Identification of immune-related genes and patient selection for hepatocellular carcinoma immunotherapy

BACKGROUND: Hepatocellular carcinoma (HCC) is a malignant disease with a poor prognosis. Among the treatment strategies for HCC, tumor immunotherapy (TIT) is a promising research hotspot, in which identifying novel immune-related biomarkers and selecting suitable patient population are urgent issues to be solved. METHODS: In this study, an abnormal expression map of HCC cell genes was constructed using public high-throughput data from 7,384 samples (3,941 HCC vs. 3,443 non-HCC tissues). Through single-cell RNA sequencing (scRNA-seq) cell trajectory analysis, the genes defined as potential drivers of HCC cell differentiation and development were selected. By screening for both immune-related genes and those associated with high differentiation potential in HCC cell development, a series of target genes were identified. Coexpression analysis was performed using Multiscale Embedded Gene Co-expression Network Analysis (MEGENA) to find the specific candidate genes involved in similar biological processes. Subsequently, nonnegative matrix factorization (NMF) was conducted to select patients suitable for HCC immunotherapy based on the coexpression network of candidate genes. RESULTS: HSP90AA1, CDK4, HSPA8, HSPH1, and HSPA5 were identified as promising biomarkers for prognosis prediction and immunotherapy of HCC. Through the use of our molecular classification system, which was based on a function module containing 5 candidate genes, patients with specific characteristics were found to be suitable candidates for TIT. CONCLUSIONS: These findings provide new insights into the selection of candidate biomarkers and patient populations for future HCC immunotherapy.