Risk Signature Related to Immunotherapy Reaction of Hepatocellular Carcinoma Based on the Immune-Related Genes Associated With CD8+ T Cell Infiltration

Comprehensive analysis of anoikis-related lncRNAs for predicting prognosis and response of immunotherapy in hepatocellular carcinoma

Nowadays, primary liver cancer is still a major threat to human health. Anoikis is a particular form of programed cell death that has an inhibitory effect on neoplasm metastasis. Although several prognostic models based on anoikis-related genes for Hepatocellular carcinoma (HCC) have been established, signatures associated with anoikis-related lncRNAs have not been identified. To fill this blank space, the authors built up a prognostic signature and appraised its value in guiding immunotherapy. Eleven prognostic anoikis-related lncRNAs were identified through Least Absolute Shrinkage and Selection Operator Cox analysis. The accuracy of the risk signature in predicting prognosis was verified by K-M survival analysis and Receiver operating characteristic analysis. We further discovered that the high-risk group was often enriched in signal pathways related to cell growth and death and immune response; in addition, in the low-risk group, cells often undergo metabolic changes through gene set enrichment analysis. Finally, we realised that HCC patients in the high-risk group were upregulated in immune-checkpoint molecules and tend to have a higher tumour mutation burden level which indicated a higher sensitivity to immunotherapy. All in all, the anoikis-related lncRNAs risk signature showed excellent ability in predicting prognosis and may guide the application of immunotherapy in future clinical practice.

STING pathway contributes to the prognosis of hepatocellular carcinoma and identification of prognostic gene signatures correlated to tumor microenvironment

Background

Hepatocellular carcinoma (HCC) is one of the most malignant solid tumors worldwide. Recent evidence shows that the stimulator of interferon genes (STING) pathway is essential for anti-tumor immunity via inducing the production of downstream inflammatory cytokines. However, its impact on the prognosis and tumor microenvironment of HCC was still limited known.

Methods

We obtained gene expression profiles of HCC from GEO, TCGA, and ICGC databases, and immune-related genes (IRGs) from the ImmPort database. Multivariate Cox regression was performed to identify independent prognostic factors. Nomogram was established to predict survival probability for individual patients. Kaplan–Meier curve was used to evaluate the survival difference. Afterward, ESTIMATE, TISCH, and TIMER databases were combined to assess the immune cell infiltration. Furthermore, the qPCR, western blotting, and immunohistochemistry were done to evaluate gene expression, and in vitro cell models were built to determine cell migratory ability.

Results

We found that gene markers of NLRC3, STING1, TBK1, TRIM21, and XRCC6 within STING pathway were independent prognostic factors in HCC patients. Underlying the finding, a predictive nomogram was constructed in TCGA-training cohort and further validated in TCGA-all and ICGC datasets, showing credible performance. Experimentally, up-regulated TBK1 promotes the ability of HCC cell migration. Next, the survival-related immune-related co-expressed gene signatures (IRCGS) (VAV1, RHOA, and ZC3HAV1) were determined in HCC cohorts and their expression was verified in human HCC cells and clinical samples. Furthermore, survival-related IRCGS was associated with the infiltration of various immune cell subtypes in HCC, the transcriptional expression of prominent immune checkpoints, and immunotherapeutic response.

Conclusion

Collectively, we constructed a novel prognostic nomogram model for predicting the survival probability of individual HCC patients. Moreover, an immune-related prognostic gene signature was determined. Both might function as potential therapeutic targets for HCC treatment in the future.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-022-02734-4.

Network analysis of hepatocellular carcinoma liquid biopsies augmented by single-cell sequencing data

Liquid biopsy, the analysis of body fluids, represents a promising approach for disease diagnosis and prognosis with minimal intervention. Sequencing cell-free RNA derived from liquid biopsies has been very promising for the diagnosis of several diseases. Cancer research, in particular, has emerged as a prominent candidate since early diagnosis has been shown to be a critical determinant of disease prognosis. Although high-throughput analysis of liquid biopsies has uncovered many differentially expressed genes in the context of cancer, the functional connection between these genes is not investigated in depth. An important approach to remedy this issue is the construction of gene networks which describes the correlation patterns between different genes, thereby allowing to infer their functional organization. In this study, we aimed at characterizing extracellular transcriptome gene networks of hepatocellular carcinoma patients compared to healthy controls. Our analysis revealed a number of genes previously associated with hepatocellular carcinoma and uncovered their association network in the blood. Our study thus demonstrates the feasibility of performing gene co-expression network analysis from cell-free RNA data and its utility in studying hepatocellular carcinoma. Furthermore, we augmented cell-free RNA network analysis with single-cell RNA sequencing data which enables the contextualization of the identified network modules with cell-type specific transcriptomes from the liver.