Identification of potential diagnostic biomarkers of atherosclerosis based on bioinformatics strategy.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37173673", "kind": "science-record", "revision": 3, "content_hash": "sha256:3ee217d653e267ecad386c9c0ba51027e5a1e8dd3fa39febc5500c085f24ac9c", "created_at": "2026-11-01T19:01:50Z", "updated_at": "2026-11-01T22:09:26Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of potential diagnostic biomarkers of atherosclerosis based on bioinformatics strategy.", "doi": "10.1186/s12920-023-01531-w", "pmid": "37173673", "authors": ["Zhipeng Zheng", "Dong Yuan", "Cheng Shen", "Zhiyuan Zhang", "Jun Ye", "Li Zhu"], "journal": "BMC medical genomics", "year": 2023, "abstract": "BACKGROUND: Atherosclerosis is the main pathological change in atherosclerotic cardiovascular disease, and its underlying mechanisms are not well understood. The aim of this study was to explore the hub genes involved in atherosclerosis and their potential mechanisms through bioinformatics analysis.\nMETHODS: Three microarray datasets from Gene Expression Omnibus (GEO) identified robust differentially expressed genes (DEGs) by robust rank aggregation (RRA). We performed connectivity map (CMap) analysis and functional enrichment analysis on robust DEGs and constructed a protein‒protein interaction (PPI) network using the STRING database to identify the hub gene using 12 algorithms of cytoHubba in Cytoscape. Receiver operating characteristic (ROC) analysis was used to assess the diagnostic potency of the hub genes.The CIBERSORT algorithm was used to perform immunocyte infiltration analysis and explore the association between the identified biomarkers and infiltrating immunocytes using Spearman's rank correlation analysis in R software. Finally, we evaluated the expression of the hub gene in foam cells.\nRESULTS: A total of 155 robust DEGs were screened by RRA and were revealed to be mainly associated with cytokines and chemokines by functional enrichment analysis. CD52 and IL1RN were identified as hub genes and were validated in the GSE40231 dataset. Immunocyte infiltration analysis showed that CD52 was positively correlated with gamma delta T cells, M1 macrophages and CD4 memory resting T cells, while IL1RN was positively correlated with monocytes and activated mast cells. RT-qPCR results indicate that CD52 and IL1RN were highly expressed in foam cells, in agreement with bioinformatics analysis.\nCONCLUSIONS: ​This study has established that CD52 and IL1RN may play a key role in the occurrence and development of atherosclerosis, which opens new lines of thought for further research on the pathogenesis of atherosclerosis.", "claims": [{"id": "claim-method", "statement": "Receiver operating characteristic (ROC) analysis was used to assess the diagnostic potency of the hub genes.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37173673", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Receiver operating characteristic (ROC) analysis was used to assess the diagnostic potency of the hub genes."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets analyzed during the current study are available in the GEO repository (https://www.ncbi.nlm.nih.gov/geo/; Accession numbers: GSE43292, GSE97210, GSE100927 and GSE40231).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37173673", "locator": "sections[30] \"notes\" paragraph 1", "quote": "The datasets analyzed during the current study are available in the GEO repository (https://www.ncbi.nlm.nih.gov/geo/; Accession numbers: GSE43292, GSE97210, GSE100927 and GSE40231)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "In the GSE40231 validation dataset, CD52 and IL1RN showed satisfactory diagnostic discrimination between atherosclerosis and control samples, with AUC values of 0.788 and 0.631, respectively.", "kind": "quantitative", "target": {"value": 0.788, "unit": "auc", "tolerance": null, "source": "To further validate the diagnostic value of the above genes, we demonstrated their satisfactory discrimination in the GSE40231 validation dataset, with an AUC of 0.788 in CD52 and 0.631 in IL1RN (Fig. 6A and B)."}, "evidence": [{"asset": "source:pmid:37173673", "locator": "sections[19] \"Clinical diagnostic significance of potential biomarkers for atherosclerosis\" paragraph 2", "quote": "To further validate the diagnostic value of the above genes, we demonstrated their satisfactory discrimination in the GSE40231 validation dataset, with an AUC of 0.788 in CD52 and 0.631 in IL1RN (Fig. 6A and B)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "In the GSE40231 validation dataset, CD52 and IL1RN showed satisfactory diagnostic discrimination between atherosclerosis and control samples, with AUC values of 0.788 and 0.631, respectively.", "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:37173673"], "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:GSE100927"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "blocked", "blocked_on": "method:group_comparison_wilcoxon", "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:GSE100927"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:run_analysis blocked on method:group_comparison_wilcoxon"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:37173673", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s12920-023-01531-w", "sha256": "sha256:0e0dfcb2547240b8389fc2c3ebc481ea2eb6227c7e5e7c7c234d8e9d4a536858", "bytes": 34966, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1186/s12920-023-01531-w", "version": null, "note": "Full article fetched from PMC; 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