Screening biomarkers for Sjogren's Syndrome by computer analysis and evaluating the expression correlations with the levels of immune cells.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37383223", "kind": "science-record", "revision": 4, "content_hash": "sha256:0e822fcdc713060c5225ce8fb169f243b065e43169671e934ffd67f0466886c2", "created_at": "2026-11-01T19:02:07Z", "updated_at": "2026-11-01T22:52:12Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Screening biomarkers for Sjogren's Syndrome by computer analysis and evaluating the expression correlations with the levels of immune cells.", "doi": "10.3389/fimmu.2023.1023248", "pmid": "37383223", "authors": ["Yafang Zhong", "Wei Zhang", "Dongzhou Liu", "Zhipeng Zeng", "Shengyou Liao", "Wanxia Cai", "Jiayi Liu", "Lian Li", "Xiaoping Hong", "Donge Tang", "Yong Dai"], "journal": "Frontiers in immunology", "year": 2023, "abstract": "Background: Sjögren's syndrome (SS) is a systemic autoimmune disease that affects about 0.04-0.1% of the general population. SS diagnosis depends on symptoms, clinical signs, autoimmune serology, and even invasive histopathological examination. This study explored biomarkers for SS diagnosis.\nMethods: We downloaded three datasets of SS patients' and healthy pepole's whole blood (GSE51092, GSE66795, and GSE140161) from the Gene Expression Omnibus (GEO) database. We used machine learning algorithm to mine possible diagnostic biomarkers for SS patients. Additionally, we assessed the biomarkers' diagnostic value using the receiver operating characteristic (ROC) curve. Moreover, we confirmed the expression of the biomarkers through the reverse transcription quantitative polymerase chain reaction (RT-qPCR) using our own Chinese cohort. Eventually, the proportions of 22 immune cells in SS patients were calculated by CIBERSORT, and connections between the expression of the biomarkers and immune cell ratios were studied.\nResults: We obtained 43 DEGs that were mainly involved in immune-related pathways. Next, 11 candidate biomarkers were selected and validated by the validation cohort data set. Besides, the area under curves (AUC) of XAF1, STAT1, IFI27, HES4, TTC21A, and OTOF in the discovery and validation datasets were 0.903 and 0.877, respectively. Subsequently, eight genes, including HES4, IFI27, LY6E, OTOF, STAT1, TTC21A, XAF1, and ZCCHC2, were selected as prospective biomarkers and verified by RT-qPCR. Finally, we revealed the most relevant immune cells with the expression of HES4, IFI27, LY6E, OTOF, TTC21A, XAF1, and ZCCHC2.\nConclusion: In this paper, we identified seven key biomarkers that have potential value for diagnosing Chinese SS patients.", "claims": [{"id": "claim-method", "statement": "The least absolute shrinkage and selection operator (LASSO) logistic regression, support vector machine-recursive feature elimination (SVM-RFE), and random forest (RF) were used to screen the potential SS diagnostic markers.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37383223", "locator": "sections[5] \"Screening of the candidate diagnostic biomarkers\" paragraph 2", "quote": "The least absolute shrinkage and selection operator (LASSO) logistic regression, support vector machine-recursive feature elimination (SVM-RFE), and random forest (RF) were used to screen the potential SS diagnostic markers."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/ Supplementary Material .", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37383223", "locator": "sections[17] \"Data availability statement\" paragraph 2", "quote": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/ Supplementary Material ."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The combination of XAF1, STAT1, IFI27, HES4, TTC21A, and OTOF had an AUC value of 0.903 in the discovery data set and 0.877 in the validation data set.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37383223", "locator": "sections[12] \"The diagnostic capability of 12 biomarkers for SS\" paragraph 2", "quote": "Then, we combined these genes into one signature and found that the combination of XAF1, STAT1, IFI27, HES4, TTC21A, and OTOF resulted in a relatively high AUC value of 0.903 ( Figure 4K )."}, {"asset": "source:pmid:37383223", "locator": "sections[12] \"The diagnostic capability of 12 biomarkers for SS\" paragraph 4", "quote": "When we combined XAF1, STAT1, IFI27, HES4, TTC21A, and OTOF as one signature, the AUC value reached 0.877 ( Figure 5K )."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "The combination of XAF1, STAT1, IFI27, HES4, TTC21A, and OTOF had an AUC value of 0.903 in the discovery data set and 0.877 in the validation data set.", "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:37383223"], "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:GSE140161"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "blocked", "blocked_on": "method:machine_learning_random_forest", "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:GSE140161"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:run_analysis blocked on method:machine_learning_random_forest"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:37383223", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fimmu.2023.1023248", "sha256": "sha256:e2c26891659af95cf9e73f70295c30505bed85c27585e3fc189fc1669b1b21c8", "bytes": 32998, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fimmu.2023.1023248", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10294232", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE140161", "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=GSE140161", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE140161", "version": null, "note": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/ Supplementary Material .", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The datasets presented in this study can be found in online repositories. 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The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R1", "reachable": true, "accessions": ["GSE51092", "GSE66795", "GSE140161"], "resolved_accessions": ["GSE51092", "GSE66795", "GSE140161"], "statements": ["The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/ Supplementary Material ."], "basis": "The paper names GSE51092, GSE66795, GSE140161 in Gene Expression Omnibus (GEO) database and the accession resolves in the repository. 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