Identification of immune-related diagnostic markers in primary Sjögren's syndrome based on bioinformatics analysis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35571446", "kind": "science-record", "revision": 4, "content_hash": "sha256:818343e922dbf7529026ca45b03d5905b7158c3b710d8a35bb0b82550dd947f4", "created_at": "2026-11-01T18:14:27Z", "updated_at": "2026-11-01T22:06:31Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of immune-related diagnostic markers in primary Sjögren's syndrome based on bioinformatics analysis.", "doi": "10.21037/atm-22-1494", "pmid": "35571446", "authors": ["Qingle Zeng", "Jing Wen", "Leting Zheng", "Wen Zeng", "Shuyuan Chen", "Cheng Zhao"], "journal": "Annals of translational medicine", "year": 2022, "abstract": "Background: Primary Sjögren's syndrome (pSS) is a relatively common diffuse connective tissue disease that often invades exocrine glands, such as the lacrimal and salivary glands, and manifests as dry eyes and dry mouth. At present, the molecular mechanism of pSS is not clear. This study was designed to explore the internal mechanism of pSS from the gene level and screen out the immune-related diagnostic markers of pSS.\nMethods: The gene expression profiles GSE84844, GSE7451, and GSE40611 were downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified with R software. Then, the DEGs were intersected with the immune genes obtained from the ImmPort database to acquire differentially expressed immune-related genes (DEIRGs), and functional enrichment analyses were performed. The DEIRGs were screened through the least absolute shrinkage and selection operator (LASSO) logistic regression algorithm to obtain the optimal immune-related genes (IRGs). Expression levels of the optimal IRGs were verified by quantitative reverse transcription-polymerase chain reaction (qRT-PCR) to obtain the key genes. Next, gene chips GSE7451 and GSE40611, from other tissues, were selected as the training sets to verify the sensitivity and specificity of the diagnosis of the key genes by receiver operating characteristic (ROC) analysis.\nResults: A total of 54 DEIRGs were obtained. The functional enrichment analysis results showed that they play an important role in immune and inflammatory responses. Nine optimal IRGs were screened from the DEIRGs by the LASSO logistic regression algorithm. After qRT-PCR verification, eight out of nine optimal IRGs (IL-18, JAK2, TBK1, EED, TNFSF10, TNFSF13B, CYSLTR1, and ICOS) were significantly highly expressed in pSS patients and were defined as key genes. ROC analysis identified that TNFSF13B and CYSLTR1 had high sensitivity and specificity. Finally, the lack of previous research on EED and CYSLTR1 in pSS suggests that these IRGs may be regarded as new gateways to explore the diagnosis and pathogenesis of pSS.\nConclusions: The key DEIRGs play a decisive role during the occurrence and development of pSS.", "claims": [{"id": "claim-method", "statement": "The DEIRGs were screened through the least absolute shrinkage and selection operator (LASSO) logistic regression algorithm to obtain the optimal immune-related genes (IRGs).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35571446", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "The DEIRGs were screened through the least absolute shrinkage and selection operator (LASSO) logistic regression algorithm to obtain the optimal immune-related genes (IRGs)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Data Sharing Statement: Available at https://atm.amegroups.com/article/view/10.21037/atm-22-1494/dss", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35571446", "locator": "sections[20] \"fn-group\" paragraph 1", "quote": "Data Sharing Statement: Available at https://atm.amegroups.com/article/view/10.21037/atm-22-1494/dss"}], "state": "complete"}, {"id": "claim-target-result", "statement": "After qRT-PCR verification, eight out of nine optimal IRGs (IL-18, JAK2, TBK1, EED, TNFSF10, TNFSF13B, CYSLTR1, and ICOS) were significantly highly expressed in pSS patients and were defined as key genes.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35571446", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "After qRT-PCR verification, eight out of nine optimal IRGs (IL-18, JAK2, TBK1, EED, TNFSF10, TNFSF13B, CYSLTR1, and ICOS) were significantly highly expressed in pSS patients and were defined as key genes."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "After qRT-PCR verification, eight out of nine optimal IRGs (IL-18, JAK2, TBK1, EED, TNFSF10, TNFSF13B, CYSLTR1, and ICOS) were significantly highly expressed in pSS patients and were defined as key 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:35571446"], "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:GSE40611"], "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:GSE40611"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:35571446", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.21037/atm-22-1494", "sha256": 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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": ["GSE84844", "GSE7451", "GSE40611"], "resolved_accessions": ["GSE84844", "GSE7451", "GSE40611"], "statements": ["Data Sharing Statement: Available at https://atm.amegroups.com/article/view/10.21037/atm-22-1494/dss"], "basis": "The paper names GSE84844, GSE7451, GSE40611 in Gene Expression Omnibus (GEO) database and the accession resolves in the repository. 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