Normal tissue adjacent to tumor expression profile analysis developed and validated a prognostic model based on Hippo-related genes in hepatocellular carcinoma

Abstract

Background: Hepatocellular carcinoma (HCC) is the most common malignant disease worldwide. Although the diagnosis and treatment of HCC have greatly improved in the recent years, there is still a lack of accurate methods to predict the prognosis of patients. Evidence has shown that Hippo signaling in tissues adjacent to HCC plays a significant role in HCC development. In the present study, we aimed to construct a model based on the expression of Hippo-related genes (HRGs) in tissues adjacent to HCC to predict the prognosis of HCC patients.

Methods: Gene expression data of paired normal tissues adjacent to HCC (PNTAH) and clinical information were obtained from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. The HRG signature was constructed using four canonical Hippo-related pathways. Univariate Cox regression analysis was used to screen survival-related HRGs. LASSO and multivariate Cox regression analyses were used to construct the prognostic model. The true and false positive rates of the model were confirmed using receiver operating characteristic (ROC) analysis.

Results: The prognostic model was constructed based on the expression levels of five HRGs (NF2, MYC, BIRC3, CSNK1E, and MINK1) in PNTAH. The mortality rate of HCC patients increased as the risk score determined by the model increased. Furthermore, the risk score was found to be an independent risk factor for the survival of patients. ROC analysis showed that the prognostic model had a better predictive value than the other conventional clinical parameters. Moreover, the reliability of the prognostic model was confirmed in TCGA-LIHC cohort. A nomogram was generated to predict patient survival. An exploration of the predictive value of the model in HCC tissues indicated that the model is PNTAH-specific.

Conclusions: We developed and validated a prognostic model based on the expression levels of five HRGs in PNTAH, and this model should be helpful in predicting the prognosis of patients with HCC.

Keywords: Hippo-related genes; hepatocellular carcinoma; paired normal tissues adjacent to HCC; prognosis.

FIGURE 1

Workflow of this study. PNTAH, paired normal tissues adjacent to HCC; GEO: Gene Expression Omnibus Database; OS: overall survival; LASSO: Least Absolute Shrinkage and Selection Operator; HRGs, Hippo‐related genes; ROC, receiver operating characteristic curve; TCGA‐LIHC: TCGA Liver Cancer Hepatocellular Carcinoma.

FIGURE 2

Differential gene expression pattern of NL, PNTAH, and HCC. (A) Principal‐component analysis (PCA) of gene expression pattern of NL, PNTAH, and HCC in GEO cohort. (B) PCA of gene expression pattern of NL, PNTAH, and HCC in TCGA‐GTEx cohort. NL, normal liver; PNTAH, paired normal tissues adjacent to HCC; HCC, hepatocellular carcinoma. PCA1 and PCA2 represent the top two dimensions of gene expression in each group.

FIGURE 3

Construction of HRG signature. (A) The heatmap of the HRG expression levels in different groups. The color of each block depends on the expression value. (B) Gene Ontology (GO) enrichment analysis of the HRG signature. (C) Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of the HRG signature. HRG: Hippo‐related genes.

FIGURE 4

Univariate Cox regression and LASSO regression analysis. (A) Fourteen HRGs with prognostic value determined by univariate Cox regression. NF2 was excluded due to the wide range of the hazard ratio. (B) The boxplot for the expression of 14 prognosis‐related HRGs in NL, PNTAH, and HCC. (C) The changing trajectory of each independent variable. (D) Confidence intervals for each lambda. HRGs: Hippo‐related genes; NL, normal liver; PNTAH, paired normal tissues adjacent to HCC; HCC, hepatocellular carcinoma. LASSO: Least Absolute Shrinkage and Selection Operator. *P value <0.05; **P value <0.01; ***P value <0.001; ns, no significance.

FIGURE 5

Evaluation of the HRG‐based prognostic model using PNTAH expression profiles from GSE14520. (A) Kaplan–Meier curve analysis of the high‐risk and low‐risk groups. (B) Time‐dependent ROC curve analysis of the prognostic model. (C) The risk score distribution of patients in the prognostic model. (D) Survival status scatter plots for patients in the prognostic model. (E) Expression patterns of risk genes in the prognostic model. PNTAH, paired normal tissues adjacent to HCC; ROC, receiver operating characteristic curve.

FIGURE 6

Validation of the HRG‐based prognostic model using PNTAH expression profiles from TCGA‐LIHC. (A) Kaplan–Meier curve analysis of the high‐risk and low‐risk groups. (B) Time‐dependent ROC curve analysis of the prognostic model. (C) The risk score distribution of patients in the prognostic model. (D) Survival status scatter plots for patients in the prognostic model. (E) Expression patterns of risk genes in the prognostic model. PNTAH, paired normal tissues adjacent to HCC; ROC, receiver operating characteristic curve.

FIGURE 7

Independence of the prognostic model and correlations with clinical parameters. (A) Univariate Cox regression analysis. (B) Multivariate Cox regression analysis. Age: ≤50 versus >50, gender: male versus female, stage: I/II versus III/IV, risk core: high risk score versus low risk score (median risk score as the cutoff value). (C) Receiver operating characteristic (ROC) curve analysis for the prognostic values of the prognostic model and other conventional clinical parameters. AUC: area under curve. (D) Association with risk score and age. (E) Association with risk score and gender. (F) Association with risk score and tumor stage. (G) Association with risk score and tumor size. (H) Association with risk score and recurrence.

FIGURE 8

Nomogram with calibration curves for the prediction of prognosis at one, three, and five years. (A) Nomogram for survival rate. (B) Calibration curves.