A Four-miRNA-Based Diagnostic Signature for Rheumatoid Arthritis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35251375", "kind": "science-record", "revision": 5, "content_hash": "sha256:086026555c1a27a9f59ad8ffebfb25c0571bb807d7681686f18a8f3ec7b88805", "created_at": "2026-11-01T17:37:00Z", "updated_at": "2026-11-01T22:06:02Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A Four-miRNA-Based Diagnostic Signature for Rheumatoid Arthritis.", "doi": "10.1155/2022/6693589", "pmid": "35251375", "authors": ["Xu Jiang", "Zhenjie Wei", "Chao Wang", "Qianqian Wang", "Yanzhuo Zhang", "Chengai Wu"], "journal": "Disease markers", "year": 2022, "abstract": "BACKGROUND: As a chronic inflammatory disease, rheumatoid arthritis (RA) usually leads to cartilage and bone damage, even disability. Earlier detection and diagnosis are crucial to improve the therapeutic efficacy, and the aim of our study is to identify a potential diagnostic signature for RA.\nMETHODS: We downloaded the GSE124373 dataset from the Gene Expression Omnibus (GEO) database. And differential expression analysis of miRNAs was conducted using the limma package of R language. The potential targeted mRNAs of differentially expressed miRNAs were predicted using the MiRTarBase database. The clusterProfiler package in R language was used to conduct functional enrichment analysis (GO term and KEGG pathway). Then, based on the key miRNAs screened by stepwise regression analysis, the logistic regression model was built and it was evaluated using a 5-fold cross-validation method.\nRESULTS: A total of 19 differentially expressed miRNAs in the blood sample of RA patients compared with that of healthy subjects were identified. Nine optimal miRNAs were screened by using stepwise regression analysis, and four key miRNAs hsa-miR-142-5p, hsa-miR-1184, hsa-miR-1246, and hsa-miR-99b-5p were further optimized. Finally, a logistic regression model was built based on the four key miRNAs, which could reliably separate RA patients from healthy subjects.\nCONCLUSION: Our study established a logistic regression diagnostic model based on four crucial miRNAs, which could separate the sample type reliably.", "claims": [{"id": "claim-method", "statement": "Then, based on the key miRNAs screened by stepwise regression analysis, the logistic regression model was built and it was evaluated using a 5-fold cross-validation method.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35251375", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Then, based on the key miRNAs screened by stepwise regression analysis, the logistic regression model was built and it was evaluated using a 5-fold cross-validation method."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The mRNA expression profile (Number: GSE124373) that consists of 28 blood samples from RA patients and 18 blood samples from healthy subjects was downloaded from the Gene Expression Omnibus database (GEO, https://www.ncbi.nlm.nih.gov/geo/).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35251375", "locator": "sections[16] \"Data Availability\" paragraph 2", "quote": "The mRNA expression profile (Number: GSE124373) that consists of 28 blood samples from RA patients and 18 blood samples from healthy subjects was downloaded from the Gene Expression Omnibus database (GEO, https://www.ncbi.nlm.nih.gov/geo/)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Finally, a logistic regression model was built based on the four key miRNAs, which could reliably separate RA patients from healthy subjects.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35251375", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Finally, a logistic regression model was built based on the four key miRNAs, which could reliably separate RA patients from healthy subjects."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Finally, a logistic regression model was built based on the four key miRNAs, which could reliably separate RA patients from healthy subjects.", "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:35251375"], "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:GSE124373"], "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:GSE124373"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:35251375", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1155/2022/6693589", "sha256": "sha256:0655941a59a3c02d63867b4c41a61a7296c83a5ee13b362151792afa95a9eb9d", "bytes": 29035, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1155/2022/6693589", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC8889404", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE124373", "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=GSE124373", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE124373", "version": null, "note": "The mRNA expression profile (Number: GSE124373) that consists of 28 blood samples from RA patients and 18 blood samples from healthy subjects was downloaded from the Gene Expression Omnibus database (GEO, https://www.ncbi.nlm.nih.gov/geo/).", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The mRNA expression profile (Number: GSE124373) that consists of 28 blood samples from RA patients and 18 blood samples from healthy subjects was downloaded from the Gene Expression Omnibus database (GEO, https://www.ncbi.nlm.nih.gov/geo/).", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM3530574", "GSM3530575", "GSM3530576", "GSM3530577", "GSM3530578", "GSM3530579", "GSM3530580", "GSM3530581", "GSM3530582", "GSM3530583", "GSM3530584", "GSM3530585", "GSM3530586", "GSM3530587", "GSM3530588", "GSM3530589", "GSM3530590", "GSM3530591", "GSM3530592", "GSM3530593", "GSM3530594", "GSM3530595", "GSM3530596", "GSM3530597", "GSM3530598", "GSM3530599", "GSM3530600", "GSM3530601", "GSM3530602", "GSM3530603", "GSM3530604", "GSM3530605", "GSM3530606", "GSM3530607", "GSM3530608", "GSM3530609", "GSM3530610", "GSM3530611", "GSM3530612", "GSM3530613", "GSM3530614", "GSM3530615", "GSM3530616", "GSM3530617", "GSM3530618", "GSM3530619"], "sample_count": 46, "group_sizes": null, "group_sizes_basis": null, "platform": "21572", "taxon": "Homo sapiens; synthetic construct", "title": "Rheumatoid arthritis associated microRNA in peripheral blood mononuclear cells", "resolution_checked_at": "2026-11-01T18:06:43Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:GSE124373 not downloaded", "asset:data:geo:GSE124373 licence not read"]}, "analysis": {"state": "partial", "methods": [{"id": "method-1", "family": "regression_modelling", "specific": "logistic", "label": "Regression modelling", "dsl_operations": ["glm_logit_fit"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["predictors_and_outcome"], "evidence": {"score": 1.0, "band": "verbatim", "quote": "Then, based on the key miRNAs screened by stepwise regression analysis, the logistic regression model was built and it was evaluated using a 5-fold cross-validation method."}}, {"id": "method-2", "family": "differential_expression", "specific": "limma_voom", "label": "Differential expression analysis", "dsl_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["count_matrix_with_condition"], "evidence": {"score": 0.7, "band": "medium", "quote": "limma"}}, {"id": "method-3", "family": "diagnostic_evaluation", "specific": "roc", "label": "Diagnostic test evaluation", "dsl_operations": ["confusion_counts", "roc_curve", "sensitivity_specificity", "solve_confusion_matrix", "classification_metrics", "published_precision_envelope"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["reference_standard_and_test"], "evidence": {"score": 0.8, "band": "high", "quote": "ROC curve"}}], "targets": [], "gaps": ["method.reader_differs_from_extractor"]}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "complete", "tier": "R1", "blocking_axis": "none", "blocked_on": "nothing:replicable_now", "axes": {"method": {"tier": "R1", "expressible": true, "required_operations": ["glm_logit_fit"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: logistic."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. 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