LAUSR

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.