Research Paper Volume 12, Issue 13 pp 12896—12920

A novel molecular-clinicopathologic nomogram to improve prognosis prediction of hepatocellular carcinoma

Zhongjing Zhang1, *, , Wanqing Weng1, *, , Weiguo Huang1, , Boda Wu1, , Yi Zhou1, , Jie Zhang1, , Tuo Deng1, , Wen Ye1, , Jiecheng Zhang1, , Jianyang Ao1, , Qiyu Zhang1, , Keqing Shi2, ,

  • 1 Department of Hepatopancreatobiliary Surgery, The First Affiliated Hospital, Wenzhou Medical University, Wenzhou 325015, Zhejiang Province, PR China
  • 2 Precision Medical Center Laboratory, The First Affiliated Hospital, Wenzhou Medical University, Wenzhou 325015, Zhejiang Province, PR China
* Co-first author

Received: October 4, 2019       Accepted: May 20, 2020       Published: June 30, 2020
How to Cite

Copyright © 2020 Zhang 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.


Background: Emerging evidence suggests that long non-coding RNA (lncRNA) plays a crucial part in the development and progress of hepatocellular carcinoma (HCC). The objective was to develop novel molecular-clinicopathological prediction methods for overall survival (OS) and recurrence of HCC.

Results: An 8-lncRNA-based classifier for OS and a 14-lncRNA-based classifier for recurrence were developed by LASSO COX regression analysis, both of which had high accuracy. The tdROC of OS-nomogram and recurrence-nomogram indicates the satisfactory accuracy and predictive power. The classifiers and nomograms for predicting OS and recurrence of HCC were validated in the Test and GEO cohorts.

Conclusions: These two lncRNA-based classifiers could be independent prognostic factors for OS and recurrence. The molecule-clinicopathological nomograms based on the classifiers could increase the prognostic value.

Methods: HCC lncRNA expression profiles from the cancer genome atlas (TCGA) were randomly divided into 1:1 training and test cohorts. Based on least absolute shrinkage and selection operator method (LASSO) COX regression model, lncRNA-based classifiers were established to predict OS and recurrence, respectively. OS-nomogram and recurrence-nomogram were developed by combining lncRNA-based classifiers and clinicopathological characterization to predict OS and recurrence, respectively. The prognostic value was accessed by the time-dependent receiver operating characteristic (tdROC) and the concordance index (C-index).


AUC: area under receiver operating characteristics; CI: confidence interval; C-index: concordance index; HCC: hepatocellular carcinoma; HR: hazard ratio; lncRNA: long non-coding RNA; LASSO: least absolute shrinkage and selection operator method; OS: overall survival; ROC: receiver operating characteristic; TCGA: The Cancer Genome Atlas; tdAUC: time-dependent area under receiver operating characteristic; td-ROC: time-dependent receiver operating characteristic.