» Articles » PMID: 35581624

Human Liver Single Nucleus and Single cell RNA Sequencing Identify a Hepatocellular Carcinoma-associated Cell-type Affecting Survival

Overview
Journal Genome Med
Publisher Biomed Central
Specialty Genetics
Date 2022 May 17
PMID 35581624
Authors
Affiliations
Soon will be listed here.
Abstract

Background: Hepatocellular carcinoma (HCC) is a common primary liver cancer with poor overall survival. We hypothesized that there are HCC-associated cell-types that impact patient survival.

Methods: We combined liver single nucleus (snRNA-seq), single cell (scRNA-seq), and bulk RNA-sequencing (RNA-seq) data to search for cell-type differences in HCC. To first identify cell-types in HCC, adjacent non-tumor tissue, and normal liver, we integrated single-cell level data from a healthy liver cohort (n = 9 non-HCC samples) collected in the Strasbourg University Hospital; an HCC cohort (n = 1 non-HCC, n = 14 HCC-tumor, and n = 14 adjacent non-tumor samples) collected in the Singapore General Hospital and National University; and another HCC cohort (n = 3 HCC-tumor and n = 3 adjacent non-tumor samples) collected in the Dumont-UCLA Liver Cancer Center. We then leveraged these single cell level data to decompose the cell-types in liver bulk RNA-seq data from HCC patients' tumor (n = 361) and adjacent non-tumor tissue (n = 49) from the Cancer Genome Atlas (TCGA) multi-center cohort. For replication, we decomposed 221 HCC and 209 adjacent non-tumor liver microarray samples from the Liver Cancer Institute (LCI) cohort collected by the Liver Cancer Institute and Zhongshan Hospital of Fudan University.

Results: We discovered a tumor-associated proliferative cell-type, Prol (80.4% tumor cells), enriched for cell cycle and mitosis genes. In the liver bulk tissue from the TCGA cohort, the proportion of the Prol cell-type is significantly increased in HCC and associates with a worse overall survival. Independently from our decomposition analysis, we reciprocally show that Prol nuclei/cells significantly over-express both tumor-elevated and survival-decreasing genes obtained from the bulk tissue. Our replication analysis in the LCI cohort confirmed that an increased estimated proportion of the Prol cell-type in HCC is a significant marker for a shorter overall survival. Finally, we show that somatic mutations in the tumor suppressor genes TP53 and RB1 are linked to an increase of the Prol cell-type in HCC.

Conclusions: By integrating liver single cell, single nucleus, and bulk expression data from multiple cohorts we identified a proliferating cell-type (Prol) enriched in HCC tumors, associated with a decreased overall survival, and linked to TP53 and RB1 somatic mutations.

Citing Articles

Development of a prognostic model for hepatocellular carcinoma based on microvascular invasion characteristic genes by spatial transcriptomics sequencing.

Mu X, Pan L, Wang X, Liu C, Li Y, Cai Y Front Immunol. 2025; 16:1529569.

PMID: 40051627 PMC: 11882567. DOI: 10.3389/fimmu.2025.1529569.


Integrative single-cell and spatial transcriptome analysis reveals heterogeneity of human liver progenitor cells.

Liu C, Wang K, Mei J, Zhao R, Shen J, Zhang W Hepatol Commun. 2025; 9(3).

PMID: 40008906 PMC: 11868439. DOI: 10.1097/HC9.0000000000000662.


Single-cell transcriptomic analysis reveals characteristic feature of macrophage reprogramming in liver Mallory-Denk bodies pathogenesis.

Fang Z, Zhong B, Shi Y, Zhou W, Huang M, French S J Transl Med. 2025; 23(1):77.

PMID: 39819676 PMC: 11740356. DOI: 10.1186/s12967-024-05999-7.


Predicting Treatment Outcomes in Patients with Low Back Pain Using Gene Signature-Based Machine Learning Models.

Lian Y, Shi Y, Shang H, Zhan H Pain Ther. 2024; 14(1):359-373.

PMID: 39722081 PMC: 11751268. DOI: 10.1007/s40122-024-00700-8.


Single-cell transcriptomics reveals over-activated reactive oxygen species pathway in hepatocytes in the development of hepatocellular carcinoma.

Wang X, Li P, Ji H, Xu Z, Xing H Sci Rep. 2024; 14(1):29809.

PMID: 39616235 PMC: 11608336. DOI: 10.1038/s41598-024-81481-0.


References
1.
Byron S, Van Keuren-Jensen K, Engelthaler D, Carpten J, Craig D . Translating RNA sequencing into clinical diagnostics: opportunities and challenges. Nat Rev Genet. 2016; 17(5):257-71. PMC: 7097555. DOI: 10.1038/nrg.2016.10. View

2.
Grossman R, Heath A, Ferretti V, Varmus H, Lowy D, Kibbe W . Toward a Shared Vision for Cancer Genomic Data. N Engl J Med. 2016; 375(12):1109-12. PMC: 6309165. DOI: 10.1056/NEJMp1607591. View

3.
Gerami P, Cook R, Wilkinson J, Russell M, Dhillon N, Amaria R . Development of a prognostic genetic signature to predict the metastatic risk associated with cutaneous melanoma. Clin Cancer Res. 2015; 21(1):175-83. DOI: 10.1158/1078-0432.CCR-13-3316. View

4.
Liu J, Lichtenberg T, Hoadley K, Poisson L, Lazar A, Cherniack A . An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics. Cell. 2018; 173(2):400-416.e11. PMC: 6066282. DOI: 10.1016/j.cell.2018.02.052. View

5.
Aizarani N, Saviano A, Sagar , Mailly L, Durand S, Herman J . A human liver cell atlas reveals heterogeneity and epithelial progenitors. Nature. 2019; 572(7768):199-204. PMC: 6687507. DOI: 10.1038/s41586-019-1373-2. View