Significance of pyroptosis-related gene in the diagnosis and classification of rheumatoid arthritis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37008939", "kind": "science-record", "revision": 4, "content_hash": "sha256:e9f4dfd50f92f0847ba2f1d2d5fc089287a8420b8faf585b271d39dba93169ad", "created_at": "2026-11-01T19:01:40Z", "updated_at": "2026-11-01T22:51:38Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Significance of pyroptosis-related gene in the diagnosis and classification of rheumatoid arthritis.", "doi": "10.3389/fendo.2023.1144250", "pmid": "37008939", "authors": ["Jian Li", "Yongfeng Cui", "Xin Jin", "Hongfeng Ruan", "Dongan He", "Xiaoqian Che", "Jiawei Gao", "Haiming Zhang", "Jiandong Guo", "Jinxi Zhang"], "journal": "Frontiers in endocrinology", "year": 2023, "abstract": "Background: Rheumatoid arthritis (RA), a chronic autoimmune inflammatory disease, is often characterized by persistent morning stiffness, joint pain, and swelling. Early diagnosis and timely treatment of RA can effectively delay the progression of the condition and significantly reduce the incidence of disability. In the study, we explored the function of pyroptosis-related genes (PRGs) in the diagnosis and classification of rheumatoid arthritis based on Gene Expression Omnibus (GEO) datasets.\nMethod: We downloaded the GSE93272 dataset from the GEO database, which contains 35 healthy controls and 67 RA patients. Firstly, the GSE93272 was normalized by the R software \"limma\" package. Then, we screened PRGs by SVM-RFE, LASSO, and RF algorithms. To further investigate the prevalence of RA, we established a nomogram model. Besides, we grouped gene expression profiles into two clusters and explored their relationship with infiltrating immune cells. Finally, we analyzed the relationship between the two clusters and the cytokines.\nResult: CHMP3, TP53, AIM2, NLRP1, and PLCG1 were identified as PRGs. The nomogram model revealed that decision-making based on established model might be beneficial for RA patients, and the predictive power of the nomogram model was significant. In addition, we identified two different pyroptosis patterns (pyroptosis clusters A and B) based on the 5 PRGs. We found that eosinophil, gamma delta T cell, macrophage, natural killer cell, regulatory T cell, type 17 T helper cell, and type 2 T helper cell were significant high expressed in cluster B. And, we identified gene clusters A and B based on 56 differentially expressed genes (DEGs) between pyroptosis cluster A and B. And we calculated the pyroptosis score for each sample to quantify the different patterns. The patients in pyroptosis cluster B or gene cluster B had higher pyroptosis scores than those in pyroptosis cluster A or gene cluster A.\nConclusion: In summary, PRGs play vital roles in the development and occurrence of RA. Our findings might provide novel views for the immunotherapy strategies with RA.", "claims": [{"id": "claim-method", "statement": "Then, we identified differentially expressed PRGs in RA and normal samples using the “limma” package.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37008939", "locator": "sections[4] \"Identification of differentially expressed PRGs\" paragraph 2", "quote": "Then, we identified differentially expressed PRGs in RA and normal samples using the “limma” package."}], "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:37008939", "locator": "sections[22] \"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": "Finally, we overlapped three different algorithms analysis results and obtained 5 genes (CHMP3, TP53, AIM2, NLRP1, and PLCG1) that were significantly related to RA ( Figure 1F ).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37008939", "locator": "sections[14] \"Identification of characteristic genes\" paragraph 2", "quote": "Finally, we overlapped three different algorithms analysis results and obtained 5 genes (CHMP3, TP53, AIM2, NLRP1, and PLCG1) that were significantly related to RA ( Figure 1F )."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Finally, we overlapped three different algorithms analysis results and obtained 5 genes (CHMP3, TP53, AIM2, NLRP1, and PLCG1) that were significantly related to RA ( Figure 1F ).", "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:37008939"], "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:GSE93272"], "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:GSE93272"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:37008939", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fendo.2023.1144250", "sha256": "sha256:0a1f9ab4a913d25e01b66c4df8e1dbead66b16d3c4f4afc27ffd4c299cd73a8b", "bytes": 39369, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fendo.2023.1144250", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10057543", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE93272", "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=GSE93272", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE93272", "version": null, "note": "The datasets presented in this study can be found in online repositories. 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