Abstract
Hepatocellular carcinoma (HCC) is an immune-related tumor, that the type and number of tumor-infiltrated immune cells can serve as biomarkers for the clinical application. In this study, we constructed the immune model for diagnostic and prognostic prediction of HCC based on the systematic bioinformatics analyses on the component of immune cells from large samples transcriptome. CIBERSORT analysis found that the component of immune cells between 513 HCC and 473 adjacent normal tissues was different. M0 macrophages and regulatory T cells were mainly enriched in tumor tissues, whereas the CD8(+) T cell and activated CD4(+) memory T cells were the most in normal tissues. Using random forest and LASSO analyses, eleven immune cell types were mined out to construct the immune diagnostic model (IDG), which showed high efficiency in distinguishing cancer from normal tissues both in testing and validation groups. In addition, the immune prognostic model (IPG) consisting of five types of immune cells was constructed using the LASSO-Cox algorithm. It showed that HCC patients of the high-risk group had a significantly shorter survival time than those of low-risk group in testing, validation, and entire cohorts. Besides, Nomogram plots and decision curve analyses revealed that the IPG was positively associated with the HCC clinical classification of the Barcelona Clinic Liver Cancer (BCLC) stage, and showing more accuracy of prediction than independent BCLC stage. Related analyses found that IDG positively correlated with epithelial-mesenchymal transition (EMT) and cytotoxic factor-related genes and negatively correlated with immune checkpoint regulators related genes. From the GSEA analysis of the biological function of genes related to IPG, it was found that the genes of the high-risk group were enriched in some tumorigenesis related pathways, such as DNA replication, cell cycle, and PPARA. Therefore, this study identified IDG and IPG as efficient biomarkers for the diagnosis and prognosis of HCC.