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Single-cell and machine learning reveal ROS-associated heterogeneity in hepatocellular carcinoma for precision
Xuesong Xia1, Haoxuan Xu2, Gang Lin3
1Department of Hepatobiliary Pancreatic Surgery, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial Hospital, Fuzhou, China.
BMC Cancer
|May 14, 2026
Summary
Reactive oxygen species (ROS) drive hepatocellular carcinoma (HCC) progression. This study identifies ROS-driven subtypes and a novel signature for predicting HCC prognosis and guiding targeted therapies.
Area of Science:
- Oncology
- Molecular Biology
- Computational Biology
Background:
- Reactive oxygen species (ROS) play a key role in tumor development, but their specific mechanisms in hepatocellular carcinoma (HCC) are unclear.
- Understanding ROS heterogeneity is crucial for HCC treatment.
Purpose of the Study:
- To investigate ROS-related heterogeneity in HCC using a computational approach.
- To develop a prognostic model and ROS signature for HCC.
Main Methods:
- Integrated bulk and single-cell transcriptomic analyses.
- Applied machine learning to create a ROS signature.
- Validated findings through laboratory experiments.
Main Results:
- Identified a high-ROS HCC subtype with immunosuppression and poor outcomes.
- High ROS in tumor cells correlated with altered intercellular communication and metabolism.
- Developed a ROS signature that accurately predicts prognosis and treatment response.
Conclusions:
- Systematically characterized ROS-driven molecular and cellular heterogeneity in HCC.
- Provides insights for precision medicine and targeted therapies in HCC.
- Highlights PFKP inhibition as a potential therapeutic strategy.
