Seven oxidative stress-related genes predict the prognosis of hepatocellular carcinoma

Abstract

Predicting the prognosis of hepatocellular carcinoma (HCC) is a major medical challenge and of guiding significance for treatment. This study explored the actual relevance of RNA expression in predicting HCC prognosis. Cox's multiple regression was used to establish a risk score staging classification and to predict the HCC patients' prognosis on the basis of data in the Cancer Genome Atlas (TCGA). We screened seven gene biomarkers related to the prognosis of HCC from the perspective of oxidative stress, including Alpha-Enolase 1(ENO1), N-myc downstream-regulated gene 1 (NDRG1), nucleophosmin (NPM1), metallothionein-3, H2A histone family member X, Thioredoxin reductase 1 (TXNRD1) and interleukin 33 (IL-33). Among them we measured the expression of ENO1, NGDP1, NPM1, TXNRD1 and IL-33 to investigate the reliability of the multi-index prediction. The first four markers' expressions increased successively in the paracellular tissues, the hepatocellular carcinoma samples (from patients with better prognosis) and the hepatocellular carcinoma samples (from patients with poor prognosis), while IL-33 showed the opposite trend. The seven genes increased the sensitivity and specificity of the predictive model, resulting in a significant increase in overall confidence. Compared with the patients with higher-risk scores, the survival rates with lower-risk scores are significantly increased. Risk score is more accurate in predicting the prognosis HCC patients than other clinical factors. In conclusion, we use the Cox regression model to identify seven oxidative stress-related genes, investigate the reliability of the multi-index prediction, and develop a risk staging model for predicting the prognosis of HCC patients and guiding precise treatment strategy.

Keywords: Alpha-Enolase 1; Cox regression model; N-myc downstream-regulated gene 1; hepatocellular carcinoma; nucleophosmin.

Figure 1

Differential oxidative stress-related genes between liver cancer and normal tissues. A volcano map (A) and heatmap (B) about differential oxidative stress-related genes was created, the down-regulated genes were expressed in blue, the up-regulated genes in yellow and the genes that did not differ between groups were represented in grey. Lambda obtained minimum value (C), model enrolled 11 important genes (D).

Figure 2

Seven oxidative stress-related genes predict patient prognosis model in training dataset. (A) A signature model for each of the 7 genes was constructed, and the risk score for each patient based on the model was calculated. X axis is number of patients. (B) Survival analysis about patients in the high-risk group and the low-risk group. (C) Analysis of the area under the ROC curve. The signature model based on Cox regression coefficients and gene expression, the riskscore = gene A expression* coefficients A+ gene B expression* coefficients B.

Figure 3

Seven oxidative stress-related genes predict patient prognosis model in test dataset. (A) A signature model for each of the 7 genes was constructed, and the risk score for each patient based on the model was calculated. X axis is number of patients. (B) Survival analysis about patients in the high-risk group and the low-risk group. (C) Analysis of the area under the ROC curve. The signature model based on Cox regression coefficients and gene expression, the riskscore = gene A expression* coefficients A+ gene B expression* coefficients B.

Figure 4

Seven oxidative stress-related genes detect early-stage liver cancer. (A) The model genes in the signature using the BP network were analyzed and the network was visualized. (B) The seven genes were used as a panel for early detection of liver cancer.

Figure 5

ENO1, NDGR1 and NPM1 expression in HCC tissues. (A) PPI network identifies ENO1 as hub gene of signature. (B, C) The expression of ENO1 gene in paracellular tissues and hepatocellular carcinoma tissues. (D) The protein expression of ENO1, NDGR1, NPM1, TXNRD1 and IL-33 in hepatocellular carcinoma tissues and paracellular tissues (n= 10 per group). Samples were stained with H&E staining to observe the structure of paracellular tissues and hepatocellular carcinoma tissues (n= 10 per group). Patients’ essential characteristics were shown in Supplementary Table 4. Normal (*): paracellular tissues; Tumor (*): hepatocellular carcinoma tissues, EDMONDSON Classification: II; Microvascular tumor thrombus: MVI grade 0; Tumor (**): hepatocellular carcinoma tissues, EDMONDSON Classification: III-IV; Microvascular tumor thrombus: MVI grade 1.

Figure 6

High expression of hub gene ENO1 suggests disease progression. (A) The clinical correlation analysis of ENO1 gene expression and tumor size and stage. The expression of ENO1 in different tumor stages (B), and in the pathological Grade stage (C). ENO1 was not associated with lymph node status (D) or distant metastatic status (E).

Figure 7

High expression of hub gene ENO1 correlates with tumor immune infiltration. The expression of ENO1 (A) correlated with Th2 cells (B), aDC (C), NK CD56bright cells (D), macrophages (E), pDC (F), CD8 T cells (G) and Th17 cells (H) in the tumor microenvironment.