Three hematologic/immune system-specific expressed genes are considered as the potential biomarkers for the diagnosis of early rheumatoid arthritis through bioinformatics analysis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:33407587", "kind": "science-record", "revision": 7, "content_hash": "sha256:e82efd1546dfaaba0b5076ff6b3e463989bc2d51b9a50e830b2c4a2248fce458", "created_at": "2026-11-01T15:41:21Z", "updated_at": "2026-11-01T22:02:53Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Three hematologic/immune system-specific expressed genes are considered as the potential biomarkers for the diagnosis of early rheumatoid arthritis through bioinformatics analysis.", "doi": "10.1186/s12967-020-02689-y", "pmid": "33407587", "authors": ["Qi Cheng", "Xin Chen", "Huaxiang Wu", "Yan Du"], "journal": "Journal of translational medicine", "year": 2021, "abstract": "BACKGROUND: Rheumatoid arthritis (RA) is the most common chronic autoimmune connective tissue disease. However, early RA is difficult to diagnose due to the lack of effective biomarkers. This study aimed to identify new biomarkers and mechanisms for RA disease progression at the transcriptome level through a combination of microarray and bioinformatics analyses.\nMETHODS: Microarray datasets for synovial tissue in RA or osteoarthritis (OA) were downloaded from the Gene Expression Omnibus (GEO) database, and differentially expressed genes (DEGs) were identified by R software. Tissue/organ-specific genes were recognized by BioGPS. Enrichment analyses were performed and protein-protein interaction (PPI) networks were constructed to understand the functions and enriched pathways of DEGs and to identify hub genes. Cytoscape was used to construct the co-expressed network and competitive endogenous RNA (ceRNA) networks. Biomarkers with high diagnostic value for the early diagnosis of RA were validated by GEO datasets. The ggpubr package was used to perform statistical analyses with Student's t-test.\nRESULTS: A total of 275 DEGs were identified between 16 RA samples and 10 OA samples from the datasets GSE77298 and GSE82107. Among these DEGs, 71 tissue/organ-specific expressed genes were recognized. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis indicated that DEGs are mostly enriched in immune response, immune-related biological process, immune system, and cytokine signal pathways. Fifteen hub genes and gene cluster modules were identified by Cytoscape. Eight haematologic/immune system-specific expressed hub genes were verified by GEO datasets. GZMA, PRC1, and TTK may be potential biomarkers for diagnosis of early RA. NEAT1-miR-212-3p/miR-132-3p/miR-129-5p-TTK, XIST-miR-25-3p/miR-129-5p-GZMA, and TTK_hsa_circ_0077158- miR-212-3p/miR-132-3p/miR-129-5p-TTK might be potential RNA regulatory pathways to regulate the disease progression of early RA.\nCONCLUSIONS: This work identified three haematologic/immune system-specific expressed genes, namely, GZMA, PRC1, and TTK, as potential biomarkers for the early diagnosis and treatment of RA and provided insight into the mechanisms of disease development in RA at the transcriptome level. In addition, we proposed that NEAT1-miR-212-3p/miR-132-3p/miR-129-5p-TTK, XIST-miR-25-3p/miR-129-5p-GZMA, and TTK_hsa_circ_0077158-miR-212-3p/miR-132-3p/miR-129-5p-TTK are potential RNA regulatory pathways that control disease progression in early RA.", "claims": [{"id": "claim-method", "statement": "Student’s t-test was used to compare the differences between the two groups.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33407587", "locator": "sections[11] \"Statistics analysis\" paragraph 2", "quote": "Student’s t-test was used to compare the differences between the two groups."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The [GSE datasets] data that support the findings of this study are available in the GEO database (https://www.ncbi.nlm.nih.gov/geo/) with the following data accession identifier(s): GSE77298, GSE82107, GSE55584, GSE55457, GSE55235, and GSE89408.