Integrated Analysis of Microarray Studies to Identify Novel Diagnostic Markers in Bladder Pain Syndrome/Interstitial Cystitis with Hunner Lesion.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35342305", "kind": "science-record", "revision": 5, "content_hash": "sha256:d309d41bfe4e86c57838c0dbb672aa64bcc957ce197b1a0f7d89d89506fa1bde", "created_at": "2026-11-01T18:14:08Z", "updated_at": "2026-11-01T22:48:44Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Integrated Analysis of Microarray Studies to Identify Novel Diagnostic Markers in Bladder Pain Syndrome/Interstitial Cystitis with Hunner Lesion.", "doi": "10.2147/IJGM.S351287", "pmid": "35342305", "authors": ["Xiao-Feng Cheng", "Zhen-Hao Zeng", "Wen Deng", "Yi-Fu Liu", "Xiao-Chen Zhou", "Cheng Zhang", "Gong-Xian Wang"], "journal": "International journal of general medicine", "year": 2022, "abstract": "Background: The aim of this study was to identify novel genetic features of Hunner's lesion interstitial cystitis (HIC) via comprehensive analysis of the Gene Expression Omnibus (GEO) database.\nMethods: The GSE11783 and GSE28242 datasets were downloaded from GEO for further analysis. Differentially expressed genes (DEGs) were identified and analyzed for functional annotation. The diagnostic markers for HIC were screened and validated using the least absolute shrinkage and selection operator (LASSO) logistic regression and support vector machine recursive feature elimination (SVM-RFE) algorithms. Finally, the cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) algorithm was adopted to investigate the correlation between immune cell infiltration and diagnostic markers in HIC.\nResults: A total of 7837 DEGs were identified in GSE11783 and 1583 DEGs in GSE28242. Venn diagrams were used to obtain 16 overlapping upregulated and 67 overlapping downregulated DEGs separately. The LASSO logistic model and SVM-RFE algorithm were used to identify 6 genes including KRT20, SLFN11, CD86, ITGA4, PLAC8, and BTN3A3 from DEGs as diagnostic markers for HIC. Their diagnostic potential in HIC and bladder pain syndrome/interstitial cystitis (BPS/IC) were acceptable. PLAC8 exhibited the best diagnostic performance in BPS/IC with an area under the curve of 0.916. The results of immune infiltration involving GSE11783 revealed that the plasma cell ratio (p = 0.017), activated memory CD4+ T cells (p = 0.009), activated dendritic cells (p = 0.01), eosinophils (p = 0.004), and neutrophils (p = 0.03) were significantly higher in HIC than in normal samples, in contrast to resting mast cells (p = 0.022). A positive correlation existed between diagnostic markers and infiltrating immune cells.\nConclusion: KRT20, SLFN11, CD86, ITGA4, PLAC8, and BTN3A3 represent novel and potent diagnostic markers for HIC. They also exhibit certain diagnostic potential in BPS/IC. Immune cell infiltration might play a key role in the pathogenesis and progression of BPS/IC.", "claims": [{"id": "claim-method", "statement": "The least absolute shrinkage and selection operator (LASSO) logistic regression9 and support vector machine recursive feature elimination (SVM-RFE) algorithms10 were used for feature selection and screening of diagnostic markers for HIC.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35342305", "locator": "sections[7] \"Screening and Verification of Diagnostic Markers\" paragraph 2", "quote": "The least absolute shrinkage and selection operator (LASSO) logistic regression9 and support vector machine recursive feature elimination (SVM-RFE) algorithms10 were used for feature selection and screening of diagnostic markers for HIC."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Gene datasets and clinical information of patients with BPS/IC are available from the GEO database (https://www.ncbi.nlm.nih.gov/geo/).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35342305", "locator": "sections[18] \"Data Sharing Statement\" paragraph 2", "quote": "Gene datasets and clinical information of patients with BPS/IC are available from the GEO database (https://www.ncbi.nlm.nih.gov/geo/)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The combined LASSO logistic regression and SVM-RFE analysis identified KRT20, SLFN11, CD86, ITGA4, PLAC8, and BTN3A3 as diagnostic markers for HIC, with PLAC8 showing the best diagnostic performance in BPS/IC at an AUC of 0.916.", "kind": "quantitative", "target": {"value": 0.916, "unit": "auc", "tolerance": null, "source": "The diagnostic efficacy of all the aforementioned diagnostic markers in IC/BPS was acceptable, and PLAC8 exhibited the best diagnostic performance with an AUC of 0.916."}, "evidence": [{"asset": "source:pmid:35342305", "locator": "sections[12] \"Screening and Verification of Diagnostic Markers\" paragraph 2", "quote": "The two gene datasets obtained by both algorithms overlap yielding 6 genes as diagnostic markers, exactly KRT20, SLFN11, CD86, ITGA4, PLAC8, and BTN3A3 (Figure 3A)."}, {"asset": "source:pmid:35342305", "locator": "sections[12] \"Screening and Verification of Diagnostic Markers\" paragraph 2", "quote": "The diagnostic efficacy of all the aforementioned diagnostic markers in IC/BPS was acceptable, and PLAC8 exhibited the best diagnostic performance with an AUC of 0.916."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The combined LASSO logistic regression and SVM-RFE analysis identified KRT20, SLFN11, CD86, ITGA4, PLAC8, and BTN3A3 as diagnostic markers for HIC, with PLAC8 showing the best diagnostic performance in BPS/IC at an AUC of 0.916.", "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:35342305"], "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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