Analysis on heterogeneity of hepatocellular carcinoma immune cells and a molecular risk model by integration of scRNA-seq and bulk RNA-seq

BACKGROUND: Studies have shown that hepatocellular carcinoma (HCC) heterogeneity is a main cause leading to failure of treatment. Technology of single-cell sequencing (scRNA) could more accurately reveal the essential characteristics of tumor genetics. METHODS: From the Gene Expression Omnibus (GEO)...

Descripción completa

Detalles Bibliográficos
Autores principales: Liu, Xiaorui, Li, Jingjing, Wang, Qingxiang, Bai, Lu, Xing, Jiyuan, Hu, Xiaobo, Li, Shuang, Li, Qinggang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9606610/
https://www.ncbi.nlm.nih.gov/pubmed/36311759
http://dx.doi.org/10.3389/fimmu.2022.1012303
_version_ 1784818334366171136
author Liu, Xiaorui
Li, Jingjing
Wang, Qingxiang
Bai, Lu
Xing, Jiyuan
Hu, Xiaobo
Li, Shuang
Li, Qinggang
author_facet Liu, Xiaorui
Li, Jingjing
Wang, Qingxiang
Bai, Lu
Xing, Jiyuan
Hu, Xiaobo
Li, Shuang
Li, Qinggang
author_sort Liu, Xiaorui
collection PubMed
description BACKGROUND: Studies have shown that hepatocellular carcinoma (HCC) heterogeneity is a main cause leading to failure of treatment. Technology of single-cell sequencing (scRNA) could more accurately reveal the essential characteristics of tumor genetics. METHODS: From the Gene Expression Omnibus (GEO) database, HCC scRNA-seq data were extracted. The FindCluster function was applied to analyze cell clusters. Autophagy-related genes were acquired from the MSigDB database. The ConsensusClusterPlus package was used to identify molecular subtypes. A prognostic risk model was built with the Least Absolute Shrinkage and Selection Operator (LASSO)–Cox algorithm. A nomogram including a prognostic risk model and multiple clinicopathological factors was constructed. RESULTS: Eleven cell clusters labeled as various cell types by immune cell markers were obtained from the combined scRNA-seq GSE149614 dataset. ssGSEA revealed that autophagy-related pathways were more enriched in malignant tumors. Two autophagy-related clusters (C1 and C2) were identified, in which C1 predicted a better survival, enhanced immune infiltration, and a higher immunotherapy response. LASSO–Cox regression established an eight-gene signature. Next, the HCCDB18, GSA14520, and GSE76427 datasets confirmed a strong risk prediction ability of the signature. Moreover, the low-risk group had enhanced immune infiltration and higher immunotherapy response. A nomogram which consisted of RiskScore and clinical features had better prediction ability. CONCLUSION: To precisely assess the prognostic risk, an eight-gene prognostic stratification signature was developed based on the heterogeneity of HCC immune cells.
format Online
Article
Text
id pubmed-9606610
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-96066102022-10-28 Analysis on heterogeneity of hepatocellular carcinoma immune cells and a molecular risk model by integration of scRNA-seq and bulk RNA-seq Liu, Xiaorui Li, Jingjing Wang, Qingxiang Bai, Lu Xing, Jiyuan Hu, Xiaobo Li, Shuang Li, Qinggang Front Immunol Immunology BACKGROUND: Studies have shown that hepatocellular carcinoma (HCC) heterogeneity is a main cause leading to failure of treatment. Technology of single-cell sequencing (scRNA) could more accurately reveal the essential characteristics of tumor genetics. METHODS: From the Gene Expression Omnibus (GEO) database, HCC scRNA-seq data were extracted. The FindCluster function was applied to analyze cell clusters. Autophagy-related genes were acquired from the MSigDB database. The ConsensusClusterPlus package was used to identify molecular subtypes. A prognostic risk model was built with the