Deciphering the hepatocellular carcinoma landscape of hepatocytes through integrative single-nucleus and bulk RNA-seq
The repository contains the code utilized in our manuscript. To protect patient confidentiality and intellectual property, select portions of the code are not openly shared. Interested parties may request access from the authors.
(Our manuscript is still under revision, and the serial numbers of all papers will be organized uniformly before publication.)
Serial numbers 1-100: mainly for snRNA-seq data analysis.
Serial number 101-200: mainly for bulk RNA-seq data analysis.
The versions of R, python and related packages relied on for the analysis can be viewed in session.txt in the dataset directory.
Hepatocellular carcinoma (HCC) remains poorly understood at the level of hepatocyte oncogenic subtypes, despite extensive studies on the tumor microenvironment. In this study, we performed single-nucleus RNA sequencing on samples from 22 HCC patients, identifying 10 distinct hepatocyte subtypes, particularly HCC progression related Hep0 (beneficial), Hep2 (predominantly malignant), and Hep9 (immunosuppressive). By integrating these findings with bulk RNA sequencing data from 165 samples across 55 patients, we found the key genes, pathways, and transcription factors associated with HCC. Further analysis of the TCGA-LIHC and Fudan HCC cohorts validated that the Hep0/2/9 subtypes significantly impact patient prognosis. Additionally, we developed a quantile-based scoring method by integrating 29 publicly available HCC datasets (4,192 samples) and constructed a Quantile Distribution Model (QDM) for HCC. The QDM demonstrated excellent diagnostic performance (AUC = 0.968–0.982) and identified a set of potential HCC biomarkers. Using feature genes from the Hep0/2/9 subtypes, we classified HCC into metabolic, inflammatory, and matrix classes, revealing distinct clinical characteristics and potential therapeutic agents. This study provides comprehensive insights into the oncogenic landscape of hepatocytes in HCC, establishes the largest single-cell resource for hepatocytes to date, and offers a framework for future research and clinical applications.