Background: Immune microenvironment implicated in liver cancer development. Nevertheless, previous studies have not fully investigated the immune microenvironment in liver cancer.
Methods: The open-access data used for analysis were obtained from The Cancer Genome Atlas (TCGA-LIHC) and the International Cancer Genome Consortium databases (ICGC-JP and ICGC-FR). R program was employed to analyze all the data statistically.
Results: First, the TCGA-LIHC, ICGC-FR, and ICGC-JP cohorts were selected for our analysis, which were merged into a combined cohort. Then, we quantified 53 immune terms in this combined cohort with large populations using the ssGSEA algorithm. Next, a prognostic approach was established based on five immune principles (CORE.SERUM.RESPONSE.UP, angiogenesis, CD8.T.cells, Th2.cells, and B.cells) was established, which showed great prognostic prediction efficiency. Clinical correlation analysis demonstrated that high-risk patients could reveal higher progressive clinical features. Next, to examine the inherent biological variations in high- and low-risk patients, pathway enrichment tests were conducted. DNA repair, E2F targets, G2M checkpoints, HEDGEHOG signaling, mTORC1 signaling, and MYC target were positively correlated with the risk score. Examination of genomic instability revealed that high-risk patients may exhibit a higher tumor mutation burden score. Meanwhile, the risk score showed a strong positive correlation with the tumor stemness index. In addition, the Tumor Immune Dysfunction and Exclusion outcome indicated that high-risk patients could be higher responsive to immunotherapy, whereas low-risk patients may be higher responsive to Erlotinib. Finally, six characteristic genes DEPDC1, DEPDC1B, NGFR, CALCRL, PRR11, and TRIP13 were identified for risk group prediction.
Conclusions: In summary, our study identified a signature as a useful tool to indicate prognosis and therapy options for liver cancer patients.
Copyright © 2023 Xiaoyun Bin et al.