{
  "abstract": "Acute Myocardial infarction (AMI) continues to be a severe deadly disease with a high case-fatality rate and great psychological and economic costs. Despite the development of new therapies, early diagnosis and treatment optimization still are problematical. Here we applied the integrated bioinformatics strategy to screen potential new diagnosis biomarkers for AMI based on the inflammation-related cGAS-STING pathway. By integrating differentially expressed genes (DEGs), weighted gene co-expression network analysis (WGCNA), and cGAS-STING gene set from high-throughput RNA sequencing, we obtained a total of 11 candidate genes from 914 DEGs, which enriched mainly for inflammatory and immune response pathways. Further refining them in machine learning algorithms, we identified core genes with diagnostic values. Subsequent test shows the AUC values of PFKFB3 and TLR2 both were more than 0.7 and their promising diagnostic values were verified. Based on the immune infiltration analysis, expression of PFKFB3 and TLR2 were very significantly associated with a large number of immune cells, such as monocytes, neutrophils and dendritic cells. As for single cell sequencing analysis, they were mainly found expressed on macrophages. Overall, we show that PFKFB3 and TLR2 are possible biomarkers of AMI which have not been reported so far. Thus they may open a path for new diagnostics and personalized therapy. Validation in independent populations is needed.",
  "abstract_shown": true,
  "affiliations": [
    "Shengli Clinical Medical College of Fujian Medical University, Fuzhou, Fujian, China",
    "Department of Cardiology, The First People’s Hospital of Yibin, Yibin, Sichuan, China",
    "School of Pharmacy, Fujian Medical University, Fuzhou, Fujian, China"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Juying Li"
    },
    {
      "affs": [
        1
      ],
      "name": "Hongkui Chen"
    },
    {
      "affs": [
        2
      ],
      "name": "Yihao Huang"
    },
    {
      "name": "Yupeng Zhi"
    },
    {
      "name": "Chun Chen"
    },
    {
      "name": "Yansong Guo"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/25bde6fqdhsne.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pone.0355694",
  "fulltext_url": null,
  "id": "25bde6fqdhsne",
  "issns": [
    "1932-6203"
  ],
  "landing_url": "https://doi.org/10.1371/journal.pone.0355694",
  "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",
      "1312|Bioinformatics and Genomic Networks"
    ],
    [
      "plos",
      "/Biology and life sciences/Biochemistry/Biomarkers"
    ],
    [
      "plos",
      "/Biology and life sciences/Biochemistry/Proteins/DNA-binding proteins/Transcription factors"
    ],
    [
      "plos",
      "/Biology and life sciences/Biochemistry/Proteins/Regulatory proteins/Transcription factors"
    ],
    [
      "plos",
      "/Biology and life sciences/Cell biology/Cellular types/Animal cells/Blood cells/White blood cells/Macrophages"
    ],
    [
      "plos",
      "/Biology and life sciences/Cell biology/Cellular types/Animal cells/Immune cells"
    ],
    [
      "plos",
      "/Biology and life sciences/Cell biology/Cellular types/Animal cells/Immune cells/White blood cells/Macrophages"
    ],
    [
      "plos",
      "/Biology and life sciences/Genetics/Gene expression"
    ],
    [
      "plos",
      "/Biology and life sciences/Genetics/Gene expression/Gene regulation/Transcription factors"
    ],
    [
      "plos",
      "/Biology and life sciences/Immunology/Immune cells"
    ],
    [
      "plos",
      "/Biology and life sciences/Immunology/Immune cells/White blood cells/Macrophages"
    ],
    [
      "plos",
      "/Biology and life sciences/Immunology/Immune response/Inflammation"
    ],
    [
      "plos",
      "/Computer and information sciences/Artificial intelligence/Machine learning"
    ],
    [
      "plos",
      "/Medicine and health sciences/Cardiology/Myocardial infarction"
    ],
    [
      "plos",
      "/Medicine and health sciences/Clinical medicine/Signs and symptoms/Inflammation"
    ],
    [
      "plos",
      "/Medicine and health sciences/Immunology/Immune cells"
    ],
    [
      "plos",
      "/Medicine and health sciences/Immunology/Immune cells/White blood cells/Macrophages"
    ],
    [
      "plos",
      "/Medicine and health sciences/Immunology/Immune response/Inflammation"
    ]
  ],
  "openalex": "W7213538330",
  "pdf_url": "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0355694&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-18",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/25bde6fqdhsne.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:49Z",
      "native_id": "10.1371/journal.pone.0355694",
      "source": "plos",
      "url": "https://doi.org/10.1371/journal.pone.0355694"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "plos:cell biology",
      "subfield": "cell-biology"
    },
    {
      "field": "biology",
      "origin": "plos:immunology",
      "subfield": "immunology"
    }
  ],
  "title": "The cGAS-STING pathway in acute myocardial infarction: Biomarker discovery and mechanistic insights",
  "title_text": "The cGAS-STING pathway in acute myocardial infarction: Biomarker discovery and mechanistic insights",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:49Z",
  "url": "https://research.draconitic.com/paper/25bde6fqdhsne",
  "venue": "PLOS One",
  "versions": []
}