Identification of SOCS3 and PTGS2 as new biomarkers for the diagnosis of gout by cross-species comprehensive analysis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38707281", "kind": "science-record", "revision": 4, "content_hash": "sha256:f2d04b1189ffb4da207a729cdf41e69a974c8bb42fd1bf4665f6cb6271e39539", "created_at": "2026-11-01T19:20:40Z", "updated_at": "2026-11-01T22:11:49Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of SOCS3 and PTGS2 as new biomarkers for the diagnosis of gout by cross-species comprehensive analysis.", "doi": "10.1016/j.heliyon.2024.e30020", "pmid": "38707281", "authors": ["Jie Peng", "Yawen Gu", "Jiang Liu", "Hao Yi", "Dong Ruan", "Haoyu Huang", "Yuan Shu", "Zhen Zong", "Rui Wu", "Hui Li"], "journal": "Heliyon", "year": 2024, "abstract": "Background: Gout is the most common inflammatory arthritis in adults. Gout is an arthritic disease caused by the deposition of monosodium urate crystal (MSU) in the joints, which can lead to acute inflammation and damage adjacent tissue. Hyperuricemia is the main risk factor for MSU crystal deposition and gout. With the increasing burden of gout disease, the identification of potential biomarkers and novel targets for diagnosis is urgently needed.\nMethods: For the analysis of this subject paper, we downloaded the human gout data set GSE160170 and the gout mouse model data set GSE190138 from the GEO database. To obtain the differentially expressed genes (DEGs), we intersected the two data sets. Using the cytohubba algorithm, we identified the key genes and enriched them through GO and KEGG. The gene expression trends of three subgroups (normal control group, intermittent gout group and acute gout attack group) were analyzed by Series Test of Cluster (STC) analysis, and the key genes were screened out, and the diagnostic effect was verified by ROC curve. The expression of key genes in dorsal root nerve and spinal cord of gout mice was analyzed. Finally, the clinical samples of normal control group, hyperuricemia group, intermittent gout group and acute gout attack group were collected, and the expression of key genes at protein level was verified by ELISA.\nResult: We obtained 59 co-upregulated and 28 co-downregulated genes by comparing the DEGs between gout mouse model data set and human gout data set. 7 hub DEGs(IL1B, IL10, NLRP3, SOCS3, PTGS2) were screened out via Cytohubba algorithm. The results of both GO and KEGG enrichment analyses indicate that 7 hub genes play a significant role in regulating the inflammatory response, cytokine production in immune response, and the TNF signaling pathway. The most representative hub genes SOCS3 and PTGS2 were screened out by Series Test of Cluster, and ROC analysis results showed the AUC values were both up to 1.000. In addition, we found that PTGS2 expression was significantly elevated in the dorsal root ganglia and spinal cord in monosodium urate(MSU)-induced gout mouse model. The ELISA results revealed that the expression of SOCS3 and PTGS2 was notably higher in the acute gout attack and intermittent gout groups compared to the normal control group. This difference was statistically significant, indicating a clear distinction between the groups.\nConclusion: Through cross-species comprehensive analysis and experimental verification, SOCS3 and PTGS2 were proved to be new biomarkers for diagnosing gout and predicting disease progression.", "claims": [{"id": "claim-method", "statement": "Specifically, we employed the least absolute shrinkage and selection operator (LASSO) algorithm, which incorporates regularization to improve the accuracy of predictions through regression analysis.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38707281", "locator": "sections[9] \"Construction and validation of diagnostic prediction model\" paragraph 2", "quote": "Specifically, we employed the least absolute shrinkage and selection operator (LASSO) algorithm, which incorporates regularization to improve the accuracy of predictions through regression analysis."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original data used in this project can be downloaded in the GEO (https://www.ncbi.nlm.nih.gov/geo/) website. Other data will be made available on request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38707281", "locator": "sections[27] \"Data availability statement\" paragraph 2", "quote": "The original data used in this project can be downloaded in the GEO (https://www.ncbi.nlm.nih.gov/geo/) website. Other data will be made available on request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "SOCS3 and PTGS2 were identified as diagnostic biomarkers for gout, with ROC AUC values of up to 1.000 in the analyzed datasets.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38707281", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "The most representative hub genes SOCS3 and PTGS2 were screened out by Series Test of Cluster, and ROC analysis results showed the AUC values were both up to 1.000."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "SOCS3 and PTGS2 were identified as diagnostic biomarkers for gout, with ROC AUC values of up to 1.000 in the analyzed datasets.", "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:38707281"], "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:GSE160170"], "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:GSE160170"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:38707281", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1016/j.heliyon.2024.e30020", "sha256": "sha256:a2d7d070cff19aea546b63ae486b33763d21cd99d7ab1c0a4d5198e01d9453b9", "bytes": 54626, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1016/j.heliyon.2024.e30020", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC11066387", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE160170", "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=GSE160170", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE160170", "version": null, "note": "The original data used in this project can be downloaded in the GEO (https://www.ncbi.nlm.nih.gov/geo/) website. 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To", "e human gout data set GSE160170 and the gout mouse model data set GSE190138 from the GEO databa", "ted genes by comparing the DEGs between gout mouse model data set and human gout data set. 7 hub DEGs", "l cord in monosodium urate(MSU)-induced gout mouse model. The ELISA results revealed that the express"], "order_basis": "paper_text_order:636", "id": "assay:1"}, {"role": "data_generation", "definition_id": "elisa", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["f key genes at protein level was verified by ELISA. Result: We obtained 59 co-upregulated and 2", "ium urate(MSU)-induced gout mouse model. The ELISA results revealed that the expression of SOCS"], "order_basis": "paper_text_order:1444", "id": "assay:2"}, {"role": "data_generation", "definition_id": "clinical_cohort", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.77, "evidence": ["thodology Collection of samples We collected blood samples from 40 patients (10 cases in the acute gout atta"], "order_basis": "paper_text_order:2746", "id": "assay:3"}, {"role": "analysis", "definition_id": "diagnostic_evaluation", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["confusion_counts", "roc_curve", "sensitivity_specificity", "solve_confusion_matrix", "classification_metrics", "published_precision_envelope"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 0.7, "evidence": ["ROC curve"], "order_basis": "consumes_earlier_output", "id": "assay:4"}]}