Skip to content
DraconiticResearch
Search Sources API
190,860 studies31 sourcesUpdated 52 min ago
Peer-reviewedCC BY-NC 4.0Cell Reports Medicine

Multi-omics-driven personalized management of advanced HCC

  • Jianpeng Sheng1,2,3
  • Jinyuan Song4,5,6
  • Lin Wang1
  • Junlei Zhang4,5,6
  • Wei Zhang7
  • Hang Shi1
  • Yaxing Zhao8
  • Chengxiang Guo5,6
  • Jianghui Tang4,5,6
  • Yongtao Ji4,5,6
  • Taohong Li8
  • Wei Shao1
3 more authors
  • Xueli Bai4,5,6
  • Qing Li3
  • Tingbo Liang4,5,6
8 affiliations
  1. 1College of Artificial Intelligence, Nanjing University of Aeronautics and Astronautics, Nanjing 210000, China
  2. 2Chinese Institutes for Medical Research, Beijing 100069, China
  3. 3Shapingba Hospital affiliated to Chongqing University (Shapingba District People’s Hospital of Chongqing), Chongqing 400016, China
  4. 4Zhejiang Provincial Key Laboratory of Pancreatic Disease, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310002, China
  5. 5Department of Hepatobiliary and Pancreatic Surgery, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310002, China
  6. 6Zhejiang University Cancer Center, Zhejiang University, Hangzhou 310002, China
  7. 7Department of medical oncology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital&Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu 611731, China
  8. 8Infinity Scope Inc., Zhejiang 311200, China
Read full textDOICite

Abstract

Hepatocellular carcinoma (HCC) management is challenging due to its complex tumor microenvironment and poor treatment responses. Here, using tumor specimens from a prospective clinical trial of combined transarterial chemoembolization (TACE) with immune checkpoint blockade (ICB), we perform exhaustive multi-omics analysis including spatial proteomics and transcriptomics, single-cell RNA sequencing, and bulk transcriptomics. These analyses reveal that treatment response is associated with enrichment of anti-tumor T cell regions that are regulated by cGAS-STING activation within immune-suppressive epithelial cells. Conversely, fibrotic processes impede these pro-response processes. Based on these insights, we test triple combination therapy consisting of cGAS activation, immune checkpoint blockade, and anti-fibrosis strategies, which shows improved efficacy over dual therapy. To identify patients who would benefit, we construct a predictive model using a group sparse learning algorithm. Our findings provide a blueprint for crafting personalized HCC therapies using next-generation biomarkers.

Related

  1. Editorial: The insights of multi-omics into the microenvironment after tumor metastasis: a paradigm shift in molecular targeting modeling and immunotherapy for advanced cancer patients, volume II
    Mengyan Xie, Hong Chang, Xue Zhao +5
    Frontiers in OncologyPeer-reviewedCC BY 4.0Full textOncologyCrossref
  2. AI-driven multi-omics biomarkers in precision breast cancer therapeutics: from discovery to clinical translation
    Dimitrios Stylianakis, Rodrigo Sánchez-Bayona, Nerina Denaro +1
    Frontiers in pharmacologyPeer-reviewedCC BYFull textPharmacology & toxicologyEurope PMC
  3. AI-Driven Multi-Omics Approaches for Deciphering Small-Cell Lung Cancer Heterogeneity Using Nucleic-Acid-Based Features
    Saman Zeeshan, Caila McVey, Shafaq Majeed +2
    CancersPeer-reviewedCC BYFull textOncologyEurope PMC
  4. A multi-omics analysis of advanced triple-negative breast cancer identifies predictors to chemo-immunotherapy and on-treatment dynamics
    Nicolaas Van Renne, Elisa Agostinetto, Andrea Joaquín García +33
    Cell Reports MedicinePeer-reviewedCC BY 4.0Full textOncologyPMC
  5. The evolution of sepsis care: from protocol-driven management to personalized intensive care
    Tim Rahmel, Benedikt H. Siegler, Markus A. Weigand +5
    InfectionPeer-reviewedCC BY 4.0Full text3 citedEpidemiology & public healthPMC

Cite

BibTeXRIS
BibTeX
@article{sheng2026multiomicsdriven,
  title = {{Multi-omics-driven personalized management of advanced HCC}},
  author = {Jianpeng Sheng and Jinyuan Song and Lin Wang and Junlei Zhang and Wei Zhang and Hang Shi and Yaxing Zhao and Chengxiang Guo and Jianghui Tang and Yongtao Ji and Taohong Li and Wei Shao and Xueli Bai and Qing Li and Tingbo Liang},
  journal = {Cell Reports Medicine},
  year = {2026},
  month = oct,
  doi = {10.1016/j.xcrm.2026.103085},
  url = {https://doi.org/10.1016/j.xcrm.2026.103085},
  note = {CC BY-NC 4.0},
}
RIS
TY  - JOUR
TI  - Multi-omics-driven personalized management of advanced HCC
AU  - Jianpeng Sheng
AU  - Jinyuan Song
AU  - Lin Wang
AU  - Junlei Zhang
AU  - Wei Zhang
AU  - Hang Shi
AU  - Yaxing Zhao
AU  - Chengxiang Guo
AU  - Jianghui Tang
AU  - Yongtao Ji
AU  - Taohong Li
AU  - Wei Shao
AU  - Xueli Bai
AU  - Qing Li
AU  - Tingbo Liang
PY  - 2026
DA  - 2026/10/02
JO  - Cell Reports Medicine
DO  - 10.1016/j.xcrm.2026.103085
UR  - https://doi.org/10.1016/j.xcrm.2026.103085
AB  - Hepatocellular carcinoma (HCC) management is challenging due to its complex tumor microenvironment and poor treatment responses. Here, using tumor specimens from a prospective clinical trial of combined transarterial chemoembolization (TACE) with immune checkpoint blockade (ICB), we perform exhaustive multi-omics analysis including spatial proteomics and transcriptomics, single-cell RNA sequencing, and bulk transcriptomics. These analyses reveal that treatment response is associated with enrichment of anti-tumor T cell regions that are regulated by cGAS-STING activation within immune-suppressive epithelial cells. Conversely, fibrotic processes impede these pro-response processes. Based on these insights, we test triple combination therapy consisting of cGAS activation, immune checkpoint blockade, and anti-fibrosis strategies, which shows improved efficacy over dual therapy. To identify patients who would benefit, we construct a predictive model using a group sparse learning algorithm. Our findings provide a blueprint for crafting personalized HCC therapies using next-generation biomarkers.
SN  - 2666-3791
ER  -