{
  "abstract": "Background: Intrahepatic cholangiocarcinoma (ICC) exhibits substantial cellular heterogeneity and a complex tumor microenvironment (TME), contributing to poor clinical outcomes. Effective prognostic biomarkers and a deeper understanding of the cellular ecosystems associated with high-risk disease remain urgently needed.\n\nMethods: We integrated five scRNA-seq datasets from GEO with four publicly available spatial transcriptomic sections and bulk transcriptomic data from the TCGA, fu-ICCA, and GEO cohorts. A multidimensional framework combining single-cell analysis, spatial transcriptomics, and machine learning was used to characterize cellular heterogeneity and identify prognostically relevant features. Hotspot was used to characterize T- and B-cell transcriptional modules, Scissor was applied to identify prognosis-associated malignant epithelial cells, and 101 machine-learning pipelines were evaluated to construct the ICCS prognostic signature. Cell–cell communication analysis and exploratory spatial co-density analysis were further performed to characterize ICCS-associated stromal–epithelial interactions, while in silico perturbation and functional assays were used to investigate WFDC3.\n\nResults: The single-cell atlas identified 11 major cell populations and prognosis-associated T- and B-cell transcriptional programs. A six-gene ICCS comprising CEP55, EGLN3, GPRC5A, PRR15, SFN, and WFDC3 showed reproducible prognostic discrimination across the analyzed cohorts. ICCS-High displayed a markedly epithelial-dominant cellular composition, with epithelial cells increasing from 15.47% in ICCS-Low to 78.44% in ICCS-High, whereas T/NK cells decreased from 44.28% to 3.95%. Comparative cell–cell communication analysis revealed prominent mesenchymal-to-epithelial signaling enriched for collagen-related ligand–receptor interactions. COL4A1–CD44 showed the highest relative contribution and a more prominent mesenchymal–epithelial communication pattern in ICCS-High. Exploratory spatial density maps further identified local co-occurrence of mesenchymal and epithelial signals, supporting an ECM-associated microenvironmental context in the high-risk state. Separately, in silico WFDC3 perturbation implicated tumor-related pathways, and WFDC3 knockdown reduced colony formation, migration, and invasion of ICC cells.\n\nConclusion: ICCS stratification captures distinct cellular and molecular states of the ICC microenvironment. The ICCS-High state is characterized by epithelial predominance, reduced T/NK-cell representation, and stronger inferred mesenchymal-to-epithelial collagen-related communication. COL4A1-CD44 and WFDC3 should be regarded as hypothesis-generating biological features requiring further mechanistic validation. ICCS remains a research-stage prognostic signature that requires standardized assays and independent prospective validation before clinical application.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Hepatobiliary Surgery, Zhangjiagang Hospital Affiliated to Soochow University/Zhangjiagang First People’s Hospital",
    "Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University",
    "Department of Oncology, The First Affiliated Hospital of Anhui Medical University"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 9,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Zhencheng Zhu"
    },
    {
      "affs": [
        1
      ],
      "name": "Tao Jiang"
    },
    {
      "affs": [
        0
      ],
      "name": "Qiuhua Liu"
    },
    {
      "affs": [
        0
      ],
      "name": "Bo Zhang"
    },
    {
      "affs": [
        0
      ],
      "name": "Gang Wang"
    },
    {
      "affs": [
        0
      ],
      "name": "Pin Huang"
    },
    {
      "affs": [
        0
      ],
      "name": "Yue Liu"
    },
    {
      "affs": [
        2
      ],
      "name": "Dingtao Hu"
    },
    {
      "affs": [
        0
      ],
      "name": "Ke Guo"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8fxr0jzmnr2p0.bib",
  "cited_by": 0,
  "doi": "10.3389/fimmu.2026.1903609",
  "fulltext_url": "https://europepmc.org/articles/PMC13687428",
  "id": "8fxr0jzmnr2p0",
  "issns": [
    "1664-3224"
  ],
  "landing_url": "https://www.frontiersin.org/articles/10.3389/fimmu.2026.1903609/full",
  "language": "eng",
  "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",
      "2746|Cholangiocarcinoma and Gallbladder Cancer Studies"
    ]
  ],
  "openalex": "W7221009230",
  "pdf_url": "https://europepmc.org/articles/PMC13687428?pdf=render",
  "pmcid": "PMC13687428",
  "pmid": "42923340",
  "published": "2026-10-08",
  "publisher": "Frontiers Media SA",
  "ris": "https://research.draconitic.com/paper/8fxr0jzmnr2p0.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:15Z",
      "native_id": "10.3389/fimmu.2026.1903609",
      "source": "crossref",
      "url": "https://www.frontiersin.org/articles/10.3389/fimmu.2026.1903609/full"
    },
    {
      "first_seen": "2026-10-30T12:20:33Z",
      "native_id": "MED:42923340",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42923340"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2746|Cholangiocarcinoma and Gallbladder Cancer Studies",
      "subfield": "surgery"
    }
  ],
  "title": "Single-cell and spatial multi-omics combined with machine learning to unravel the heterogeneity of cholangiocarcinoma and the WFDC3-mediated prognostic regulatory mechanisms",
  "title_text": "Single-cell and spatial multi-omics combined with machine learning to unravel the heterogeneity of cholangiocarcinoma and the WFDC3-mediated prognostic regulatory mechanisms",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:20:33Z",
  "url": "https://research.draconitic.com/paper/8fxr0jzmnr2p0",
  "venue": "Frontiers in Immunology",
  "versions": []
}