Identification of novel biomarkers for atherosclerosis using single-cell RNA sequencing and machine learning.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39400603", "kind": "science-record", "revision": 3, "content_hash": "sha256:556ff73654e996c9a089ba6231036fbbf56aa0c298d161ef9bca40a28ee66e23", "created_at": "2026-11-01T20:29:38Z", "updated_at": "2026-11-01T22:12:55Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of novel biomarkers for atherosclerosis using single-cell RNA sequencing and machine learning.", "doi": "10.1007/s00335-024-10077-w", "pmid": "39400603", "authors": ["Xi Yong", "Tengyao Kang", "Mingzhu Li", "Sixuan Li", "Xiang Yan", "Jiuxin Li", "Jie Lin", "Bo Lu", "Jianghua Zheng", "Zhengmin Xu", "Qin Yang", "Jingdong Li"], "journal": "Mammalian genome : official journal of the International Mammalian Genome Society", "year": 2025, "abstract": "Atherosclerosis (AS) is a predominant etiological factor in numerous cardiovascular diseases, with its associated complications such as myocardial infarction and stroke serving as major contributors to worldwide mortality rates. Here, we devised dependable AS-related biomarkers through the utilization of single-cell RNA sequencing, weighted co-expression network (WGCNA), and differential expression analysis. Furthermore, we employed various machine learning techniques (LASSO and SVM-RFE) to enhance the identification of AS biomarkers, subsequently validating them using the GEO dataset. Following this, CIBERSORT was employed to investigate the correlation between biomarkers and infiltrating immune cells. Consequently, 256 differentially expressed genes (DEGs) were selected in samples of AS and normal. GO and KEGG analyses indicated that these DEGs may be related to the negative regulation of leukocyte-mediated immunity, leukocyte cell-cell adhesion, and immune system processes. Notably, C1QC and COL1A1 were pinpointed as potential diagnostic markers for AS, a finding that was further validated in the GSE21545 dataset. Moreover, the area under the curve (AUC) values for these markers exceeded 0.8, underscoring their diagnostic utility. Analysis of immune cell infiltration revealed that the expression of C1QC was correlated with M0 macrophages, gamma delta T cells, activated mast cells and memory B cells. Similarly, COL1A1 expression was linked to M0 macrophages, memory B cells, activated mast cells, gamma delta T cells, and CD4 native T cells. Finally, these results were validated using mice and human samples through immunofluorescence, immunohistochemistry, and ELISA analysis. Overall, C1QC and COL1A1 would be potential biomarkers for AS diagnosis, and that would provides novel perspectives on the diagnosis and treatment of AS.", "claims": [{"id": "claim-method", "statement": "The \"corrplot\" was used to compute the Spearman correlation analysis between the invading immune cells and the obtained gene biomarkers.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39400603", "locator": "sections[10] \"Correlation analysis between infiltrating immune cells and diagnostic genes\" paragraph 2", "quote": "The \"corrplot\" was used to compute the Spearman correlation analysis between the invading immune cells and the obtained gene biomarkers."}], "state": "complete"}, {"id": "claim-data-access", "statement": "GEO data can be obtained from the official website (https://www.ncbi.nlm.nih.gov/geo). The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39400603", "locator": "sections[32] \"notes\" paragraph 1", "quote": "GEO data can be obtained from the official website (https://www.ncbi.nlm.nih.gov/geo). The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "C1QC and COL1A1 were identified as potential diagnostic biomarkers for atherosclerosis.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39400603", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "Notably, C1QC and COL1A1 were pinpointed as potential diagnostic markers for AS, a finding that was further validated in the GSE21545 dataset."}, {"asset": "source:pmid:39400603", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "Moreover, the area under the curve (AUC) values for these markers exceeded 0.8, underscoring their diagnostic utility."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "C1QC and COL1A1 were identified as potential diagnostic biomarkers for atherosclerosis.", "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:39400603"], "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": "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:GSE100927"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:39400603", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1007/s00335-024-10077-w", "sha256": "sha256:d897cd23965933bb35646f5f1c2eaa0916fef5e2253ca4781544521c835a19cb", "bytes": 37794, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1007/s00335-024-10077-w", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC11880100", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE100927", "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=GSE100927", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE100927", "version": null, "note": "GEO data can be obtained from the official website (https://www.ncbi.nlm.nih.gov/geo). 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