CKS2 and S100A12: Two Novel Diagnostic Biomarkers for Rheumatoid Arthritis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35789606", "kind": "science-record", "revision": 5, "content_hash": "sha256:69c79b2127528cd844e4210bb4163457ac1fd148b647914247acb9224f5b7bdb", "created_at": "2026-11-01T18:14:50Z", "updated_at": "2026-11-01T22:49:28Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "CKS2 and S100A12: Two Novel Diagnostic Biomarkers for Rheumatoid Arthritis.", "doi": "10.1155/2022/2431976", "pmid": "35789606", "authors": ["Zhi-Wen Wang", "Li-Jun Zhang", "Yu Zhuang", "Zhi-Fen Lv", "Zhi-Ming Tan"], "journal": "Disease markers", "year": 2022, "abstract": "Rheumatoid arthritis (RA) is a chronic systematicness autoimmunity disease with joint inflammation. RA etiology is still unknown. Early and exact diagnosing is still hard to reach. In the paper, we purposed to discover novel diagnosis biological marker for RA. Two open, usable gene expression profiles of human RA as well as controlled specimens (dataset GSE17755 as well as GSE93272) were downloaded from the GEO database. Differentially expressed genes (DEGs) were screened between 331 RA and 88 control samples. Functional enrichment analysis was applied to explore the possible function of DEGs. Expression levels as well as diagnosis values of biological marker in RA were further verified in our cohort by the use of RT-PCR and ROC assays. We identified 13 DEGs between RA samples and control samples. 13 DEGs were remarkably abundant in NF-kappa B signal pathway. Among the 13 DEGs, CKS2, S100A12, LY96, and ANXA3 exhibited a strong diagnostic ability in screening RA specimens from normal specimens using all AUC > 0.8. Moreover, we confirmed that the expression of CKS2 and S100A12 was distinctly upregulated in RA specimens contrasted to normal specimens. Overall, serum CKS2 and S100A12 could be used as novel diagnosis biological markers for RA patients.", "claims": [{"id": "claim-method", "statement": "Limma package of R was applied for background revising, normalizing among arrays, and differential expression analysis between 331 RA and 88 control samples.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35789606", "locator": "sections[4] \"2.2. Data Processing and DEG Screening\" paragraph 2", "quote": "Limma package of R was applied for background revising, normalizing among arrays, and differential expression analysis between 331 RA and 88 control samples."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data used in this research are available from the corresponding author upon reasonable request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35789606", "locator": "sections[17] \"Data Availability\" paragraph 2", "quote": "The data used in this research are available from the corresponding author upon reasonable request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "CKS2, S100A12, LY96, and ANXA3 exhibited a strong diagnostic ability in screening RA samples from normal samples with all AUC > 0.8.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35789606", "locator": "sections[12] \"3.3. The Diagnostic Value of 13 DEGs in RA\" paragraph 2", "quote": "As shown in Figure 4(a), we observed that four genes including CKS2, S100A12, LY96, and ANXA3 exhibited a strong diagnostic ability in screening RA samples from normal samples with all AUC > 0.8."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "CKS2, S100A12, LY96, and ANXA3 exhibited a strong diagnostic ability in screening RA samples from normal samples with all AUC > 0.8.", "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:35789606"], "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:GSE17755"], "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:GSE17755"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:35789606", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1155/2022/2431976", "sha256": 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