Table_4_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.

引言：肝细胞癌（HCC）为预后不良的恶性肿瘤之一。上皮间质转化（EMT）对于肿瘤的进展和转移至关重要。因此，本研究旨在构建一个与EMT相关的长链非编码RNA（lncRNA）特征，以预测肝细胞癌患者的预后。方法：采用Cox回归分析和LASSO回归方法，基于TCGA数据库构建了EMT相关的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）。ROC曲线和Cox比例风险分析用于评估风险特征的性能。在肝细胞癌细胞系和组织样本中进行了实时定量PCR（RT-qPCR）检测，以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诺模图，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：获得12个与EMT相关的lncRNA，构建了用于预测肝细胞癌患者预后的风险特征。Kaplan-Meier曲线分析显示，高风险组的患者总体生存期（OS）低于低风险组。ROC曲线和Cox回归分析表明，该风险特征能够精确且独立地预测肝细胞癌的生存率。在测试组和整个组中均证实了风险模型的预后价值。我们还发现AC099850.3和AC092171.2在肝细胞癌细胞和肝细胞癌组织中高表达。诺模图能够准确预测肝细胞癌患者的生存概率。基因集富集分析（GSEA）和基因本体（GO）分析显示，高风险组中富集了与癌症相关的通路和细胞分裂活性。SNPs显示，TP53突变在高风险组和低风险组中的发生率存在显著差异；TP53突变和高的肿瘤突变负荷（TMB）都与肝细胞癌患者的预后不良相关。我们还观察到风险特征与肝细胞癌药物敏感性之间的广泛关联。结论：本研究成功建立并鉴定了一个包含12个lncRNA的新型EMT相关风险特征，能够准确预测肝细胞癌患者的预后，并可指导临床实践中的个体化治疗。