Identification of a glycolysis-related gene signature for predicting prognosis in patients with hepatocellular carcinoma

BMC Cancer

Jiawei Chai¹  Guangbing Li²  Guangsheng Yu²  Yong Liu³  Junjie Kong⁴  Jun Liu⁴  Wei Si⁴ 

[1] Department of Breast and Thyroid Surgery, Shandong Maternity and Child Care Hospital, 250014, Jinan, Shandong Province, China;Department of Liver Transplantation and Hepatobiliary Surgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, 250021, Jinan, Shandong, China;Department of Liver Transplantation and Hepatobiliary Surgery, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, 250021, Jinan, Shandong Province, China;Department of Liver Transplantation and Hepatobiliary Surgery, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, 250021, Jinan, Shandong Province, China;Department of Liver Transplantation and Hepatobiliary Surgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, 250021, Jinan, Shandong, China;

关键词: Hepatocellular carcinoma;    Glycolysis;    Prognosis;    GSEA;    Gene signature;    Tumor mutational burden;    TCGA;   

DOI  :  10.1186/s12885-022-09209-9

来源: Springer

【 摘 要 】

BackgroundHepatocellular carcinoma (HCC) is the most common primary liver cancer in the world. Although great advances in HCC diagnosis and treatment have been achieved, due to the complicated mechanisms in tumor development and progression, the prognosis of HCC is still dismal. Recent studies have revealed that the Warburg effect is related to the development, progression and treatment of various cancers; however, there have been a few explorations of the relationship between glycolysis and HCC prognosis.MethodsmRNA expression profiling was downloaded from public databases. Gene set enrichment analysis (GSEA) was used to explore glycolysis-related genes (GRGs), and the LASSO method and Cox regression analysis were used to identify GRGs related to HCC prognosis and to construct predictive models associated with overall survival (OS) and disease-free survival (DFS). The relationship between the predictive model and the tumor mutation burden (TMB) and tumor immune microenvironment (TIME) was explored. Finally, real-time PCR was used to validate the expression levels of the GRGs in clinical samples and different cell lines.ResultsFive GRGs (ABCB6, ANKZF1, B3GAT3, KIF20A and STC2) were identified and used to construct gene signatures to predict HCC OS and DFS. Using the median value, HCC patients were divided into low- and high-risk groups. Patients in the high-risk group had worse OS/DFS than those in the low-risk group, were related to higher TMB and were associated with a higher rate of CD4+ memory T cells resting and CD4+ memory T cells activated. Finally, real-time PCR suggested that the five GRGs were all dysregulated in HCC samples compared to adjacent normal samples.ConclusionsWe identified five GRGs associated with HCC prognosis and constructed two GRGs-related gene signatures to predict HCC OS and DFS. The findings in this study may contribute to the prediction of prognosis and promote HCC treatment.

【 授权许可】

CC BY