AUTHOR=Zheng Ju-Yan , Liu Jun-Yan , Zhu Tao , Liu Chong , Gao Ying , Dai Wen-Ting , Zhuo Wei , Mao Xiao-Yuan , He Bai-Mei , Liu Zhao-Qian 

TITLE=Effects of Glycolysis-Related Genes on Prognosis and the Tumor Microenvironment of Hepatocellular Carcinoma

JOURNAL=Frontiers in Pharmacology

VOLUME=Volume 13 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2022.895608

DOI=10.3389/fphar.2022.895608

ISSN=1663-9812

ABSTRACT=Background: Hepatocellular carcinoma (HCC) is a common and deadly malignancy worldwide. Current treatment methods for hepatocellular carcinoma have many disadvantages, thus it is urgent to improve the efficacy of these therapies. Glycolysis is critical in the occurrence and development of tumors. However, survival and prognosis biomarkers related to glycolysis in HCC patients remains to be fully identified.
Methods: Glycolysis-related genes (GRGs) were downloaded from “The Molecular Signatures Database” (MSigDB), the mRNA expression profiles and clinical information of HCC patients were obtained from TCGA. Consensus clustering was performed to classify the HCC patients into two subgroups. We used The least absolute shrinkage and selection operator (LASSO) regression analysis to construct risk signature model. Kaplan-Meier (K-M) survival analysis was performed to evaluate the prognostic significance of the risk model and receiver operating characteristic (ROC) curve analysis were used to evaluate prediction accuracy. The independent prediction ability of the risk model was validated by univariant and multivariant Cox regression analysis. The differences of immune infiltrates and relevant oncogenic signaling between different risk groups were compared. Finally, biological experiments were performed to explore the functions of screened genes.
Results: HCC patients were classified into two subgroups according to the expression of prognostic-related GRGs. Almost all GRGs categorized in cluster 2 showed up-regulated expression, whereas GRGs in cluster 1 conferred survival advantages. GSEA identified a positive correlation between cluster 2 and glycolysis process. Ten genes were selected for risk signature construction. Patients were assigned into high-risk and low-risk groups based on the median risk score and K-M survival analysis indicated that the high-risk group had shorter survival time. Additionally, risk gene signature can partially affect immune infiltrates within HCC microenvironment, and many oncogenic pathways were enriched in the high-risk group, including glycolysis, hypoxia, DNA repair, etc. Finally, in vitro knockdown of ME1 suppressed proliferation, migration and invasion of hepatocellular carcinoma cells.
Conclusion: In our study, we successfully constructed and verified a novel glycolysis-related risk signature for HCC prognosis prediction, which is meaningful for classifying HCC patients and offers potential targets for treatment of hepatocellular carcinoma.
Keywords: Hepatocellular carcinoma; glycolysis; prognosis; risk signature; immune infiltrates.