Table4_Construction of a Metabolism-Related Long Non-Coding RNAs-Based Risk Score Model of Hepatocellular Carcinoma for Prognosis and Personalized Treatment Prediction.XLSX

Background: Long non-coding RNAs (lncRNAs) play a key regulatory role in tumor metabolism. Although hepatocellular carcinoma (HCC) is a metabolic disease, there have been few systematic reports on the association between lncRNA expression and metabolism in HCC.Results: In this study, we screened 557 metabolism-related lncRNAs in HCC. A risk score model based on 13 metabolism-related lncRNA pairs was constructed to predict the outcome and drug response in HCC. The risk score model presented a better prediction of the outcomes than that with common clinicopathological characteristics, such as tumor stage, grade, and status and aneuploidy score in both training and testing cohorts. In addition, patients in the high-risk group exhibited higher responses to gemcitabine and epothilone, whereas those in the low-risk group were more sensitive to metformin and nilotinib.Conclusion: The metabolism-related lncRNAs-based risk score model and the other findings of this study may be helpful for HCC prognosis and personalized treatment prediction.

背景：长非编码RNA（lncRNA）在肿瘤代谢中发挥着关键的调控作用。尽管肝细胞癌（HCC）是一种代谢性疾病，但关于lncRNA表达与HCC代谢之间关联的系统报道却为数不多。结果：在本研究中，我们筛选出557个与HCC代谢相关的lncRNA。基于13对代谢相关lncRNA构建了风险评分模型，以预测HCC的预后和药物反应。该风险评分模型在训练组和测试组中均比基于肿瘤分期、分级、状态及非整倍体评分等常见临床病理特征的预测模型具有更好的预测效果。此外，高风险组的患者对吉西他滨和依泊替康的响应更高，而低风险组的患者对二甲双胍和尼洛替尼更为敏感。结论：基于代谢相关lncRNA的风险评分模型及本研究的其他发现，可能有助于HCC的预后评估和个性化治疗方案预测。