Abstract
Purpose: The purpose of this study was to construct a novel risk scoring model with prognostic value that could elucidate tumor immune microenvironment of hepatocellular carcinoma (HCC). Samples and methods: Data were obtained through The Cancer Genome Atlas (TCGA) database. Univariate Cox analysis, least absolute shrinkage and selection operator (LASSO) analysis, and multivariate Cox analysis were carried out to screen for glycolysis-related long noncoding RNAs (lncRNAs) that could provide prognostic value. Finally, we established a risk score model to describe the characteristics of the model and verify its prediction accuracy. The receiver operating characteristic (ROC) curves of 1, 3, and 5 years of overall survival (OS) were depicted with risk score and some clinical features. ESTIMATE algorithm, single-sample gene set enrichment analysis (ssGSEA), and CIBERSORT analysis were employed to reveal the characteristics of tumor immune microenvironment in HCC. The nomogram was drawn by screening indicators with high prognostic accuracy. The correlation of risk signature with immune infiltration and immune checkpoint blockade (ICB) therapy was analyzed. After enrichment of related genes, active behaviors and pathways in high-risk groups were identified and lncRNAs related to poor prognosis were validated in vitro. Finally, the impact of MIR4435-2HG upon ICB treatment was uncovered. Results: After screening through multiple steps, four glycolysis-related lncRNAs were obtained. The risk score constructed with the four lncRNAs was found to significantly correlate with prognosis of samples. From the ROC curve of samples with 1, 3, and 5 years of OS, two indicators were identified with high prognostic accuracy and were used to draw a nomogram. Besides, the risk score significantly correlated with immune score, immune-related signature, infiltrating immune cells (i.e. B cells, etc.), and ICB key molecules (i.e. CTLA4,etc.). Gene enrichment analysis indicated that multiple biological behaviors and pathways were active in the high-risk group. In vitro validation results showed that MIR4435-2HG was highly expressed in the two cell lines, which had a significant impact on the OS of samples. Finally, we corroborated that MIR4435-2HG had intimate relationship with ICB therapy in hepatocellular carcinoma. Conclusion: We elucidated the crucial role of risk signature in immune cell infiltration and immunotherapy, which might contribute to clinical strategies and clinical outcome prediction of HCC.
Highlights
Liver cancer is one of the most common malignant tumors with a high rate of metastasis and high mortality (Siegel et al, 2020)
Multiple Long noncoding RNAs (lncRNAs) Are Associated With Glycolysis-Related Genes
To further uncover the potential role of prognostic risk score in tumor immune microenvironment (TIME) of hepatocellular carcinoma (HCC), we investigated the relationship between risk score and immune-related score, immune signature and Tumorinitiating cell subtypes and level, and the 46 immune checkpoint blockade–related genes expression level
Summary
Liver cancer is one of the most common malignant tumors with a high rate of metastasis and high mortality (Siegel et al, 2020). The administrations of immune checkpoint blockade inhibitors have revolutionized antitumor treatment in wide range of cancers. About 20% of samples were observed for objective response, indicating immune checkpoint inhibitors may contribute novel insight into clinical intervention and decision-making of HCC (Cheng et al, 2019). Tumor mutation burden (TMB), which represents the somatic coding errors such as base substitutions, insertions, or deletions across per million bases, has been termed as a promising indicator for predicting responsiveness to ICB based on numerous researches (Snyder et al, 2014; Rizvi et al, 2015; Chan et al, 2019). Exploring new ways to judge prognosis and clinical outcome is helpful to the survival evaluation and disease treatment of samples
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