Prognostic evaluation of glycolysis markers in hepatocellular carcinoma: insights from meta-analysis and multi-omics approaches

Abstract

Background: Glycolysis, a central process of cellular energy metabolism, has been shown to be closely associated with the development of hepatocellular carcinoma (HCC). This study aimed to investigate the prognostic value of the glycolysis gene set (GGS) in HCC.

Methods: Online databases were searched to identify studies on the correlation between glycolysis-related gene signature score and clinical characteristics in patients with HCC. HR and OR values with 95% CI were calculated. Bioinformatics analysis and in vitro validation were used to validate the results of the meta-analysis and investigate the potential oncogenic mechanisms of GGS.

Results: Nineteen studies involving 3,406 patients were included. The pooled analysis showed that a high glycolysis-related gene signature score was associated with poor overall survival (OS) (HR = 1.98, 95% CI 1.59-2.46, P < 0.001), disease-free survival (DFS) (HR = 2.02, 95% CI 1.54-2.64, P < 0.001), and relapse-free survival (RFS) (HR = 2.38, 95% CI 1.39-4.08, P = 0.002). Bioinformatic and in vitro experiments confirmed the prognostic relevance and differential expression of GGS in HCC, and functional assays of ENO1 further demonstrated its role in HCC progression.

Conclusion: The upregulation of the glycolysis-related gene signature score is predominantly associated with poor prognosis in patients with HCC, suggesting that GGS may serve as a potential prognostic biomarker and therapeutic target for HCC, as exemplified by ENO1 functional validation.

Keywords: Biomarker; Glycolysis gene set; Hepatocellular carcinoma; Prognosis; Therapeutic target.

Fig. 1

The PRISMA flow diagram of study selection and screening

Fig. 2

Prognostic significance of GGS in the HCC patients of the meta-analysis with random-effects model: (A) unadjusted OS. (B) adjusted OS. (C) DFS. (D) RFS

Fig. 3

The tpm expression level of GGS in HCC. (A) normal and tumor tissues, (B) tumor and its paired tissues, (C) ROC graphics. Statistical significance is indicated as follows: P < 0.05 (*), P < 0.01 (**), and P < 0.001 (***)

Fig. 4

The Kaplan-Meier plotter method was used to draw the relationship between the high and low expression of GGS in HCC and the survival time (A) OS of TPI1, (B) OS of ENO1, (C) OS of PGK1, (D) OS of SLC2A1, (E) OS of PKM, (F) DSS of TPI1, (G) DSS of ENO1, (H) DSS of PGK1, (I) DSS of PKM, (J) DFI of TPI1, (K) DFI of ENO1, (L) PFI of TPI1, (M) PFI of ENO1, (N) PFI of PKM

Fig. 5

The relationship between the tpm expression of GGS in HCC and its clinical features. (A) gender, (B) grade stage, (C) tumor stage, (D) T stage, (E) ROC curves of clinical features and GGS, (F) nomogram, (G) calibration curves. Statistical significance is indicated as follows: P < 0.05 (*), P < 0.01 (**), and P < 0.001 (***)

Fig. 6

(A) RT-qPCR results of GGS using the HL-7702 cell line as the normal control group, (B) Western blot results of glycolysis-related gene signature score in normal and tumor cells, and (C) Immunohistochemistry analysis of glycolysis-related gene signature score in normal and tumor tissues. The mRNA levels following the knockdown of ENO1 expression in MHCC97H (D). (E) Assessment of the relationship between ENO1 knockout and cell proliferation using the MTT assay. Knockdown of ENO1 reduces the cell mobility of HCC in MHCC97H cells (F). Knockdown of ENO1 inhibits the migration and invasion of HCC in MHCC97H cells (G). Statistical significance is indicated as follows: P < 0.05 (*), P < 0.01 (**), and P < 0.001 (***)

Fig. 7

Hypothetical mechanistic model summarizing the regulatory role of GGS in HCC, based on literature evidence