Prognosis stratification of patients with breast invasive carcinoma based on cysteine metabolism-disulfidptosis affinity

Zhang, Yuting; Ye, Yinghui; Xu, Anping; Luo, Yulou; Sun, Yutian; Zhang, Wei; Ji, Ling

doi:10.1007/s00432-023-05028-y

Cited by 3 publications

(2 citation statements)

References 41 publications

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“…Metabolically, disulfidptosis relates to glycolipid metabolism, sulfide metabolism, redox balance, and cellular structures stability. Recent studies have indicated a close relationship between disulfidptosis and metabolic processes, indicating new directions for metabolic NB treatment (Liu et al 2023a , b ; Zheng et al 2023a , b ; Zhang et al 2023 ), though detailed molecular understanding and clinical application require more research.…”

Section: Discussionmentioning

confidence: 99%

Integrated machine learning-driven disulfidptosis profiling: CYFIP1 and EMILIN1 as therapeutic nodes in neuroblastoma

Mengzhen,

Xinwei,

Zeheng

et al. 2024

J Cancer Res Clin Oncol

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Background Neuroblastoma (NB), a prevalent pediatric solid tumor, presents formidable challenges due to its high malignancy and intricate pathogenesis. The role of disulfidptosis, a novel form of programmed cell death, remains poorly understood in the context of NB. Methods Gaussian mixture model (GMM)-identified disulfidptosis-related molecular subtypes in NB, differential gene analysis, survival analysis, and gene set variation analysis were conducted subsequently. Weighted gene co-expression network analysis (WGCNA) selected modular genes most relevant to the disulfidptosis core pathways. Integration of machine learning approaches revealed the combination of the Least absolute shrinkage and selection operator (LASSO) and Random Survival Forest (RSF) provided optimal dimensionality reduction of the modular genes. The resulting model was validated, and a nomogram assessed disulfidptosis characteristics in NB. Core genes were filtered and subjected to tumor phenotype and disulfidptosis-related experiments. Results GMM clustering revealed three distinct subtypes with diverse prognoses, showing significant variations in glucose metabolism, cytoskeletal structure, and tumor-related pathways. WGCNA highlighted the red module of genes highly correlated with disulfide isomerase activity, cytoskeleton formation, and glucose metabolism. The LASSO and RSF combination yielded the most accurate and stable prognostic model, with a significantly worse prognosis for high-scoring patients. Cytological experiments targeting core genes (CYFIP1, EMILIN1) revealed decreased cell proliferation, migration, invasion abilities, and evident cytoskeletal deformation upon core gene knockdown. Conclusions This study showcases the utility of disulfidptosis-related gene scores for predicting prognosis and molecular subtypes of NB. The identified core genes, CYFIP1 and EMILIN1, hold promise as potential therapeutic targets and diagnostic markers for NB.

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Section: Discussionmentioning

confidence: 99%

Integrated machine learning-driven disulfidptosis profiling: CYFIP1 and EMILIN1 as therapeutic nodes in neuroblastoma

Mengzhen,

Xinwei,

Zeheng

et al. 2024

J Cancer Res Clin Oncol

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“…There is an urgent need to develop a signature to predict prognosis and immunotherapy response in HCC. Although some studies have demonstrated the value of disulfidptosis in the development and prognosis of lung adenocarcinoma, breast cancer, and renal cell carcinoma [15][16][17], the specific mechanism of disulfidptosis and its actual value in the development and prognosis of HCC have not yet been clearly revealed.…”

Section: Introductionmentioning

confidence: 99%

Development of the RF-GSEA Method for Identifying Disulfidptosis-Related Genes and Application in Hepatocellular Carcinoma

Ni,

Yu,

You

et al. 2023

CIMB

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Disulfidptosis is a newly discovered cellular programmed cell death mode. Presently, a considerable number of genes related to disulfidptosis remain undiscovered, and its significance in hepatocellular carcinoma remains unrevealed. We have developed a powerful analytical method called RF-GSEA for identifying potential genes associated with disulfidptosis. This method draws inspiration from gene regulation networks and graph theory, and it is implemented through a combination of random forest regression model and Gene Set Enrichment Analysis. Subsequently, to validate the practical application value of this method, we applied it to hepatocellular carcinoma. Based on the RF-GSEA method, we developed a disulfidptosis-related signature. Lastly, we looked into how the disulfidptosis-related signature is connected to HCC prognosis, the tumor microenvironment, the effectiveness of immunotherapy, and the sensitivity of chemotherapy drugs. The RF-GSEA method identified a total of 220 disulfidptosis-related genes, from which 7 were selected to construct the disulfidptosis-related signature. The high-disulfidptosis-related score group had a worse prognosis compared to the low-disulfidptosis-related score group and showed lower infiltration levels of immune-promoting cells. The high-disulfidptosis-related score group had a higher likelihood of benefiting from immunotherapy compared to the low-disulfidptosis-related score group. The RF-GSEA method is a powerful tool for identifying disulfidptosis-related genes. The disulfidptosis-related signature effectively predicts HCC prognosis, immunotherapy response, and drug sensitivity.

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Identification of molecular characteristics associated with disulfidptosis and ferroptosis-related genes in breast cancer, along with immune cell infiltration Analysis and Development of a prognostic risk model

Pei,

Wang,

Zhao

et al. 2023

Preprint

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Background Breast cancer (BRCA) is a common and highly heterogeneous malignancy with a relative poor prognosis. Disulfidptosis is a newly discovered mode of cell death induced by disulfide stress. Like ferroptosis, disulfidptosis is a novel form of programmed cell death. The goal of this research was to explore new biomarkers associated with disulfidptosis and ferroptosis that can guide the treatment of BRCA. Methods We collected expression and clinical information about BRCA patients from public database. We comprehensively characterized the relationship between disulfidptosis and ferroptosis-related genes and the molecular characteristics as well as immune cell infiltration of tumor tissue. Next, a risk model was identified based on COX regression model with least absolute shrinkage and selection operator regression (LASSO) algorithm. Besides, we evaluated the prognostic value and treatment sensitivity of various subtypes. Results Specific molecular characteristics and model possessed a promising predictive potential. Immune infiltration and treatment sensitivity analysis all showed differences between groups. Conclusions Our molecular subtypes and risk model, with strong immune prediction and prognostic prediction capabilities, are committed to guiding BRCA treatment.

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Prognosis stratification of patients with breast invasive carcinoma based on cysteine metabolism-disulfidptosis affinity

Cited by 3 publications

References 41 publications

Integrated machine learning-driven disulfidptosis profiling: CYFIP1 and EMILIN1 as therapeutic nodes in neuroblastoma

Integrated machine learning-driven disulfidptosis profiling: CYFIP1 and EMILIN1 as therapeutic nodes in neuroblastoma

Development of the RF-GSEA Method for Identifying Disulfidptosis-Related Genes and Application in Hepatocellular Carcinoma

Identification of molecular characteristics associated with disulfidptosis and ferroptosis-related genes in breast cancer, along with immune cell infiltration Analysis and Development of a prognostic risk model

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