AUTHOR=Zhang Peichen , Chen Liping , Wu Shengjie , Ye Bailiang , Chen Chao , Shi Lingyan 

TITLE=Construction of a Metabolism-Related Long Non-Coding RNAs-Based Risk Score Model of Hepatocellular Carcinoma for Prognosis and Personalized Treatment Prediction

JOURNAL=Pathology and Oncology Research

VOLUME=28

YEAR=2022

URL=https://www.por-journal.com/journals/pathology-and-oncology-research/articles/10.3389/pore.2022.1610066

DOI=10.3389/pore.2022.1610066

ISSN=1532-2807

ABSTRACT=<p><bold>Background:</bold> 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.</p><p><bold>Results:</bold> 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.</p><p><bold>Conclusion:</bold> 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.</p>