Deciphering the Oncogenic Landscape of Hepatocytes Through Integrated Single-Nucleus and Bulk RNA-Seq of Hepatocellular Carcinoma

Abstract

Hepatocellular carcinoma (HCC) is a major cause of cancer-related mortality, while the hepatocyte mechanisms driving oncogenesis remains poorly understood. In this study, single-nucleus RNA sequencing of samples from 22 HCC patients revealed 10 distinct hepatocyte subtypes, including beneficial Hep0, predominantly malignant Hep2, and immunosuppressive Hep9. These subtypes were strongly associated with patient prognosis, confirmed in TCGA-LIHC and Fudan HCC cohorts through hepatocyte composition deconvolution. A quantile-based scoring method is developed to integrate data from 29 public HCC datasets, creating a Quantile Distribution Model (QDM) with excellent diagnostic accuracy (Area Under the Curve, AUC = 0.968-0.982). QDM was employed to screen potential biomarkers, revealing that PDE7B functions as a key gene whose suppression promotes HCC progression. Guided by the genes specific to Hep0/2/9 subtypes, HCC is categorized into metabolic, inflammatory, and matrix classes, which are distinguishable in gene mutation frequencies, survival times, enriched pathways, and immune infiltration. Meanwhile, the sensitive drugs of the three HCC classes are identified, namely ouabain, teniposide, and TG-101348. This study presents the largest single-cell hepatocyte dataset to date, offering transformative insights into hepatocarcinogenesis and a comprehensive framework for advancing HCC diagnostics, prognostics, and personalized treatment strategies.