Integrated analysis of single-cell transcriptome of liver cancer and cirrhosis reveals cell lineage similarity and prognostic-associated subpopulations

Abstract Background & Aims Liver cancer is one of the most leading causes of cancer deaths. Cirrhosis is an important risk factor for liver cancer, which is the result of over-fibrosis caused by diffuse and long-term liver damage. Despite extensive research, a systematic study for characterizing similarity between liver cancer and cirrhosis at single cell resolution is still lacking. Methods We established a data analysis framework to elucidate cell lineage similarity between liver cancer and cirrhosis to discover prognostic-associated subpopulations. We integrated single-cell transcriptome data from liver samples at normal, cirrhotic and cancer conditions, which totally contained 78,000 cells. Gene regulation analysis, cellular interactions and trajectory analysis were performed to characterize cirrhosis-like cell subpopulations. Bulk transcriptomes were used to discover prognostic-associated subpopulations. Results By aligning cellular subpopulations across different samples, we found remarkable similarity between KNG1 + hepatocytes in cirrhosis and PGA5 + hepatocytes in HCC. Furthermore, gene regulation analysis and cellular interactions implicated E2F1, FOXA2, EGF, CDH and ANGPTL signaling in maintaining cirrhosis-like ecosystem. Strikingly, subpopulations with higher expression of cirrhosis-like signatures were associated with patients’ worse survival. Conclusions We revealed a previously unexplored cirrhosis-like ecosystem of liver cancer, which could provide novel biomarkers for therapeutic interventions in HCC. Core analysis modules in this study were integrated into a user-friendly toolkit, SIM scRNA ( https://github.com/xmuhuanglab/SIM-scRNA ), which could facilitate the exploration of similarity and heterogeneity between precancerous diseases and solid tumors.