{
  "abstract": "Background: Sepsis and thoracic aortic aneurysm (TAA) share pathological features of inflammation and vascular dysfunction, yet their molecular connections remain unclear. Methods: Transcriptomic datasets of sepsis and TAA from GEO were integrated to identify shared differentially expressed genes (DEGs). Functional enrichment and gene set enrichment analyses were performed to explore potential pathways. Machine learning models were applied to identify candidate genes with discriminatory potential, followed by immune infiltration analysis and assessment of gene expression in lipopolysaccharide (LPS)-treated vascular smooth muscle cells (VSMCs). Results: A total of 193 shared DEGs were identified, mainly enriched in T cell activation, Th17 differentiation, and oxidative stress–related pathways. Five candidate genes (ARG1, IL18R1, LIX1L, TRIM32, and PRKCH) were selected through sequential machine-learning analysis and showed generally reproducible expression patterns across independent datasets. Their expression was associated with estimated immune cell proportions. In LPS-treated VSMCs, ARG1 and IL18R1 were upregulated, whereas LIX1L was downregulated. Conclusion: Sepsis and TAA share common transcriptional signatures involving immune dysregulation and metabolic stress. ARG1 and IL18R1 may represent candidate molecular features associated with inflammatory and vascular responses.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Cardiothoracic Surgery, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang City, Hunan Province, China"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Chengqun She"
    },
    {
      "name": "Hao Deng"
    },
    {
      "name": "Yaoguang Feng"
    },
    {
      "name": "Chaozhong Long"
    },
    {
      "name": "Juan Wu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/ecxjsbt9mrjhc.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pone.0359784",
  "fulltext_url": null,
  "id": "ecxjsbt9mrjhc",
  "issns": [
    "1932-6203"
  ],
  "landing_url": "https://doi.org/10.1371/journal.pone.0359784",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2713|Sepsis Diagnosis and Treatment"
    ],
    [
      "plos",
      "/Biology and life sciences/Cell biology/Cellular types/Animal cells/Immune cells"
    ],
    [
      "plos",
      "/Biology and life sciences/Computational biology/Genome analysis/Transcriptome analysis"
    ],
    [
      "plos",
      "/Biology and life sciences/Genetics/Gene expression"
    ],
    [
      "plos",
      "/Biology and life sciences/Genetics/Genomics/Genome analysis/Transcriptome analysis"
    ],
    [
      "plos",
      "/Biology and life sciences/Immunology/Immune cells"
    ],
    [
      "plos",
      "/Biology and life sciences/Immunology/Immune response/Inflammation"
    ],
    [
      "plos",
      "/Computer and information sciences/Artificial intelligence/Machine learning"
    ],
    [
      "plos",
      "/Medicine and health sciences/Clinical medicine/Signs and symptoms/Inflammation"
    ],
    [
      "plos",
      "/Medicine and health sciences/Clinical medicine/Signs and symptoms/Sepsis"
    ],
    [
      "plos",
      "/Medicine and health sciences/Immunology/Immune cells"
    ],
    [
      "plos",
      "/Medicine and health sciences/Immunology/Immune response/Inflammation"
    ],
    [
      "plos",
      "/Medicine and health sciences/Medical conditions/Inflammatory diseases"
    ],
    [
      "plos",
      "/Research and analysis methods/Bioassays and physiological analysis/Microarrays"
    ]
  ],
  "openalex": "W7221014803",
  "pdf_url": "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0359784&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-08",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/ecxjsbt9mrjhc.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:30Z",
      "native_id": "10.1371/journal.pone.0359784",
      "source": "plos",
      "url": "https://doi.org/10.1371/journal.pone.0359784"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "plos:immunology",
      "subfield": "immunology"
    }
  ],
  "title": "Identification of shared diagnostic biomarkers and immune-related mechanisms between sepsis and thoracic aortic aneurysm based on integrated bioinformatics and machine learning approaches",
  "title_text": "Identification of shared diagnostic biomarkers and immune-related mechanisms between sepsis and thoracic aortic aneurysm based on integrated bioinformatics and machine learning approaches",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:30Z",
  "url": "https://research.draconitic.com/paper/ecxjsbt9mrjhc",
  "venue": "PLOS One",
  "versions": []
}