Identification of subclusters and prognostic genes based on glycolysis/gluconeogenesis in hepatocellular carcinoma

Dan Chen; Ayinuer Aierken; Hui Li; Ruihua Chen; Lei Ren; Kai Wang

doi:10.3389/fimmu.2023.1232390

Identification of subclusters and prognostic genes based on glycolysis/gluconeogenesis in hepatocellular carcinoma

Front Immunol. 2023 Oct 10:14:1232390. doi: 10.3389/fimmu.2023.1232390. eCollection 2023.

Authors

Dan Chen¹, Ayinuer Aierken², Hui Li³, Ruihua Chen⁴, Lei Ren⁵, Kai Wang⁶

Affiliations

¹ School of Public Health, Xinjiang Medical University, Urumqi, China.
² Department of Hepatobiliary Hydatid Disease, the First Affiliated Hospital of Xinjiang Medical University, Urumqi, China.
³ Central Laboratory, Xinjiang Medical University, Urumqi, China.
⁴ Center of Animal Experiments, Xinjiang Medical University, Urumqi, China.
⁵ Department of Burns, the First Affiliated Hospital of Xinjiang Medical University, Urumqi, China.
⁶ Department of Medical Engineering and Technology, Xinjiang Medical University, Urumqi, China.

Abstract

Background: This study aimed to examine glycolysis/gluconeogenesis-related genes in hepatocellular carcinoma (HCC) and evaluate their potential roles in HCC progression and immunotherapy response.

Methods: Data analyzed in this study were collected from GSE14520, GSE76427, GSE174570, The Cancer Genome Atlas (TCGA), PXD006512, and GSE149614 datasets, metabolic pathways were collected from MSigDB database. Differentially expressed genes (DEGs) were identified between HCC and controls. Differentially expressed glycolysis/gluconeogenesis-related genes (candidate genes) were obtained and consensus clustering was performed based on the expression of candidate genes. Bioinformatics analysis was used to evaluate candidate genes and screen prognostic genes. Finally, the key results were tested in HCC patients.

Results: Thirteen differentially expressed glycolysis/gluconeogenesis-related genes were validated in additional datasets. Consensus clustering analysis identified two distinct patient clusters (C1 and C2) with different prognoses and immune microenvironments. Immune score and tumor purity were significantly higher in C1 than in C2, and CD4+ memory activated T cell, Tfh, Tregs, and macrophage M0 were higher infiltrated in HCC and C1 group. The study also identified five intersecting DEGs from candidate genes in TCGA, GSE14520, and GSE141198 as prognostic genes, which had a protective role in HCC patient prognosis. Compared with the control group, the prognostic genes all showed decreased expression in HCC patients in RT-qPCR and Western blot analyses. Flow cytometry verified the abnormal infiltration level of immune cells in HCC patients.

Conclusion: Results showed that glycolysis/gluconeogenesis-related genes were associated with patient prognosis, immune microenvironment, and response to immunotherapy in HCC. It suggests that the model based on five prognostic genes may valuable for predicting the prognosis and immunotherapy response of HCC patients.

Keywords: consensus clustering; glycolysis/gluconeogenesis; hepatocellular carcinoma; immune microenvironments; overall survival.

MeSH terms

Carcinoma, Hepatocellular* / genetics
Gluconeogenesis
Glycolysis / genetics
Humans
Liver Neoplasms* / genetics
Prognosis
Tumor Microenvironment / genetics

Grants and funding

This work was supported by the Youth science and technology innovation talent of Tianshan Talent Training Program in Xinjiang (Grant No.: 2022TSYCCX0099), the Technology Innovation Team (Tianshan Innovation Team) Project (Grant No.: 2022TSYCTD0018) and the Undergraduate innovation and entrepreneurship training programs (Grant No.: S202210760193X).