Abstract
BackgroundHepatocellular carcinoma (HCC) is a serious threat to public health due to its poor prognosis. The current study aimed to develop and validate a prognostic nomogram to predict the overall survival of HCC patients.MethodsThe model cohort consisted of 24,991 mRNA expression data points from 348 HCC patients. The least absolute shrinkage and selection operator method (LASSO) Cox regression model was used to evaluate the prognostic mRNA biomarkers for the overall survival of HCC patients.ResultsUsing multivariate Cox proportional regression analyses, a prognostic nomogram (named Eight-mRNA prognostic nomogram) was constructed based on the expression data of N4BP3, -ADRA2B, E2F8, MAPT, PZP, HOXD9, COL15A1, and -NDST3. The C-index of the Eight-mRNA prognostic nomogram was 0.765 (95% CI 0.724–0.806) for the overall survival in the model cohort. The Harrell’s concordance-index of the Eight-mRNA prognostic nomogram was 0.715 (95% CI 0.658–0.772) in the validation cohort. The survival curves demonstrated that the HCC patients in the high risk group had a significantly poorer overall survival than the patients in the low risk group.ConclusionIn the current study, we have developed two convenient and efficient predictive precision medicine tools for hepatocellular carcinoma. These two predictive precision medicine tools are helpful for predicting the individual mortality risk probability and improving the personalized comprehensive treatments for HCC patients. The Smart Cancer Predictive System can be used by clicking the following URL: https://zhangzhiqiao2.shinyapps.io/Smart_cancer_predictive_system_HCC_2/. The Gene Survival Analysis Screen System is available at the following URL: https://zhangzhiqiao5.shinyapps.io/Gene_Survival_Analysis_A1001/.
Highlights
Hepatocellular carcinoma (HCC) is the sixth most common type of cancer and the third leading cause of cancerrelated death, resulting in 841,080 new cases and 781,631 deaths worldwide in 2018 [1]
We identified the prognostic biomarkers for overall survival using the least absolute shrinkage and selection operator method (LASSO) Cox regression model
The Gene Survival Analysis Screen System is available at the following URL: https://zhangzhiqiao5.shinyapps.io/Gene_Survival_Analysis_A1001/
Summary
Hepatocellular carcinoma (HCC) is the sixth most common type of cancer and the third leading cause of cancerrelated death, resulting in 841,080 new cases and 781,631 deaths worldwide in 2018 [1]. Surgical resection remains the main therapy for the majority of HCC patients. With the substantial development of high-throughput sequencing technology, numerous genes have been reported to be related to the prognosis of HCC patients [6,7,8,9]. Li et al developed a three-gene prognostic signature to predict the prognosis of HCC patients [10]. Hepatocellular carcinoma (HCC) is a serious threat to public health due to its poor prognosis. The current study aimed to develop and validate a prognostic nomogram to predict the overall survival of HCC patients
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