A glycolysis-related gene pairs signature predicts prognosis in patients with hepatocellular carcinoma

Abstract

Background: Hepatocellular carcinoma (HCC) is one of the most universal malignant liver tumors worldwide. However, there were no systematic studies to establish glycolysis‑related gene pairs (GRGPs) signatures for the patients with HCC. Therefore, the study aimed to establish novel GRGPs signatures to better predict the prognosis of HCC.

Methods: Based on the data from Gene Expression Omnibus, The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium databases, glycolysis-related mRNAs were used to construct GRGPs. Cox regression was applied to establish a seventeen GRGPs signature in TCGA dataset, which was verified in two validation (European and American, and Asian) datasets.

Results: Seventeen prognostic GRGPs (HMMR_PFKFB1, CHST1_GYS2, MERTK_GYS2, GPC1_GYS2, LDHA_GOT2, IDUA_GNPDA1, IDUA_ME2, IDUA_G6PD, IDUA_GPC1, MPI_GPC1, SDC2_LDHA, PRPS1_PLOD2, GALK1_IER3, MET_PLOD2, GUSB_IGFBP3, IL13RA1_IGFBP3 and CYB5A_IGFBP3) were identified to be significantly progressive factors for the patients with HCC in the TCGA dataset, which constituted a GRGPs signature. The patients with HCC were classified into low-risk group and high-risk group based on the GRGPs signature. The GRGPs signature was a significantly independent prognostic indicator for the patients with HCC in TCGA (log-rank P = 2.898e-14). Consistent with the TCGA dataset, the patients in low-risk group had a longer OS in two validation datasets (European and American: P = 1.143e-02, and Asian: P = 6.342e-08). Additionally, the GRGPs signature was also validated as a significantly independent prognostic indicator in two validation datasets.

Conclusion: The seventeen GRGPs and their signature might be molecular biomarkers and therapeutic targets for the patients with HCC.

Keywords: GRGPs signature; Glycolysis; Hepatocellular carcinoma; Prognosis; mRNAs.

Figure 1. Enrichment graph of five gene sets with significant differences between tumor and non-tumor tissues.

The high expression of these genes was principally enriched in biological processes such as glycolysis, DNA repair, metabolism and protein synthesis secretion.

Figure 2. GRGPs selection utilizing lasso model based on TCGA dataset.

(A) Elastic net regularization course with partial likelihood deviance plot. The vertical dashed line with minimum partial likelihood deviance value is at the optimal logarithmic (Lambda) value. Lambda is the parameter which controls the regulation degree of lasso regression complexity. The ordinate is the value of the coefficient, the lower abscissa is log (lambda), and the upper abscissa is the number of non-zero coefficients in the model. (B) Lasso coefficient values of 17 prognosis GRGPs. Each colored line represents the change track of each independent variable coefficient. (C) Time-dependent ROC curve for GRGPs at 1 year. GRGPs score of −0.698 was utilized as cutoff point for GRGPs.

Figure 3. The Kaplan–Meier (KM) survival curves of the GRGPs signature for patients with HCC based on TCGA and two validation datasets.

(A) The KM survival curves of TCGA dataset demonstrated that high-risk group had shorter OS period contrasted with low-risk group (P < 0.001). (B and C) Consistent with TCGA dataset, the OS of patients in high-risk group was shorter than that in low-risk group in two validation datasets (P < 0.05).

Figure 4. The GRGPs signature analysis of patients with HCC in TCGA dataset.

(A) The low-risk group and high-risk group for the GRGPs signature in patients with HCC. (B) The survival status and time of patients with HCC. (C) Visualized heat map of the seventeen vital prognosis GRGPs expression in patients with HCC. The color from green to red reveals a rising tendency from low to high levels.

Figure 5. Prognostic indicators based on GRGPs signature revealed great clinical values in TCGA dataset and Asian dataset.

Univariate (A) and multivariate (B) Cox regression of the relevancy between clinical features (containing the risk score) and OS of patients with HCC in the TCGA dataset. Univariate (C) and multivariate (D) Cox regression of the relevancy between clinical indicators (including the risk score) and OS of patients with HCC in the Asian dataset.

Figure 6. The evaluation of prognostic GRGPs signature in the TCGA dataset and the Asian dataset.

(A and E) The nomogram figure about 1-year, 3-year or 5-year OS in HCC based on the TCGA dataset (A) or the Asian dataset (E). The point in the nomogram represented the individual score of each variable under different values. Total points represented the sum of the individual scores corresponding to all variables. For a single variable, we could get the corresponding point by drawing a vertical line upward, which must be perpendicular to the point line. For example, if someone’s risk score is −1, the corresponding point of risk score in nomogram based on the TCGA dataset was about 42.5 by drawing a vertical line upward. Similarly, the corresponding point of the third stage was about 16.5. Then, add the points of all variables to get the total points of the patient (59). Based on the total point, the corresponding 1-year survival rate of the patient was about 0.86. (B, C, F and G) Calibration plots of 3-year (B) and 5-year (C) based on the TCGA dataset. Calibration plots of 3-year (F) and 5-year (G) based on the Asian dataset. Calibration plots for evaluating the agreement between the predicted and the actual OS for the model established by GRGPs. The 45° reference line indicates perfect calibration, where the predicted probabilities are consistent with the actual probabilities. (D and H) The areas under the ROC curve corresponding to 1 year, 3 years and 5 years of survival in the TCGA (D) or Asian datasets (H). The higher area under the ROC curve meant greater model accuracy.