CLP1 is a Prognosis-Related Biomarker and Correlates With Immune Infiltrates in Rheumatoid Arthritis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35721104", "kind": "science-record", "revision": 5, "content_hash": "sha256:2e15f9b6d6336ad65a7165961ffd82448db18bf2045debf9ad061afc652311ef", "created_at": "2026-11-01T18:14:43Z", "updated_at": "2026-11-01T22:49:21Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "CLP1 is a Prognosis-Related Biomarker and Correlates With Immune Infiltrates in Rheumatoid Arthritis.", "doi": "10.3389/fphar.2022.827215", "pmid": "35721104", "authors": ["Zhenyu Zhao", "Shaojie He", "Sheng Tang", "Xiaofeng Lai", "Jie Ren", "XinCheng Yu", "Jinhua Lin", "Mohan Wang", "Mariya M El Akkawi", "Shan Zeng", "Dingsheng Zha"], "journal": "Frontiers in pharmacology", "year": 2022, "abstract": "Rheumatoid arthritis (RA) is a chronic, heterogeneous autoimmune disease with a high disability rate that seriously affects society and individuals. However, there is a lack of effective and reliable diagnostic markers and therapeutic targets. In this study, we identified diagnostic markers of RA based on RNA modification and explored its role as well as degree of immune cell infiltration. We used the gene expression profile data of three synovial tissues (GSE55235, GSE55457, GSE77298) from the Gene Expression Omnibus (GEO) database and the gene of 5 RNA modification genes (including m6A, m1A, m5C, APA, A-1), combined with cluster analysis, identified four RNA modifiers closely related to RA (YTHDC1, LRPPRC, NOP2, and CLP1) and five immune cells namely T cell CD8, CD4 memory resting, T cells regulatory (Tregs) Macrophages M0, and Neutrophils. Based on the LASSO regression algorithm, hub genes and immune cell prediction models were established respectively in RA and a nomogram based on the immune cell model was built. Around 4 key RNA modification regulator genes, miRNA-mRNA, mRNA-TF networks have been established, and GSEA-GO, KEGG-GSEA enrichment analysis has been carried out. Finally, CLP1 was established as an effective RA diagnostic marker, and was highly positively correlated with T cells follicular helper (Tfh) infiltration. On the other hand, highly negatively correlated with the expression of mast cells. In short, CLP1 may play a non-negligible role in the onset and development of RA by altering immune cell infiltration, and it is predicted to represent a novel target for RA clinical diagnosis and therapy.", "claims": [{"id": "claim-data-access", "statement": "The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding authors.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35721104", "locator": "sections[25] \"Data Availability Statement\" paragraph 2", "quote": "The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding authors."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The immune correlation of diagnostic marker CLP1 was investigated, and the result demonstrated a significant positive correlation between the infiltration degree of B cells naïve, Dendritic cells activated, Eosinophils,Macrophages, Monocytes, Plasma cells,T cells CD8, and T cells follicular helper. of which, T cells follicular helper had the highest positive correlation (0.48), while mast cells, NK cells activated, and T cells CD4 memory infiltration revealed a significant negative correlation, with Mast cells having the highest negative correlation (-0.39) (Figure 14).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35721104", "locator": "sections[22] \"Diagnostic Marker Analysis\" paragraph 2", "quote": "The immune correlation of diagnostic marker CLP1 was investigated, and the result demonstrated a significant positive correlation between the infiltration degree of B cells naïve, Dendritic cells activated, Eosinophils,Macrophages, Monocytes, Plasma cells,T cells CD8, and T cells follicular helper. of which, T cells follicular helper had the highest positive correlation (0.48), while mast cells, NK cells activated, and T cells CD4 memory infiltration revealed a significant negative correlation, with Mast cells having the highest negative correlation (-0.39) (Figure 14)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "The immune correlation of diagnostic marker CLP1 was investigated, and the result demonstrated a significant positive correlation between the infiltration degree of B cells naïve, Dendritic cells activated, Eosinophils,Macrophages, Monocytes, Plasma cells,T cells CD8, and T cells follicular helper. of which, T cells follicular helper had the highest positive correlation (0.48), while mast cells, NK cells activated, and T cells CD4 memory infiltration revealed a significant negative correlation, with Mast cells having the highest negative correlation (-0.39) (Figure 14).", "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:35721104"], "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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