Constructing a disulfidptosis-related prognostic signature of hepatocellular carcinoma based on single-cell sequencing and weighted co-expression network analysis

Abstract

Hepatocellular carcinoma (HCC) ranks as the second leading cause of cancer-related deaths globally. Disulfidptosis is a newly identified form of regulated cell death that is induced by glucose starvation. However, the clinical prognostic characteristics of disulfidptosis-associated genes in HCC remain poorly understood. We conducted an analysis of the single-cell datasets GSE149614 and performed weighted co-expression network analysis (WGCNA) on the Cancer Genome Atlas (TCGA) datasets to identify the genes related to disulfidptosis. A prognostic model was constructed using univariate COX and Lasso regression. Survival analysis, immune microenvironment analysis, and mutation analysis were performed. Additionally, a nomogram associated with disulfidptosis-related signature was constructed to identify the prognosis of HCC patients. Patients with HCC in the TCGA and GSE14520 datasets were categorized using a disulfidptosis-related model, revealing significant differences in survival times between the high- and low-disulfidptosis groups. High-disulfidptosis patients exhibited increased expression of immune checkpoint-related genes, implying that immunotherapy and certain chemotherapies may be beneficial for them. Meanwhile, the ROC and decision curves analysis (DCA) indicated that the nomogram has satisfying prognostic efficacy. Moreover, the experimental results of GATM in this prognostic model indicated that GATM is low expressed in HCC tissues, and GATM knockdown promotes the proliferation and migration of HCC cells. By analyzing single-cell and bulk multi-omics sequencing data, we developed a prognostic signature related to disulfidptosis and explored the relationship between high- and low-disulfidptosis groups in HCC. This study offers a novel reference for gaining a deeper understanding of the role of disulfidptosis in HCC.