{
  "abstract": "Background: Atherosclerosis is a chronic inflammatory disease of the arterial wall in which immune-cell accumulation and vascular stromal remodeling jointly shape plaque progression and stability. Because bulk-tissue transcriptomes combine signals from multiple cell types, the cellular origin of candidate expression markers must be resolved before they can be interpreted as vascular smooth muscle cell (VSMC)-intrinsic features. Methods: We integrated the GSE309462 human carotid-plaque single-cell RNA-sequencing dataset with the bulk arterial-tissue datasets GSE100927 and GSE57691. Intersection-derived genes were prioritized using LASSO, random forest, and SVM-RFE. Discriminatory direction was standardized for receiver operating characteristic analysis, the five consensus genes were evaluated in the independent plaque-stage dataset GSE28829, and their cellular origins were audited across 99,027 cells from 12 donors. Results: Forty-nine genes were shared between the SMC-cluster-associated gene set and the bulk arterial-tissue differential-expression results. Five genes (MPEG1, CX3CR1, SERPINA3, LMCD1, and COL1A1) were selected by all three machine-learning procedures. In the training data, direction-standardized AUCs ranged from 0.859 to 0.922. In GSE28829, MPEG1, CX3CR1, and COL1A1 showed higher expression in advanced plaques and AUCs of 0.904, 0.827, and 0.928, respectively; LMCD1 was higher in early plaques (AUC 0.870), whereas SERPINA3 showed limited discrimination (AUC 0.442). Single-cell localization showed that MPEG1 and CX3CR1 were predominantly expressed in macrophages, whereas LMCD1 and COL1A1 were enriched in VSMCs and other vascular stromal cells. Conclusion: The expression signature captures compartmentally distinct processes, particularly myeloid-cell infiltration and extracellular-matrix remodeling. These genes should therefore be interpreted as plaque-associated tissue markers and mechanistic candidates rather than validated non-invasive diagnostic biomarkers or definitive VSMC-lineage markers.",
  "abstract_shown": true,
  "affiliations": [
    "College of Chinese Medicine, Hunan University of Chinese Medicine, Changsha 410208, China"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Zhiqiang Wang"
    },
    {
      "affs": [
        0
      ],
      "name": "Zhenyu Zhao"
    },
    {
      "affs": [
        0
      ],
      "name": "Zhixi Hu",
      "orcid": "0000-0002-2316-8645"
    },
    {
      "affs": [
        0
      ],
      "name": "Jiahao Ye"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/egmcd6mqb9drk.bib",
  "cited_by": 0,
  "doi": "10.3390/metabo16100742",
  "fulltext_url": null,
  "id": "egmcd6mqb9drk",
  "issns": [
    "2218-1989"
  ],
  "landing_url": "https://www.mdpi.com/2218-1989/16/10/742",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2403|Atherosclerosis and Cardiovascular Diseases"
    ]
  ],
  "openalex": "W7215966071",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-01",
  "publisher": "MDPI AG",
  "ris": "https://research.draconitic.com/paper/egmcd6mqb9drk.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:00Z",
      "native_id": "10.3390/metabo16100742",
      "source": "crossref",
      "url": "https://www.mdpi.com/2218-1989/16/10/742"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "openalex:2403|Atherosclerosis and Cardiovascular Diseases",
      "subfield": "immunology"
    }
  ],
  "title": "Deciphering Vascular Smooth Muscle Cell Phenotype Switching via Integrated Single-Cell Transcriptomics and Machine Learning: CX3CR1 and COL1A1 as Novel Diagnostic Biomarkers for Atherosclerosis",
  "title_text": "Deciphering Vascular Smooth Muscle Cell Phenotype Switching via Integrated Single-Cell Transcriptomics and Machine Learning: CX3CR1 and COL1A1 as Novel Diagnostic Biomarkers for Atherosclerosis",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:19:00Z",
  "url": "https://research.draconitic.com/paper/egmcd6mqb9drk",
  "venue": "Metabolites",
  "versions": []
}