ["Minchun Bu", "Xiandong Cao", "Bo Zhou"], "journal": "Computational and mathematical methods in medicine", "year": 2022, "abstract": "Objective: This study was designed to identify potential biomarkers for ulcerative colitis (UC) and analyze the immune infiltration characteristics in UC.\nMethods: Datasets containing human UC and normal control tissues (GSE87466, GSE107597, and GSE13367) were downloaded from the GEO database. Then, the GSE87466 and GSE107597 datasets were merged, and the differentially expressed genes (DEGs) between UC and normal control tissues were screened out by the \"limma R\" package. The LASSO regression model and support vector machine recursive feature elimination (SVM-RFE) were performed to screen out the best biomarkers. The GSE13367 dataset was used as a validation cohort, and the receiver operating characteristic curve (ROC) was used to evaluate the diagnostic performance. Finally, the immune infiltration characteristics in UC were explored by CIBERSORT, and we further analyzed the correlation between potential biomarkers and different immune cells.\nResults: A total of 76 DEGs were screened out, among which 56 genes were upregulated and 20 genes were downregulated. Functional enrichment analysis revealed that these DEGs were mainly involved in immune response, chemokine signaling, IL-17 signaling, cytokine receptor interactions, inflammatory bowel disease, etc. ABCG2, HSPB3, SLC6A14, and VNN1 were identified as potential biomarkers for UC and validated in the GSE13367 dataset (AUC = 0.889, 95% CI: 0.797~0.961). Immune infiltration analysis by CIBERSORT revealed that there were significant differences in immune infiltration characteristics between UC and normal control tissues. A high level of memory B cells, γδ T cells, activated mast cells, M1 macrophages, neutrophils, etc. were found in the UC group, while a high level of M2 type macrophages, resting mast cells, eosinophils, CD8+ T cells, etc. were found in the normal control group.\nConclusion: ABCG2, HSPB3, SLC6A14, and VNN 1 were identified as potential biomarkers for UC. There was an obvious difference in immune infiltration between UC and normal control tissues, which may provide help to guide individualized treatment and develop new research directions.", "claims": [{"id": "claim-method", "statement": "Using false discovery rate (FDR) < 0.05 and fold change (FC) ≥ 2 or ≤-2 as test criteria, the “limma R” package was used to detect differentially expressed genes.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35959356", "locator": "sections[4] \"2.2. Identification of DEGs and Functional Enrichment Analysis\" paragraph 2", "quote": "Using false discovery rate (FDR) < 0.05 and fold change (FC) ≥ 2 or ≤-2 as test criteria, the “limma R” package was used to detect differentially expressed genes."}], "state": "complete"}, {"id": "claim-data-access", "statement": "All raw data of our research are downloaded from the NCBI Gene Expression Synthesis (GEO) database(https://www.ncbi.nlm.nih.gov/geo/), which are publicly available databases. Accession numbers: GSE87466, GSE107597, and GSE13367.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35959356", "locator": "sections[18] \"Data Availability\" paragraph 2", "quote": "All raw data of our research are downloaded from the NCBI Gene Expression Synthesis (GEO) database(https://www.ncbi.nlm.nih.gov/geo/), which are publicly available databases. Accession numbers: GSE87466, GSE107597, and GSE13367."}], "state": "complete"}, {"id": "claim-target-result", "statement": "All expression values were standardized, and a total of 76 DEGs were identified at last (the analysis results were shown in Table 1 of the Supplementary Material), among which 56 genes were upregulated and 20 genes were downregulated (Figure 2).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35959356", "locator": "sections[10] \"3.1. Identification of DEGs in UC\" paragraph 2", "quote": "All expression values were standardized, and a total of 76 DEGs were identified at last (the analysis results were shown in Table 1 of the Supplementary Material), among which 56 genes were upregulated and 20 genes were downregulated (Figure 2)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "All expression values were standardized, and a total of 76 DEGs were identified at last (the analysis results were shown in Table 1 of the Supplementary Material), among which 56 genes were upregulated and 20 genes were downregulated (Figure 2).", "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:35959356"], "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:GSE107597"], "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:GSE107597"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:35959356", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1155/2022/5412627", "sha256": "sha256:f09c971abc5808e1ccaec8e438c9b6455af5d90e833b28d7eb93e0a0ee8eb0c8", "bytes": 24686, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1155/2022/5412627", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9359832", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE107597", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE107597", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE107597", "version": null, "note": "All raw data of our research are downloaded from the NCBI Gene Expression Synthesis (GEO) database(https://www.ncbi.nlm.nih.gov/geo/), which are publicly available databases. 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