table10_Identification of Prognostic Glycolysis-Related lncRNA Signature in Tumor Immune Microenvironment of Hepatocellular Carcinoma.xlsx

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.

研究目的：本研究旨在构建一种具有预后价值的新型风险评分模型，以阐明肝细胞癌（hepatocellular carcinoma, HCC）的肿瘤免疫微环境特征。
样本与方法：数据来源于癌症基因组图谱（The Cancer Genome Atlas, TCGA）数据库。通过单因素Cox分析、最小绝对收缩和选择算子（least absolute shrinkage and selection operator, LASSO）分析及多因素Cox分析，筛选具有预后价值的糖酵解相关长链非编码RNA（long noncoding RNAs, lncRNAs）。最终构建风险评分模型以描述模型特征并验证其预测效能。以风险评分及部分临床特征绘制1年、3年及5年总生存期（overall survival, OS）的受试者工作特征（receiver operating characteristic, ROC）曲线。采用ESTIMATE算法、单样本基因集富集分析（single-sample gene set enrichment analysis, ssGSEA）及CIBERSORT分析，揭示肝细胞癌的肿瘤免疫微环境特征。通过筛选预后准确性较高的指标构建列线图。分析风险特征与免疫浸润及免疫检查点阻断（immune checkpoint blockade, ICB）治疗的相关性。对相关基因进行富集分析后，明确高风险组的活跃生物学行为及通路，并在体外验证与不良预后相关的lncRNAs。最终揭示MIR4435-2HG对免疫检查点阻断治疗的影响。
研究结果：经多步筛选后，共获得4个糖酵解相关lncRNAs。基于这4个lncRNAs构建的风险评分与样本预后显著相关。通过1年、3年及5年OS的ROC曲线分析，筛选出2个预后准确性较高的指标并用于构建列线图。此外，风险评分与免疫评分、免疫相关特征、浸润性免疫细胞（如B细胞等）及免疫检查点阻断关键分子（如CTLA4等）显著相关。基因富集分析结果显示，高风险组存在多种活跃的生物学行为与通路。体外验证结果表明，MIR4435-2HG在两种细胞系中均呈高表达，且对样本总生存期具有显著影响。最终证实MIR4435-2HG与肝细胞癌的免疫检查点阻断治疗密切相关。
研究结论：本研究阐明了风险特征在免疫细胞浸润及免疫治疗中的关键作用，可为肝细胞癌的临床诊疗策略及临床结局预测提供参考依据。