Screening and identification of biomarkers for systemic sclerosis via microarray technology.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:31545397", "kind": "science-record", "revision": 8, "content_hash": "sha256:058b83501f23124918201cc7e92ad8efdd6ea6eda16789fce270bab6221bef8e", "created_at": "2026-11-01T15:40:49Z", "updated_at": "2026-11-01T22:44:12Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Screening and identification of biomarkers for systemic sclerosis via microarray technology.", "doi": "10.3892/ijmm.2019.4332", "pmid": "31545397", "authors": ["Chen Xu", "Ling-Bing Meng", "Yu-Chen Duan", "Yong-Jing Cheng", "Chun-Mei Zhang", "Xing Zhou", "Ci-Bo Huang"], "journal": "International journal of molecular medicine", "year": 2019, "abstract": "Systemic sclerosis (SSc) is a complex autoimmune disease. The pathogenesis of SSc is currently unclear, although like other rheumatic diseases its pathogenesis is complicated. However, the ongoing development of bioinformatics technology has enabled new approaches to research this disease using microarray technology to screen and identify differentially expressed genes (DEGs) in the skin of patients with SSc compared with individuals with healthy skin. Publicly available data were downloaded from the Gene Expression Omnibus (GEO) database and intra‑group data repeatability tests were conducted using Pearson's correlation test and principal component analysis. DEGs were identified using an online tool, GEO2R. Functional annotation of DEGs was performed using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Finally, the construction and analysis of the protein‑protein interaction (PPI) network and identification and analysis of hub genes was carried out. A total of 106 DEGs were detected by the screening of SSc and healthy skin samples. A total of 10 genes [interleukin‑6, bone morphogenetic protein 4, calumenin (CALU), clusterin, cysteine rich angiogenic inducer 61, serine protease 23, secretogranin II, suppressor of cytokine signaling 3, Toll‑like receptor 4 (TLR4), tenascin C] were identified as hub genes with degrees ≥10, and which could sensitively and specifically predict SSc based on receiver operator characteristic curve analysis. GO and KEGG analysis showed that variations in hub genes were mainly enriched in positive regulation of nitric oxide biosynthetic processes, negative regulation of apoptotic processes, extracellular regions, extracellular spaces, cytokine activity, chemo‑attractant activity, and the phosphoinositide 3 kinase‑protein kinase B signaling pathway. In summary, bioinformatics techniques proved useful for the screening and identification of biomarkers of disease. A total of 106 DEGs and 10 hub genes were linked to SSc, in particular the TLR4 and CALU genes.", "claims": [{"id": "claim-method", "statement": "The limma (Linear Models for Microarray Analysis) R package, which could perform the paired-samples T test, has emerged as one of the most widely used statistical tests for identifying differentially expressed genes (17).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:31545397", "locator": "sections[5] \"Identification of DEGs\" paragraph 2", "quote": "The limma (Linear Models for Microarray Analysis) R package, which could perform the paired-samples T test, has emerged as one of the most widely used statistical tests for identifying differentially expressed genes (17)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:31545397", "locator": "sections[20] \"Availability of data and materials\" paragraph 2", "quote": "The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": null, "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:31545397"], "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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