Table_2_Identification of an Epithelial-Mesenchymal Transition-Related Long Non-coding RNA Prognostic Signature to Determine the Prognosis and Drug Treatment of Hepatocellular Carcinoma Patients.DOCX

IntroductionHepatocellular carcinoma (HCC) is one of the most common malignant tumors with poor prognosis. Epithelial–mesenchymal transition (EMT) is crucial for cancer progression and metastasis. Thus, we aimed to construct an EMT-related lncRNA signature for predicting the prognosis of HCC patients.MethodsCox regression analysis and LASSO regression method were used to build an EMT-related lncRNAs risk signature based on TCGA database. Kaplan-Meier survival analysis was conducted to compare the overall survival (OS) in different risk groups. ROC curves and Cox proportional-hazards analysis were performed to evaluate the performance of the risk signature. RT-qPCR was conducted in HCC cell lines and tissue samples to detect the expression of some lncRNAs in this risk model. Furthermore, a nomogram involving the risk score and clinicopathological features was built and validated with calibration curves and ROC curves. In addition, we explored the association between risk signature and tumor immunity, somatic mutations status, and drugs sensitivity.ResultsTwelve EMT-related lncRNAs were obtained to construct the prognostic risk signature for patients with HCC. The Kaplan-Meier curve analysis revealed that patients in the high-risk group had worse overall survival (OS) than those in low-risk group. ROC curves and Cox regression analysis suggested the risk signature could predict HCC survival exactly and independently. The prognostic value of the risk model was confirmed in the testing and entire groups. We also found AC099850.3 and AC092171.2 were highly expressed in HCC cells and HCC tissues. The nomogram could accurately predict survival probability of HCC patients. Gene set enrichment analysis (GSEA) and gene ontology (GO) analysis showed that cancer-related pathways and cell division activity were enriched in high-risk group. The SNPs showed that the prevalence of TP53 mutations was significantly different between high- and low-risk groups; the TP53 mutations and the high TMB were both associated with a worse prognosis in patients with HCC. We also observed widely associations between risk signature and drugs sensitivity in HCC.ConclusionA novel EMT-related lncRNAs risk signature, including 12 lncRNAs, was established and identified in patients with HCC, which can accurately predict the prognosis of HCC patients and may be used to guide individualized treatment in the clinical practice.

本研究旨在构建一个与上皮间质转化（EMT）相关的长链非编码RNA（lncRNA）特征，以预测肝细胞癌（HCC）患者的预后。研究方法包括基于TCGA数据库的Cox回归分析和LASSO回归方法构建EMT相关lncRNAs风险特征，通过Kaplan-Meier生存分析比较不同风险组的总生存期（OS）。通过ROC曲线和Cox比例风险分析评估风险特征的性能。在HCC细胞系和组织样本中通过实时定量PCR（RT-qPCR）检测该风险模型中某些lncRNA的表达。此外，构建并验证了包含风险评分和临床病理特征的列线图，并通过校准曲线和ROC曲线进行验证。此外，我们还探讨了风险特征与肿瘤免疫、体细胞突变状态和药物敏感性的关联。结果显示，我们获得了12个与EMT相关的lncRNA，构建了HCC患者的预后风险特征。Kaplan-Meier曲线分析显示，高风险组患者的总生存期（OS）较低风险组差。ROC曲线和Cox回归分析表明，风险特征可以精确且独立地预测HCC的生存率。风险模型在测试组和整个组中的预后价值得到证实。我们还发现AC099850.3和AC092171.2在HCC细胞和HCC组织中高表达。列线图可以准确预测HCC患者的生存概率。基因集富集分析（GSEA）和基因本体（GO）分析显示，高风险组中富集了癌症相关途径和细胞分裂活性。SNPs显示，TP53突变在高风险组和低风险组之间的发生率存在显著差异；TP53突变和高TMB均与HCC患者的预后不良相关。我们还观察到风险特征与HCC药物敏感性之间存在广泛的关联。结论：本研究建立并鉴定了一个包含12个lncRNA的新型EMT相关lncRNA风险特征，该特征可以准确预测HCC患者的预后，并可能用于临床实践中的个体化治疗指导。