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

Author:

Hu Jian1ORCID,Mao Feifei2ORCID,Li Lifang3ORCID,Wang Xiaoqian1ORCID,Cai Depei4ORCID,He Longmei5ORCID,Wu Qian1ORCID,Wang Cong1ORCID,Zhang Ning1ORCID,Ma Yanfen1ORCID,Wu Xia6ORCID,Qu Kai7ORCID,Wang Xiaoqin1ORCID

Affiliation:

1. Department of Clinical Laboratory, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, 710061 Shaanxi Province, China

2. Tongji University Cancer Center, Shanghai Tenth People’s Hospital, School of Medicine, Tongji University, Shanghai 200072, China

3. Emergency Department, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, 710061 Shaanxi Province, China

4. Department of Clinical Laboratory, Xi’an Aerospace General Hospital, Xi’an, 710061 Shaanxi Province, China

5. Department of Clinical Laboratory, Shaanxi Provincial Hospital of Chinese Medicine, Xi’an, 710082 Shaanxi Province, China

6. Department of Clinical Laboratory, Xi’an Chest Hospital, Xi’an, 710061 Shaanxi Province, China

7. Department of Hepatobiliary Surgery, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, 710061 Shaanxi Province, China

Abstract

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.

Funder

National Natural Science Foundation of China

Publisher

Hindawi Limited

Subject

Oncology

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