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Analysis of the Heterogeneity of the Tumor Microenvironment and the Prognosis and Immunotherapy Response of Different Immune Subtypes in Hepatocellular Carcinoma

PURPOSE: The current clinical classification of hepatocellular carcinoma (HCC) cannot well predict the patient's possible response to the treatment plan, nor can it predict the patient's prognosis. We use the gene expression patterns of patients with hepatocellular carcinoma to reveal the...

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Autores principales: Hu, Jian, Mao, Feifei, Li, Lifang, Wang, Xiaoqian, Cai, Depei, He, Longmei, Wu, Qian, Wang, Cong, Zhang, Ning, Ma, Yanfen, Wu, Xia, Qu, Kai, Wang, Xiaoqin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8984740/
https://www.ncbi.nlm.nih.gov/pubmed/35401750
http://dx.doi.org/10.1155/2022/1087399
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author Hu, Jian
Mao, Feifei
Li, Lifang
Wang, Xiaoqian
Cai, Depei
He, Longmei
Wu, Qian
Wang, Cong
Zhang, Ning
Ma, Yanfen
Wu, Xia
Qu, Kai
Wang, Xiaoqin
author_facet Hu, Jian
Mao, Feifei
Li, Lifang
Wang, Xiaoqian
Cai, Depei
He, Longmei
Wu, Qian
Wang, Cong
Zhang, Ning
Ma, Yanfen
Wu, Xia
Qu, Kai
Wang, Xiaoqin
author_sort Hu, Jian
collection PubMed
description PURPOSE: The current clinical classification of hepatocellular carcinoma (HCC) cannot well predict the patient's possible response to the treatment plan, nor can it predict the patient's prognosis. We use the gene expression patterns of patients with hepatocellular carcinoma to reveal the heterogeneity of hepatocellular carcinoma and analyze the differences in prognosis and immunotherapy response of different immune subtypes. METHODS: Firstly, using the hepatocellular carcinoma expression profile data of TCGA, combined with the single sample gene set enrichment analysis (ssGSEA) algorithm, the immune enrichment of the patient's tumor microenvironment was analyzed. Subsequently, the spectral clustering algorithm was used to extract different classifications, and the cohort of hepatocellular carcinoma was divided into 3 subtypes, and the correlation between immune subtypes and clinical characteristics and survival prognosis was established. The patient's risk index is obtained through the prognostic prediction model, suggesting the correlation between the risk index and various types of immune cells. RESULTS: We can divide the liver cancer cohort into three subtypes: stromal cell activated immune-enriched type (A-IS), general immune-enriched type (N-IS), and non-immune-enriched type (non-IS). The 3-year survival rate of TCGA's A-IS is higher than that of N-IS and non-IS, and the three components are significantly different (p = 0.017). The 3-year survival rates of ICGC's A-IS and N-IS groups were higher than those of the non-IS group. The analysis of the correlation between the risk index and immune cells showed that the patient's disease risk was significantly positively correlated with cancer-associated fibroblast (CAF) stimulated cell, activated stroma cell, and anti-PD-1 resistant cell. CONCLUSION: The tumor gene expression characteristics of patients with hepatocellular carcinoma can be used as a basis for clinical patient classification. Different immune subtypes are closely related to survival prognosis. Different immune cell states of patients may lead to different disease risk levels. All these provide important references for the clinical identification and prognosis prediction of hepatocellular carcinoma.
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spelling pubmed-89847402022-04-07 Analysis of the Heterogeneity of the Tumor Microenvironment and the Prognosis and Immunotherapy Response of Different Immune Subtypes in Hepatocellular Carcinoma Hu, Jian Mao, Feifei Li, Lifang Wang, Xiaoqian Cai, Depei He, Longmei Wu, Qian Wang, Cong Zhang, Ning Ma, Yanfen Wu, Xia Qu, Kai Wang, Xiaoqin J Oncol Research Article PURPOSE: The current clinical classification of hepatocellular carcinoma (HCC) cannot well predict the patient's possible response to the treatment plan, nor can it predict the patient's prognosis. We use the gene expression patterns of patients with hepatocellular carcinoma to reveal the heterogeneity of hepatocellular carcinoma and analyze the differences in prognosis and immunotherapy response of different immune subtypes. METHODS: Firstly, using the hepatocellular carcinoma expression profile data of TCGA, combined with the single sample gene set enrichment analysis (ssGSEA) algorithm, the immune enrichment of the patient's tumor microenvironment was analyzed. Subsequently, the spectral clustering algorithm was used to extract different classifications, and the cohort of hepatocellular carcinoma was divided into 3 subtypes, and the correlation between immune subtypes and clinical characteristics and survival prognosis was established. The patient's risk index is obtained through the prognostic prediction model, suggesting the correlation between the risk index and various types of immune cells. RESULTS: We can divide the liver cancer cohort into three subtypes: stromal cell activated immune-enriched type (A-IS), general immune-enriched type (N-IS), and non-immune-enriched type (non-IS). The 3-year survival rate of TCGA's A-IS is higher than that of N-IS and non-IS, and the three components are significantly different (p = 0.017). The 3-year survival rates of ICGC's A-IS and N-IS groups were higher than those of the non-IS group. The analysis of the correlation between the risk index and immune cells showed that the patient's disease risk was significantly positively correlated with cancer-associated fibroblast (CAF) stimulated cell, activated stroma cell, and anti-PD-1 resistant cell. CONCLUSION: The tumor gene expression characteristics of patients with hepatocellular carcinoma can be used as a basis for clinical patient classification. Different immune subtypes are closely related to survival prognosis. Different immune cell states of patients may lead to different disease risk levels. All these provide important references for the clinical identification and prognosis prediction of hepatocellular carcinoma. Hindawi 2022-03-29 /pmc/articles/PMC8984740/ /pubmed/35401750 http://dx.doi.org/10.1155/2022/1087399 Text en Copyright © 2022 Jian Hu et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Hu, Jian
Mao, Feifei
Li, Lifang
Wang, Xiaoqian
Cai, Depei
He, Longmei
Wu, Qian
Wang, Cong
Zhang, Ning
Ma, Yanfen
Wu, Xia
Qu, Kai
Wang, Xiaoqin
Analysis of the Heterogeneity of the Tumor Microenvironment and the Prognosis and Immunotherapy Response of Different Immune Subtypes in Hepatocellular Carcinoma
title Analysis of the Heterogeneity of the Tumor Microenvironment and the Prognosis and Immunotherapy Response of Different Immune Subtypes in Hepatocellular Carcinoma
title_full Analysis of the Heterogeneity of the Tumor Microenvironment and the Prognosis and Immunotherapy Response of Different Immune Subtypes in Hepatocellular Carcinoma
title_fullStr Analysis of the Heterogeneity of the Tumor Microenvironment and the Prognosis and Immunotherapy Response of Different Immune Subtypes in Hepatocellular Carcinoma
title_full_unstemmed Analysis of the Heterogeneity of the Tumor Microenvironment and the Prognosis and Immunotherapy Response of Different Immune Subtypes in Hepatocellular Carcinoma
title_short Analysis of the Heterogeneity of the Tumor Microenvironment and the Prognosis and Immunotherapy Response of Different Immune Subtypes in Hepatocellular Carcinoma
title_sort analysis of the heterogeneity of the tumor microenvironment and the prognosis and immunotherapy response of different immune subtypes in hepatocellular carcinoma
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8984740/
https://www.ncbi.nlm.nih.gov/pubmed/35401750
http://dx.doi.org/10.1155/2022/1087399
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