Scholarlink

Background <p>Hepatocellular carcinoma (HCC) is a common malignant tumor characterized by a high recurrence rate and poor prognosis. This study aimed to identify glycolysis-related prognostic markers and immunological abnormalities in patients with HCC.</p> Methods <p>We collected samples from cancerous and adjacent non-cancerous tissues for transcriptomic, metabolomic, and <i>16&#xa0;S rRNA</i> sequencing analyses. Glycolysis-related prognostic markers were identified by integrating public data from The Cancer Genome Atlas, GSE14520, and GSE76427 datasets. Additionally, single-cell sequencing data (GSE202642) were used to analyze the significantly infiltrated cellular subpopulations in HCC and investigate the expression of prognostic markers across different cell types. Spatial transcriptomics and mass cytometry (CyTOF) data were used to examine the expression differences in immune cells across tumor, peritumoral, and control tissues. Key prognostic markers were validated using reverse transcription-quantitative polymerase chain reaction, western blotting, and immunohistochemistry.</p> Results <p>Differentially expressed genes (DEGs) between HCC and control tissues were primarily clustered in cell cycle and metabolic pathways, particularly in the glycolysis pathway. Metabolomic analysis identified 175 differentially expressed metabolites that were mainly enriched in digestive and amino acid metabolism pathways. <i>16&#xa0;S rRNA</i> analysis revealed a significant increase in the abundance of Aenigmarchaeota and a decrease in the abundance of Proteobacteria in HCC tissues. The former was positively associated with glycolysis, whereas the latter showed a negative association. Through public data integration, 17 glycolysis-related DEGs were identified and 101 predictive models were constructed using machine learning. The StepCox[both] + random survival forest model using AGL, G6PD, GOT2, and KIF20A exhibited the best diagnostic performance among the three datasets. Single-cell RNA sequencing indicated significant infiltration of CD8 + Tex, CD8 + T, CD8 + Trm, and epithelial cells in HCC tissues. AGL, G6PD, GOT2, and KIF20A were highly expressed in CD8 + Tex cells, CD8 + Trm cells, macrophages, and monocytes, respectively. Spatial transcriptomics and CyTOF analyses showed greater infiltration of CD8 + Tex and CD8 + Trm cells in tumor tissues than in controls. Molecular assays further confirmed that G6PD and KIF20A expression levels were significantly higher, whereas AGL and GOT2 expression levels were lower, in HCC tissues than in control tissues.</p> Conclusion <p>Through integrative multi-omics analysis, we identified glycolysis-related prognostic markers with distinct expression profiles across immune cell subsets in HCC. Our findings identify potential biomarkers and therapeutic targets for the diagnosis and treatment of HCC.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

The glycolytic characteristics of hepatocellular carcinoma and its interaction with the microenvironment: a comprehensive omics study

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年4月10日

查看全文
  • Dan Chen,
  • Dandan Lin,
  • Huling Li,
  • Jiandong Yang,
  • Lei Liu,
  • Hanyuan Zhang,
  • Dandan Tang,
  • Kai Wang

摘要

Background

Hepatocellular carcinoma (HCC) is a common malignant tumor characterized by a high recurrence rate and poor prognosis. This study aimed to identify glycolysis-related prognostic markers and immunological abnormalities in patients with HCC.

Methods

We collected samples from cancerous and adjacent non-cancerous tissues for transcriptomic, metabolomic, and 16 S rRNA sequencing analyses. Glycolysis-related prognostic markers were identified by integrating public data from The Cancer Genome Atlas, GSE14520, and GSE76427 datasets. Additionally, single-cell sequencing data (GSE202642) were used to analyze the significantly infiltrated cellular subpopulations in HCC and investigate the expression of prognostic markers across different cell types. Spatial transcriptomics and mass cytometry (CyTOF) data were used to examine the expression differences in immune cells across tumor, peritumoral, and control tissues. Key prognostic markers were validated using reverse transcription-quantitative polymerase chain reaction, western blotting, and immunohistochemistry.

Results

Differentially expressed genes (DEGs) between HCC and control tissues were primarily clustered in cell cycle and metabolic pathways, particularly in the glycolysis pathway. Metabolomic analysis identified 175 differentially expressed metabolites that were mainly enriched in digestive and amino acid metabolism pathways. 16 S rRNA analysis revealed a significant increase in the abundance of Aenigmarchaeota and a decrease in the abundance of Proteobacteria in HCC tissues. The former was positively associated with glycolysis, whereas the latter showed a negative association. Through public data integration, 17 glycolysis-related DEGs were identified and 101 predictive models were constructed using machine learning. The StepCox[both] + random survival forest model using AGL, G6PD, GOT2, and KIF20A exhibited the best diagnostic performance among the three datasets. Single-cell RNA sequencing indicated significant infiltration of CD8 + Tex, CD8 + T, CD8 + Trm, and epithelial cells in HCC tissues. AGL, G6PD, GOT2, and KIF20A were highly expressed in CD8 + Tex cells, CD8 + Trm cells, macrophages, and monocytes, respectively. Spatial transcriptomics and CyTOF analyses showed greater infiltration of CD8 + Tex and CD8 + Trm cells in tumor tissues than in controls. Molecular assays further confirmed that G6PD and KIF20A expression levels were significantly higher, whereas AGL and GOT2 expression levels were lower, in HCC tissues than in control tissues.

Conclusion

Through integrative multi-omics analysis, we identified glycolysis-related prognostic markers with distinct expression profiles across immune cell subsets in HCC. Our findings identify potential biomarkers and therapeutic targets for the diagnosis and treatment of HCC.