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Background <p>Hepatocellular carcinoma (HCC) is a prevalent global malignancy characterized by a high incidence and poor prognosis. Current diagnostic and therapeutic modalities are insufficient to meet the demands for effective HCC diagnosis and management. Cuproptosis represents a novel mechanism of cellular death, which may offer new avenues for drug research in oncology.</p> Methods <p>We employed the Summary-data-based Mendelian Randomization (SMR) method alongside gene intersection analyses related to Cuproptosis, identifying SIRT2 as the target gene. Single-cell RNA sequencing (scRNA-seq) and spatial transcriptome sequencing (stRNA-seq) were conducted to investigate the role of SIRT2 in HCC. Subsequently, we analyzed the differentially expressed genes of SIRT2 + malignant cell (SIRT2 + Mali) using a Deep Learning Survival Neural Network (deepsurv), leading to the construction of a prognosis model. The protein interactions between SIRT2 and the key genes involved in the model were assessed. Finally, RNA sequencing (RNA-seq) analysis was performed, encompassing gene correlation analysis, immune checkpoint analysis, GO, GSEA, and KEGG enrichment analysis, immune cell infiltration, clinical characteristics analysis and drug sensitivity analysis.</p> Results <p>SIRT2 was identified as a gene positively correlated with HCC risk through SMR analysis. The scRNA-seq analysis revealed that, compared to SIRT2- malignant cell (SIRT2- Mali), SIRT2 + Mali exhibited stronger interactions with cancer-associated fibroblasts (CAF), tumor-associated macrophages (TAM), and tumor-associated endothelial cells (TEC), participating in more diverse metabolic pathways. The stRNA-seq analysis indicated a robust spatial correlation of SIRT2 + Mali with CAF, TAM, and TEC. The deepsurv prognostic model demonstrated that the survival rate of patients with elevated risk scores was significantly lower than that of those with low risk scores. RNA-seq analysis confirmed a positive correlation between SIRT2 and various immune checkpoint genes. Immune cell infiltration analyses indicated higher abundance scores of monocytic lineage, endothelial cells, and fibroblasts in the SIRT2 high expression group compared to the SIRT2 low expression group. The expression of SIRT2 was associated with gender, T classification, and Stage.</p> Conclusion <p>SIRT2 is posited as a pathogenic gene for HCC. It may facilitate the growth and invasion of HCC via multiple mechanisms, including Cuproptosis, tumor microenvironment modulation, metabolic pathway alteration, and immune checkpoint activation. SIRT2 presents as a potential biomarker for HCC and may serve as a novel therapeutic target for its treatment.</p>

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Elucidating the role of SIRT2 in hepatocellular carcinoma through multi-omics and deep learning

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月3日

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  • Changan Chen,
  • Tingmei Yang,
  • Caiyuan Yu,
  • Lijiao Cui,
  • Bingjun Cai,
  • Zhenkai Li,
  • Shicai Ye,
  • Yijie Weng

摘要

Background

Hepatocellular carcinoma (HCC) is a prevalent global malignancy characterized by a high incidence and poor prognosis. Current diagnostic and therapeutic modalities are insufficient to meet the demands for effective HCC diagnosis and management. Cuproptosis represents a novel mechanism of cellular death, which may offer new avenues for drug research in oncology.

Methods

We employed the Summary-data-based Mendelian Randomization (SMR) method alongside gene intersection analyses related to Cuproptosis, identifying SIRT2 as the target gene. Single-cell RNA sequencing (scRNA-seq) and spatial transcriptome sequencing (stRNA-seq) were conducted to investigate the role of SIRT2 in HCC. Subsequently, we analyzed the differentially expressed genes of SIRT2 + malignant cell (SIRT2 + Mali) using a Deep Learning Survival Neural Network (deepsurv), leading to the construction of a prognosis model. The protein interactions between SIRT2 and the key genes involved in the model were assessed. Finally, RNA sequencing (RNA-seq) analysis was performed, encompassing gene correlation analysis, immune checkpoint analysis, GO, GSEA, and KEGG enrichment analysis, immune cell infiltration, clinical characteristics analysis and drug sensitivity analysis.

Results

SIRT2 was identified as a gene positively correlated with HCC risk through SMR analysis. The scRNA-seq analysis revealed that, compared to SIRT2- malignant cell (SIRT2- Mali), SIRT2 + Mali exhibited stronger interactions with cancer-associated fibroblasts (CAF), tumor-associated macrophages (TAM), and tumor-associated endothelial cells (TEC), participating in more diverse metabolic pathways. The stRNA-seq analysis indicated a robust spatial correlation of SIRT2 + Mali with CAF, TAM, and TEC. The deepsurv prognostic model demonstrated that the survival rate of patients with elevated risk scores was significantly lower than that of those with low risk scores. RNA-seq analysis confirmed a positive correlation between SIRT2 and various immune checkpoint genes. Immune cell infiltration analyses indicated higher abundance scores of monocytic lineage, endothelial cells, and fibroblasts in the SIRT2 high expression group compared to the SIRT2 low expression group. The expression of SIRT2 was associated with gender, T classification, and Stage.

Conclusion

SIRT2 is posited as a pathogenic gene for HCC. It may facilitate the growth and invasion of HCC via multiple mechanisms, including Cuproptosis, tumor microenvironment modulation, metabolic pathway alteration, and immune checkpoint activation. SIRT2 presents as a potential biomarker for HCC and may serve as a novel therapeutic target for its treatment.