Abstract
Objective: Non-alcoholic fatty liver disease (NAFLD) is the most prevalent liver disease in the world, and its pathogenesis is not fully understood. Disulfidptosis is the most recently reported form of cell death and may be associated with NAFLD progression. Our study aimed to explore the molecular clusters associated with disulfidptosis in NAFLD and to construct a predictive model.

Methods: First, we analyzed the expression profile of the disulfidptosis regulators and immune characteristics in NAFLD. Using 104 NAFLD samples, we investigated molecular clusters based on differentially expressed disulfidptosis-related genes, along with the related immune cell infiltration. Cluster-specific differentially expressed genes were then identified by using the WGCNA method. We also evaluated the performance of four machine learning models before choosing the optimal machine model for diagnosis. Nomogram, calibration curves, decision curve analysis, and external datasets were used to confirm the prediction effectiveness. Finally, the expression levels of the biomarkers were assessed in a mouse model of a high-fat diet.

Results: Two differentially expressed DRGs were identified between healthy and NAFLD patients. We revealed the expression profile of DRGs in NAFLD and the correlation with 22 immune cells. In NAFLD, two clusters of molecules connected to disulfidptosis were defined. Significant immunological heterogeneity was shown by immune infiltration analysis among the various clusters. A significant amount of immunological infiltration was seen in Cluster 1. Functional analysis revealed that Cluster 1 differentially expressed genes were strongly linked to energy metabolism and immune control. The highest discriminatory performance was demonstrated by the SVM model, which had a higher area under the curve, relatively small residual and root mean square errors. Nomograms, calibration curves, and decision curve analyses were used to show how accurate the prediction of NAFLD was. Further analysis revealed that the expression of three model-related genes was significantly associated with the level of multiple immune cells. In animal experiments, the expression trends of DDO, FRK and TMEM19 were consistent with the results of bioinformatics analysis.

Conclusion: This study systematically elucidated the complex relationship between disulfidptosis and NAFLD and developed a promising predictive model to assess the risk of disease in patients with disulfidptosis subtypes and NAFLD.

Background: Despite accumulating evidence revealing that Glucose-6-phosphate dehydrogenase (G6PD) is highly expressed in many tumor tissues and plays a remarkable role in cancer tumorigenesis and progression, there is still a lack of G6PD pan-cancer analysis. This study was designed to analyze the expression status and prognostic significance of G6PD in pan-cancer.

Methods: G6PD expression data were obtained from multiple data resources including the Genotype-Tissue Expression, the Cancer Genome Atlas, and the Tumor Immunity Estimation Resource. These data were used to assess the G6PD expression, prognostic value, and clinical characteristics. The ESTIMATE algorithms were used to analyze the association between G6PD expression and immune-infiltrating cells and the tumor microenvironment. The functional enrichment analysis was also performed across pan-cancer. In addition, the GDSC1 database containing 403 drugs was utilized to explore the relationship between drug sensitivity and G6PD expression levels. Furthermore, we also performed clinical validation and in vitro experiments to further validate the role of G6PD in hepatocellular carcinoma (HCC) cells and its correlation with prognosis. The R software was used for statistical analysis and data visualization.

Results: G6PD expression was upregulated in most cancers compared to their normal counterparts. The study also revealed that G6PD expression was a prognostic indicator and high levels of G6PD expression were correlated with worse clinical prognosis including overall survival, disease-specific survival, and progression-free interval in multiple cancers. Furthermore, the G6PD level was also related to cancer immunity infiltration in most of the cancers, especially in KIRC, LGG, and LIHC. In addition to this, G6PD expression was positively related to pathological stages of KIRP, BRCA, KIRC, and LIHC. Functional analysis and protein-protein interactions network results revealed that G6PD was involved in metabolism-related activities, immune responses, proliferation, and apoptosis. Drug sensitivity analysis showed that IC50 values of most identified anti-cancer drugs were positively correlated with the G6PD expression. Notably, in vitro functional validation showed that G6PD knockdown attenuated the phenotypes of proliferation in HCC.

Conclusion: G6PD may serve as a potential prognostic biomarker for cancers and may be a potential therapeutic target gene for tumor therapy.