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Peer-reviewedCC BY-NC-ND 4.0Journal of Translational Medicine

Cancer-associated fibroblasts shape features of lymphocyte aggregation and collagen deposition in hepatocellular carcinoma

  • Yuan’e Lian1
  • Zehong Zhang2
  • Dongsheng Xia3
  • Zhongchang Weng2
  • Jiaying Ye2
  • Siqin Zhang2
  • Xingxing Huang4
  • Lianhuang Li5
  • Jie Lin6
  • Songqiang Zhou2
  • Shi Chen2,7
  • Yannan Bai2,7
7 affiliations
  1. 1Department of Pathology, The Affiliated Union Hospital of Fujian Medical University, Fuzhou, China
  2. 2Department of Hepatobiliary Pancreatic Surgery, Fujian Provincial Hospital, Shengli Clinical Medical College of Fujian Medical University, Fuzhou University Affiliated Provincial Hospital, Fuzhou, 350001 China
  3. 3School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China
  4. 4School of Arts and Sciences, Fujian Medical University, Fuzhou, China
  5. 5Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou, 350007 China
  6. 6Fujian Provincial Hospital, Shengli Clinical Medical College of Fujian Medical University, Fuzhou University Affiliated Provincial Hospital, Fuzhou, Fujian China
  7. 7Fuzhou University Affiliated Provincial Hospital, 134 Dongjie Street, Fuzhou, Fujian Province China
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Abstract

Background: The progression of hepatocellular carcinoma (HCC) is significantly influenced by the tumor microenvironment, in which cancer-associated fibroblasts (CAFs), collagen fibers (CFs), and tumor-infiltrating lymphocytes (TILs) interact to regulate tumor growth and immune infiltration. Understanding the spatial and functional coordination among these components is essential for prognostic assessment.

Methods: This retrospective study included 335 patients from two tertiary hospitals. Early-stage HCC tissue samples were analyzed using artificial intelligence-based algorithms to quantify TIL fraction and aggregation, while collagen architecture was characterized using multiphoton microscopy (MPM). Moreover, single-cell RNA sequencing was used for functional profiling of CAFs, and multiplex immunofluorescence was applied for spatial validation. The relationship between TILs and collagen features was associated with clinical outcomes. Prognostic models were developed using Cox regression and validated internally and externally.

Results: MPM demonstrated strong co-localization of CFs and TILs, with lymphocytes spatially aligned following collagen orientation within the tumor core. CAF subtypes exhibited heterogeneous expression of extracellular matrix (ECM)-related and lymphocyte-modulatory genes, with ECM-mediated signaling dominating CAF–T/B cell communication. TIL aggregation and CF orientation independently predicted disease-free and overall survival. The multifactorial Fiber-TIL-Clinical model (FTC model) achieved superior predictive performance (area under the curve = 0.902 for early recurrence) by integrating these features with clinical variables.

Conclusions: The FTC model integrates CF features and TIL characteristics, demonstrating strong performance in predicting HCC recurrence.

Supplementary Information: The online version contains supplementary material available at https://doi.org/10.1186/s12967-026-08686-x.

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Cite

BibTeXRIS
BibTeX
@article{lian2026cancerassociated,
  title = {{Cancer-associated fibroblasts shape features of lymphocyte aggregation and collagen deposition in hepatocellular carcinoma}},
  author = {Yuan’e Lian and Zehong Zhang and Dongsheng Xia and Zhongchang Weng and Jiaying Ye and Siqin Zhang and Xingxing Huang and Lianhuang Li and Jie Lin and Songqiang Zhou and Shi Chen and Yannan Bai},
  journal = {Journal of Translational Medicine},
  year = {2026},
  month = jul,
  doi = {10.1186/s12967-026-08686-x},
  url = {https://doi.org/10.1186/s12967-026-08686-x},
  note = {CC BY-NC-ND 4.0},
}
RIS
TY  - JOUR
TI  - Cancer-associated fibroblasts shape features of lymphocyte aggregation and collagen deposition in hepatocellular carcinoma
AU  - Yuan’e Lian
AU  - Zehong Zhang
AU  - Dongsheng Xia
AU  - Zhongchang Weng
AU  - Jiaying Ye
AU  - Siqin Zhang
AU  - Xingxing Huang
AU  - Lianhuang Li
AU  - Jie Lin
AU  - Songqiang Zhou
AU  - Shi Chen
AU  - Yannan Bai
PY  - 2026
DA  - 2026/07/30
JO  - Journal of Translational Medicine
DO  - 10.1186/s12967-026-08686-x
UR  - https://doi.org/10.1186/s12967-026-08686-x
AB  - Background: The progression of hepatocellular carcinoma (HCC) is significantly influenced by the tumor microenvironment, in which cancer-associated fibroblasts (CAFs), collagen fibers (CFs), and tumor-infiltrating lymphocytes (TILs) interact to regulate tumor growth and immune infiltration. Understanding the spatial and functional coordination among these components is essential for prognostic assessment.  Methods: This retrospective study included 335 patients from two tertiary hospitals. Early-stage HCC tissue samples were analyzed using artificial intelligence-based algorithms to quantify TIL fraction and aggregation, while collagen architecture was characterized using multiphoton microscopy (MPM). Moreover, single-cell RNA sequencing was used for functional profiling of CAFs, and multiplex immunofluorescence was applied for spatial validation. The relationship between TILs and collagen features was associated with clinical outcomes. Prognostic models were developed using Cox regression and validated internally and externally.  Results: MPM demonstrated strong co-localization of CFs and TILs, with lymphocytes spatially aligned following collagen orientation within the tumor core. CAF subtypes exhibited heterogeneous expression of extracellular matrix (ECM)-related and lymphocyte-modulatory genes, with ECM-mediated signaling dominating CAF–T/B cell communication. TIL aggregation and CF orientation independently predicted disease-free and overall survival. The multifactorial Fiber-TIL-Clinical model (FTC model) achieved superior predictive performance (area under the curve = 0.902 for early recurrence) by integrating these features with clinical variables.  Conclusions: The FTC model integrates CF features and TIL characteristics, demonstrating strong performance in predicting HCC recurrence.  Supplementary Information: The online version contains supplementary material available at https://doi.org/10.1186/s12967-026-08686-x.
SN  - 1479-5876
ER  -