A genomic-clinicopathologic nomogram for predicting overall survival of hepatocellular carcinoma
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RESEARCH ARTICLE
Open Access
A genomic-clinicopathologic nomogram for predicting overall survival of hepatocellular carcinoma Kena Zhou1†, Qiang Zhou2† and Congbo Cai2*
Abstract Background: Hepatocellular carcinoma (HCC) is a common digestive tumor with great heterogeneity and different overall survival (OS) time, causing stern problems for selecting optimal treatment. Here we aim to establish a nomogram to predict the OS in HCC patients. Methods: International Cancer Genome Consortium (ICGC) database was searched for the target information in our study. Lasso regression, univariate and multivariate cox analysis were applied during the analysis process. And a nomogram integrating model scoring and clinical characteristic was drawn. Results: Six mRNAs were screened out by Lasso regression to make a model for predicting the OS of HCC patients. And this model was proved to be an independent prognostic model predicting OS in HCC patients. The area under the ROC curve (AUC) of this model was 0.803. TCGA database validated the significant value of this 6-mRNA model. Eventually a nomogram including 6-mRNA risk score, gender, age, tumor stage and prior malignancy was set up to predict the OS in HCC patients. Conclusions: We established an independent prognostic model of predicting OS for 1–3 years in HCC patients, which is available to all populations. And we developed a nomogram on the basis of this model, which could be of great help to precisely individual treatment measures. Keywords: Hepatocellular carcinoma, Overall survival, ICGC, TCGA, Nomogram
Background Liver cancer is a deadly disease ranking the fourth leading cause of cancer related death. Hepatocellular carcinoma (HCC) accounts for 75–85% of liver cancer and is the main type for dead cases in liver cancer [1]. In the past decades, the evaluation of prognosis in HCC patients was usually based on the pathological diagnosis and clinical risk factors, such as AFP, DM, hypertension, hyperlipidemia, drinking, obesity and smoking [2–5].
* Correspondence: [email protected] † Kena Zhou and Qiang Zhou contributed equally to this work. 2 Emergency Department of Yinzhou No.2 Hospital, Ningbo 315000, Zhejiang, China Full list of author information is available at the end of the article
However, the exact prediction ability of the above risk factors has never been described in previous articles. With the widespread application of new generation RNA sequencing technology, more novel genes have been discovered playing important roles in tumors [6, 7]. For example, BIRC3 induces growth and metastasis both in vitro and vivo in HCC [8]; CTHRC1 is related to invasion and metastasis in liver cancer [9]; the expression level of OCIAD2 is related to growth and invasion in HCC [10]. Many literatures indicated that considering the low expression level of single gene, a combination of multiple genes can better predict the OS of HCC patients [11, 12]. However, such models cannot accurately predict the OS of HCC patients, which has an impact on
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