Identification of Prognostic Glycolysis-Related lncRNA Signature in Tumor Immune Microenvironment of Hepatocellular Carcinoma

Purpose: Constructing a novel risk scoring model with prognostic value that could elucidate tumor immune microenvironment of hepatocellular carcinoma (HCC). Samples and methods: Data was obtained through The Cancer Genome Atlas(TCGA) database, and univariate Cox analysis, least absolute shrinkage and selection operator(LASSO) analysis and multivariate Cox analysis were carried out to screen for glycolysis-related long non-coding 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 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 were 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 correlated 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 was used to draw a nomogram. Besides, 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 MIR4435-2HG had intimate relationship with ICB therapy in HCC. 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.