{
  "abstract": "Background: Endothelial to mesenchymal transition (EndMT) is a key driver of atherosclerosis (AS). This study integrated multiple RNA transcriptomic and single-cell transcriptomic datasets to identify shared gene expression signatures between EndMT and AS.\n\nMethods: GEO expression profiles were analyzed via differential expression, weighted gene co-expression network analysis (WGCNA), and functional enrichment analyses. A Random Forest model evaluated key genes, validating their expression and diagnostic performance. Single-cell RNA sequencing (scRNA-seq) further assessed their expression patterns, and a Transcription Factor (TF)-mRNA-miRNA regulatory network.\n\nResults: Fifteen overlapping genes were identified, from which six candidates were screened through a Protein-Protein Interaction (PPI) network and validated in vitro . The application of a Random Forest model identified two key hub genes, CCL5 and IL1B , which were significantly upregulated. Their diagnostic Area Under the Curve (AUC) was >0.7 when assessed separately and increased to >0.85 in a combined model. Three shared TFs (NFKBIA, NFKB1, RELA) of the Nuclear Factor-kappa B (NF-κB) pathway were identified, highlighting its central role in disease progression.\n\nConclusions: CCL5 and IL1B are key genes potentially regulating EndMT-mediated AS via inflammatory pathways including Nucleotide-binding oligomerization domain-like (NOD-like) receptor, Tumor Necrosis Factor (TNF), and NF-κB signaling, offering promising diagnostic and therapeutic targets for AS.",
  "abstract_shown": true,
  "affiliations": [
    "Chongqing University of Post and Telecommunications, Chongqing, China",
    "Chongqing Industry Polytechnic University, Chongqing, China",
    "Chongqing Blood Center, Chongqing, China"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Zhengjun Hou"
    },
    {
      "affs": [
        0
      ],
      "name": "Sihan Chen"
    },
    {
      "affs": [
        1
      ],
      "name": "Yuliang Cui"
    },
    {
      "affs": [
        2
      ],
      "name": "Qinfeng Ma"
    },
    {
      "affs": [
        0
      ],
      "name": "Jiandong Chen"
    },
    {
      "affs": [
        0
      ],
      "name": "Kunxian Shu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/3hk3q506gxn5w.bib",
  "cited_by": 0,
  "doi": "10.7717/peerj.21863",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13741981/",
  "id": "3hk3q506gxn5w",
  "issns": [
    "2167-8359"
  ],
  "landing_url": "https://peerj.com/articles/21863",
  "language": "en",
  "licence": "CC-BY-NC-4.0",
  "licence_label": "CC BY-NC 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc/4.0",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Bioinformatics"
    ],
    [
      "heading",
      "Cardiology"
    ],
    [
      "heading",
      "Vascular Medicine"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://peerj.com/articles/21863.pdf",
  "pmcid": "PMC13741981",
  "pmid": "42997443",
  "published": "2026-11-03",
  "publisher": "PeerJ",
  "ris": "https://research.draconitic.com/paper/3hk3q506gxn5w.ris",
  "sources": [
    {
      "first_seen": "2026-11-03T12:20:21Z",
      "native_id": "10.7717/peerj.21863",
      "source": "crossref",
      "url": "https://peerj.com/articles/21863"
    },
    {
      "first_seen": "2026-11-10T12:22:54Z",
      "native_id": "PMC13741981",
      "source": "pmc",
      "url": "https://doi.org/10.7717/peerj.21863"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "heading:Bioinformatics",
      "subfield": "bioinformatics"
    },
    {
      "field": "medicine",
      "origin": "heading:Cardiology",
      "subfield": "cardiovascular"
    }
  ],
  "title": "Identifying potential targets in endothelial to mesenchymal transition-mediated atherosclerosis using bioinformatics and machine learning",
  "title_text": "Identifying potential targets in endothelial to mesenchymal transition-mediated atherosclerosis using bioinformatics and machine learning",
  "type": "peer-reviewed",
  "updated_at": "2026-11-10T12:22:54Z",
  "url": "https://research.draconitic.com/paper/3hk3q506gxn5w",
  "venue": "PeerJ",
  "versions": []
}