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Research Paper|Volume 13, Issue 14|pp 18789—18805

Identification of prognostic long non-coding RNA signature with potential drugs in hepatocellular carcinoma

Fengjie Hao1,2,3, Nan Wang1, Xiang Wang4, Yongjun Chen1, Junqing Wang1
  • 1Department of General Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, P.R. China
  • 2Department of Immunology, Ophthalmology and ORL, Complutense University School of Medicine, Madrid, Spain
  • 312 de Octubre Health Research Institute (imas12), Madrid, Spain
  • 4Department of Quantitative and Computational Biology, Baylor College of Medicine, Houston, TX 77030, USA
* Equal contribution
Received: June 1, 2021Accepted: July 5, 2021Published: July 20, 2021
https://doi.org/10.18632/aging.203322

Copyright: © 2021 Hao et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

Hepatocellular carcinoma (HCC) is the primary malignancy in the liver with high rate of death and recurrence. Novel prognostic model would be crucial for early diagnosis and improved clinical decision. The study aims to provide an effective lncRNA-based signature to predict survival time and tumor recurrence for HCC. Based on public database, lncRNA-based classifiers for overall survival and tumor recurrence were built with regression analysis and cross validation strategy. According to the risk-score of the classifiers, the whole cohorts were divided into groups with high and low risk. Afterwards, the efficiency of the lncRNA-based classifiers was evaluated and compared with other clinical factors. Finally, candidate small molecules for high risk groups were further screened using drug response databases to explore potential drugs for HCC treatment.