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33407587", "locator": "sections[27] \"Availability of data and materials\" paragraph 2", "quote": "The [GSE datasets] data that support the findings of this study are available in the GEO database (https://www.ncbi.nlm.nih.gov/geo/) with the following data accession identifier(s): GSE77298, GSE82107, GSE55584, GSE55457, GSE55235, and GSE89408."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "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:33407587"], "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:GSE55235"], "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:student_t", "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:GSE55235"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:run_analysis blocked on method:student_t"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:33407587", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": 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{"pmcid": "PMC7789535", "section_index": 14, "section_title": "Identification of the tissue/organ-specific expressed genes", "row_count": 15, "column_count": 3}, "table": {"label": "Table 2", "caption": "Distribution of tissue/organ-specific expressed genes identified by BioGPS", "rows": [["System/Organ", "Genes", "Counts"], ["Haematologic/Immune"], ["Haematologic/Immune cells", "PLA2G7, SLC50A1, T, MSC, MATK, PRKCD, CCR7, CYB561A3, P2RY8, CD3G, EMR2, NOV, BCL2A1, CD52, CD27, IL7R, TTK, MAP3K7CL, PNOC, FCGR1B, GZMB, GZMA, DLGAP5, TRBC1, MYOM2, CORO1A, PRC1, CEP55, CD3D, IER2, ITK, TNFRSF17", "32"], ["Immune organs", "CXCL13, LCK, CD163", "3"], ["Nervous", "PALM, KCND2, RASL10A, DACH1, STXBP1, DNM1, IL17D, PLP1, WRB, RCAN2, ZNF423, LRRN4CL, LPHN3", "13"], ["Digestive", "GIPC2, AKR1B10, IGJ, ADAMDEC1, C6, TOX3, C15orf48", "7"], ["Respiratory", "CHAD, MFAP4, CLDN5, LAMP3", "4"], ["Circulatory", "ACTC1, CASQ2, LRRC2, CKMT2", "4"], ["Placenta", "PVRL3, RHOBTB1, AGTR1", "3"], ["Endocrine", "DUOX2", "1"], ["Genital", "MLF1", "1"], ["Others"], ["Tongue", "MAL", "1"], ["Prostate", "PPAP2A", "1"], ["Adipose", "HOXC10", "1"]], "row_count": 15, "column_count": 3, "numeric_cells": 17, "header_rows": 1, "locator": "section[14]/table-wrap[1]#Tab2"}}, {"id": "asset:table:pmid:33407587:3", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:eadd1a05a935d150eab32d10892476a865112089eb2af6fc387734efeede935f", "bytes": 1185, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[16]/table-wrap[1]#Tab3", "version": null, "note": "15 hub genes identified by five algorithms of cytoHubba", "structure": {"pmcid": "PMC7789535", "section_index": 16, "section_title": "PPI network analysis, MCODE cluster modules and hub gene identification", "row_count": 16, "column_count": 5}, "table": {"label": "Table 3", "caption": "15 hub genes identified by five algorithms of cytoHubba", "rows": [["Gene symbol", "Description", "log2FC", "Q value", "Regulation"], ["CXCL13", "C–X–C motif chemokine ligand 13", "2.846", "0.012", "Up"], ["CD52", "CD52 molecule", "1.928", "0.004", "Up"], ["GZMA", "Qranzyme A", "1.753", "0.022", "Up"], ["CD27", "CD27 molecule", "1.419", "0.004", "Up"], ["CEP55", "Centrosomal protein 55", "1.264", "0.01", "Up"], ["SKA3", "Spindle and kinetochore associated complex subunit 3", "1.215", "3.03E−07", "Up"], ["IL21R", "Interleukin 21 receptor", "1.208", "7E−04", "Up"], ["DLGAP5", "DLG associated protein 5", "1.172", "0.027", "Up"], ["PRC1", "Protein regulator of cytokinesis 1", "1.112", "0.042", "Up"], ["EOMES", "eomesodermi", "1.108", "0.031", "Up"], ["TTK", "TTK protein kinas", "1.065", "0.006", "Up"], ["CDCA8", "Cell division cycle associated 8", "1.049", "1.74E−08", "Up"], ["UHRF1", "Ubiquitin-like with PHD and ring finger domains 1", "1.048", "0.016", "Up"], ["KIAA0101", "PCNA clamp associated factor", "1.017", "0.013", "Up"], 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