A four-gene-based prognostic model predicts overall survival in patients with hepatocellular carcinoma

Abstract

With the development of new advances in hepatocellular carcinoma (HCC) management and noninvasive radiological techniques, high-risk patient groups such as those with hepatitis virus are closely monitored. HCC is increasingly diagnosed early, and treatment may be successful. In spite of this progress, most patients who undergo a hepatectomy will eventually relapse, and the outcomes of HCC patients remain unsatisfactory. In our study, we aimed to identify potential gene biomarkers based on RNA sequencing data to predict and improve HCC patient survival. The gene expression data and clinical information were acquired from The Cancer Genome Atlas (TCGA) database. A total of 339 differentially expressed genes (DEGs) were obtained between the HCC (n = 374) and normal tissues (n = 50). Four genes (CENPA, SPP1, MAGEB6 and HOXD9) were screened by univariate, Lasso and multivariate Cox regression analyses to develop the prognostic model. Further analysis revealed the independent prognostic capacity of the prognostic model in relation to other clinical characteristics. The receiver operating characteristic (ROC) curve analysis confirmed the good performance of the prognostic model. Then, the prognostic model and the expression levels of the four genes were validated using the Gene Expression Omnibus (GEO) dataset. A nomogram comprising the prognostic model to predict the overall survival was established, and internal validation in the TCGA cohort was performed. The predictive model and the nomogram will enable patients with HCC to be more accurately managed in trials testing new drugs and in clinical practice.

Keywords: hepatocellular carcinoma; overall survival; risk score.

Figure 1

Overall workflow describing the process used to develop and validate the prognostic model to predict prognostic outcomes

Figure 2

K‐M and time‐dependent ROC curves for the prognostic model in the TCGA HCC cohort (A) and in the GEO HCC cohort. The K‐M survival curves show the overall survival based on the relative high‐ and low‐risk patients divided by the optimal cut‐off point. Time‐dependent ROC curve analysis of survival prediction by the prognostic model

Figure 3

Univariate and multivariate association of the prognostic model and clinicopathological characteristics with overall survival. Red represents no statistical significance, and blue represents statistical significance

Figure 4

The four prognostic genes are upregulated in human HCC specimens. A, The expression profiles of the four genes in the TCGA liver cancer RNA‐seq (n = 371) dataset. B, GEO data showing the expression profiles of the four prognostic genes in normal liver (n = 78) vs tumour tissue (n = 78); P < 0.01 (*), P < 0.001 (**) and P < 0.0001 (***). C, The expression profiles of the four genes in the normal liver tissue and HCC specimens. Images were taken from the Human Protein Atlas (http://www.proteinatlas.org) online database

Figure 5

Nomogram predicting 1‐, 3‐ and 5‐y OS for patients with HCC (A). The nomogram is applied by adding up the points identified on the points scale for each variable. The total points projected on the bottom scales indicate the probability of 1‐, 3‐ and 5‐y OS. The calibration curve for predicting 1‐, 3‐ and 5‐y OS for patients with HCC (B). The Y‐axis represents actual survival, as measured by K‐M analysis, and the X‐axis represents the nomogram‐predicted survival

Figure 6

The time‐dependent ROC and DCA curves of the nomograms. Time‐dependent ROC curves analysis evaluates the accuracy of the nomograms (A). The black, red, green or blue solid line represents the nomogram. The DCA curves can intuitively evaluate the clinical benefit of the nomograms and the scope of application of the nomograms to obtain clinical benefits (B). The net benefits (Y‐axis) as calculated are plotted against the threshold probabilities of patients having 1‐, 3‐ and 5‐y survival on the X‐axis. The orange dotted line represents the assumption that all patients have 1‐, 3‐ and 5‐y survival. The grey solid line represents the assumption that no patients have 1‐, 3‐ or 5‐y survival. The black, red, green or blue solid lines represent the nomograms