Least Absolute Shrinkage and Selection Operator (LASSO)–Cox algorithm. A nomogram including a prognostic risk model and multiple clinicopathological factors was constructed. RESULTS: Eleven cell clusters labeled as various cell types by immune cell markers were obtained from the combined scRNA-seq GSE149614 dataset. ssGSEA revealed that autophagy-related pathways were more enriched in malignant tumors. Two autophagy-related clusters (C1 and C2) were identified, in which C1 predicted a better survival, enhanced immune infiltration, and a higher immunotherapy response. LASSO–Cox regression established an eight-gene signature. Next, the HCCDB18, GSA14520, and GSE76427 datasets confirmed a strong risk prediction ability of the signature. Moreover, the low-risk group had enhanced immune infiltration and higher immunotherapy response. A nomogram which consisted of RiskScore and clinical features had better prediction ability. CONCLUSION: To precisely assess the prognostic risk, an eight-gene prognostic stratification signature was developed based on the heterogeneity of HCC immune cells. Frontiers Media S.A. 2022-10-13 /pmc/articles/PMC9606610/ /pubmed/36311759 http://dx.doi.org/10.3389/fimmu.2022.1012303 Text en Copyright © 2022 Liu, Li, Wang, Bai, Xing, Hu, Li and Li https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Immunology
Liu, Xiaorui
Li, Jingjing
Wang, Qingxiang
Bai, Lu
Xing, Jiyuan
Hu, Xiaobo
Li, Shuang
Li, Qinggang
Analysis on heterogeneity of hepatocellular carcinoma immune cells and a molecular risk model by integration of scRNA-seq and bulk RNA-seq
title Analysis on heterogeneity of hepatocellular carcinoma immune cells and a molecular risk model by integration of scRNA-seq and bulk RNA-seq
title_full Analysis on heterogeneity of hepatocellular carcinoma immune cells and a molecular risk model by integration of scRNA-seq and bulk RNA-seq
title_fullStr Analysis on heterogeneity of hepatocellular carcinoma immune cells and a molecular risk model by integration of scRNA-seq and bulk RNA-seq
title_full_unstemmed Analysis on heterogeneity of hepatocellular carcinoma immune cells and a molecular risk model by integration of scRNA-seq and bulk RNA-seq
title_short Analysis on heterogeneity of hepatocellular carcinoma immune cells and a molecular risk model by integration of scRNA-seq and bulk RNA-seq
title_sort analysis on heterogeneity of hepatocellular carcinoma immune cells and a molecular risk model by integration of scrna-seq and bulk rna-seq
topic Immunology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9606610/
https://www.ncbi.nlm.nih.gov/pubmed/36311759
http://dx.doi.org/10.3389/fimmu.2022.1012303
work_keys_str_mv AT liuxiaorui analysisonheterogeneityofhepatocellularcarcinomaimmunecellsandamolecularriskmodelbyintegrationofscrnaseqandbulkrnaseq
AT lijingjing analysisonheterogeneityofhepatocellularcarcinomaimmunecellsandamolecularriskmodelbyintegrationofscrnaseqandbulkrnaseq
AT wangqingxiang analysisonheterogeneityofhepatocellularcarcinomaimmunecellsandamolecularriskmodelbyintegrationofscrnaseqandbulkrnaseq
AT bailu analysisonheterogeneityofhepatocellularcarcinomaimmunecellsandamolecularriskmodelbyintegrationofscrnaseqandbulkrnaseq
AT xingjiyuan analysisonheterogeneityofhepatocellularcarcinomaimmunecellsandamolecularriskmodelbyintegrationofscrnaseqandbulkrnaseq
AT huxiaobo analysisonheterogeneityofhepatocellularcarcinomaimmunecellsandamolecularriskmodelbyintegrationofscrnaseqandbulkrnaseq
AT lishuang analysisonheterogeneityofhepatocellularcarcinomaimmunecellsandamolecularriskmodelbyintegrationofscrnaseqandbulkrnaseq
AT liqinggang analysisonheterogeneityofhepatocellularcarcinomaimmunecellsandamolecularriskmodelbyintegrationofscrnaseqandbulkrnaseq