Integrating single-cell transcriptomics to construct an oncogene-driven prognostic model and elucidate metabolic-immune crosstalk in hepatocellular carcinoma
Computational cohort study reveals oncogene-driven metabolic and immune crosstalk in liver cancer, indicating potential pathways for personalized therapy.
Key Points
The study aimed to uncover how oncogenic driver genes and tumor microenvironment interactions influence hepatocellular carcinoma progression at single-cell resolution and develop a prognostic risk model.
Integrated bulk RNA-sequencing data from TCGA with multicenter single-cell transcriptomic datasets to establish a 575-gene hepatocellular carcinoma core set.
Developed a single-cell oncogene scoring system to quantify carcinogenic activity across cell populations.
Trained a machine learning-based Random Survival Forest (RSF) prognostic model and validated it across multiple independent patient cohorts.
Elevated oncogene scores localized predominantly to malignant cells and proliferative T cells, associating strongly with metabolic reprogramming, altered cell communication, and immunosuppressive features.
The RSF model stratified patients into risk tiers, linking the high-risk subgroup with genomic instability, elevated tumor stemness, and immune evasion.
Patients classified into the low-risk subgroup demonstrated greater sensitivity to targeted therapy with sorafenib.