Disulfidptosis: a new target for metabolic cancer therapy

Zheng, Peijie; Zhou, Chuntao; Ding, Yuemin; Duan, Shiwei

doi:10.1186/s13046-023-02675-4

Cited by 134 publications

(107 citation statements)

References 5 publications

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“…Actin networks can collapse if the disulfide bonds among proteins are not repaired, leading to cell death. Disulfidptosis, a novel type of cell death, has been excavated recently, and disulfidptosis regulators are screened, which will ultimately contribute to the advancement of therapeutic approaches targeting cancer 41 . It is crucial to recognize the underlying mechanisms of disulfidptosis, as they have significant impacts on understanding the fundamentals of cellular homeostasis, as well as providing insight into treatment of diverse diseases such as cancer.…”

Section: Discussionmentioning

confidence: 99%

Multi‐omics analysis of disulfidptosis regulators and therapeutic potential reveals glycogen synthase 1 as a disulfidptosis triggering target for triple‐negative breast cancer

Xie,

Deng,

Xie

et al. 2024

MedComm

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Disruption of disulfide homeostasis during biological processes can have fatal consequences. Excess disulfides induce cell death in a novel manner, termed as “disulfidptosis.” However, the specific mechanism of disulfidptosis has not yet been elucidated. To determine the cancer types sensitive to disulfidptosis and outline the corresponding treatment strategies, we firstly investigated the crucial functions of disulfidptosis regulators pan‐cancer at multi‐omics levels. We found that different tumor types expressed dysregulated levels of disulfidptosis regulators, most of which had an impact on tumor prognosis. Moreover, we calculated the disulfidptosis activity score in tumors and validated it using multiple independent datasets. Additionally, we found that disulfidptosis activity was correlated with classic biological processes and pathways in various cancers. Disulfidptosis activity was also associated with tumor immune characteristics and could predict immunotherapy outcomes. Notably, the disulfidptosis regulator, glycogen synthase 1 (GYS1), was identified as a promising target for triple‐negative breast cancer and validated via in vitro and in vivo experiments. In conclusion, our study elucidated the complex molecular phenotypes and clinicopathological correlations of disulfidptosis regulators in tumors, laying a solid foundation for the development of disulfidptosis‐targeting strategies for cancer treatment.

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Section: Discussionmentioning

confidence: 99%

Multi‐omics analysis of disulfidptosis regulators and therapeutic potential reveals glycogen synthase 1 as a disulfidptosis triggering target for triple‐negative breast cancer

Xie,

Deng,

Xie

et al. 2024

MedComm

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“…Therefore, it is urgent to identify reliable prognostic signatures for early prognosis to facilitate the diagnosis and treatment of ovarian cancer. Recently, emerging research reported that, disul dptosis, a newly discovered type of non-programmed cell death characterized by iron accumulation and lipid peroxidation, was closely associated with various physiological and pathological processes in cancer development 14 . However, the landscape of disul dptosis in ovarian cancer remains unclear.…”

Section: Discussionmentioning

confidence: 99%

Identification of a disulfidptosis-related genes signature for prognostic implication in ovarian cancer

Tang,

An,

Sun

2024

Preprint

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Background: Ovarian cancer is an extremely deadly gynecological malignancy, with a 5-year survival rate below 30%. Additionally, disulfidptosis, a newly discovered type of cell death, has been found to be closely associated with the onset and progression of tumors. Methods: Disulfidptosis-related clusters were identified by consensus clustering. Univariate and multivariate Cox regression analyses were applied to construct a prognostic risk model. Patients were then divided into high- and low-risk groups. Gene mutation frequency, tumor microenvironment, and drug sensitivity analysis were performed between these two groups. Subsequently, a nomogram was constructed. Results: We identified 721 differentially expressed genes (DEGs) from two disulfidptosis-related clusters, and constructed a risk-prognosis signature. Analysis of the risk score revealed that compared to the high-risk group, the low-risk group had a better prognosis. Gene mutation frequency and tumor microenvironment analysis identified distinct characteristics between two risk groups. We also screened potential chemotherapy drugs that could sensitize ovarian cancer. Finally, the nomogram based on risk score and other clinical features showed a strong prognostic capability to predict overall survival (OS) for ovarian cancer patients. Conclusion: This study constructed a risk model related to disulfidptosis, which has a good prognostic value for ovarian cancer patients. The findings of this research provide novel insights into the understanding of ovarian cancer and could potentially lead to the development of new treatment strategies.

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“…However, as the underlying mechanism of disulfidptosis has been discovered, the potential link between disulfidptosis‐related genes (DRGs) and AD remains unclear. Currently, studies on disulfidptosis have mainly focused on cancers 18–20 . A recent study has reported that the dysregulation of actin cytoskeletal dynamics is associated with the pathology of AD 21 .…”

Section: Introductionmentioning

confidence: 99%

Machine learning identification and immune infiltration of disulfidptosis‐related Alzheimer's disease molecular subtypes

Zhu,

Kong,

Han

et al. 2023

Immunity Inflam & Disease

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BackgroundAlzheimer's disease (AD) is a common neurodegenerative disorder. Disulfidptosis is a newly discovered form of programmed cell death that holds promise as a therapeutic strategy for various disorders. However, the functional roles of disulfidptosis‐related genes (DRGs) in AD remain unknown.MethodsMicroarray data and clinical information from patients with AD and healthy controls were downloaded from the Gene Expression Omnibus database. A thorough examination of DRG expression and immune characteristics in both groups was performed. Based on the identified DRGs, we performed an unsupervised clustering analysis to categorize the AD samples into various disulfidptosis‐related molecular clusters. Weighted gene co‐expression network analysis was performed to select hub genes specific to disulfidptosis‐related AD clusters. The performances of various machine learning models were compared to determine the optimal predictive model. The predictive ability of the optimal model was assessed using nomogram analysis and five external datasets.ResultsEight DRGs showed differential expression between the AD and control samples. Two different molecular clusters were identified. The immune cell infiltration analysis revealed distinct differences in the immune microenvironment of the two clusters. The support vector machine model showed the highest performance, and a panel of five signature genes was identified, which showed excellent performance on the external validation datasets. The nomogram analysis also showed high accuracy in predicting AD.ConclusionWe identified disulfidptosis‐related molecular clusters in AD and established a novel risk model to assess the likelihood of developing AD. These findings revealed a complex association between disulfidptosis and AD, which may aid in identifying potential therapeutic targets for this debilitating disorder.

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Disulfidptosis: a new target for metabolic cancer therapy

Cited by 134 publications

References 5 publications

Multi‐omics analysis of disulfidptosis regulators and therapeutic potential reveals glycogen synthase 1 as a disulfidptosis triggering target for triple‐negative breast cancer

Multi‐omics analysis of disulfidptosis regulators and therapeutic potential reveals glycogen synthase 1 as a disulfidptosis triggering target for triple‐negative breast cancer

Identification of a disulfidptosis-related genes signature for prognostic implication in ovarian cancer

Machine learning identification and immune infiltration of disulfidptosis‐related Alzheimer's disease molecular subtypes

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