Single-cell and spatial multi-omics combined with machine learning to unravel the heterogeneity of cholangiocarcinoma and the WFDC3-mediated prognostic regulatory mechanisms
- 1Department of Hepatobiliary Surgery, Zhangjiagang Hospital Affiliated to Soochow University/Zhangjiagang First People’s Hospital
- 2Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University
- 3Department of Oncology, The First Affiliated Hospital of Anhui Medical University
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.
Methods: 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.
Results: 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.
Conclusion: 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.
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BibTeX
@article{zhu2026singlecell,
title = {{Single-cell and spatial multi-omics combined with machine learning to unravel the heterogeneity of cholangiocarcinoma and the WFDC3-mediated prognostic regulatory mechanisms}},
author = {Zhencheng Zhu and Tao Jiang and Qiuhua Liu and Bo Zhang and Gang Wang and Pin Huang and Yue Liu and Dingtao Hu and Ke Guo},
journal = {Frontiers in Immunology},
year = {2026},
month = oct,
doi = {10.3389/fimmu.2026.1903609},
url = {https://www.frontiersin.org/articles/10.3389/fimmu.2026.1903609/full},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Single-cell and spatial multi-omics combined with machine learning to unravel the heterogeneity of cholangiocarcinoma and the WFDC3-mediated prognostic regulatory mechanisms
AU - Zhencheng Zhu
AU - Tao Jiang
AU - Qiuhua Liu
AU - Bo Zhang
AU - Gang Wang
AU - Pin Huang
AU - Yue Liu
AU - Dingtao Hu
AU - Ke Guo
PY - 2026
DA - 2026/10/08
JO - Frontiers in Immunology
DO - 10.3389/fimmu.2026.1903609
UR - https://www.frontiersin.org/articles/10.3389/fimmu.2026.1903609/full
AB - 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. Methods: 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. Results: 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. Conclusion: 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.
SN - 1664-3224
ER -