Targeting pyroptosis in breast cancer: biological functions and therapeutic potentials on It

Chen, Cong; Ye, Qianwei; Wang, Linbo; Zhou, Jichun; Xiang, Aizhai; Lin, Xianghong; Guo, Jufeng; Hu, Shufang; Tao, Rui; Liu, Jian

doi:10.1038/s41420-023-01370-9

Cited by 13 publications

(7 citation statements)

References 143 publications

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“…Recent studies have revealed targeting pyroptosis is a promising approach to treat breast cancer (42,43). Evidence are accumulating that by triggering biological process in breast cancer, the tumor microenvironment could be reorganized and thus antitumor immunity could be enhanced (44).…”

Section: Discussionmentioning

confidence: 99%

A novel signature based on crosstalk between anoikis- and pyroptosis-associated genes for prediction of clinical outcomes, TME and treatment response of breast cancer

Liu

et al. 2023

Preprint

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Background: Breast cancer is nowthe most prevalent malignant among female population worldwide. Anoikis is a key progress during genesis and metastasis of malignant cells. Pyroptosis is a newly defined type of programmed cell death reported to have a dual effect on the development of carcinomas and had been reported to have the potential to affect anti-tumor immunity. However, few studies investigated the connections between anoikis, pyroptosis and prognosis in breast cancer. Methods: Anoikis and pyroptosis-related genes (APGs) were achieved from GeneCards and Harmonizome portals database. Based on expression profiles of APGs of patients from TCGA-BRCA cohort, differentiated expressed APGs between normal and tumoral tissues are identified. Next, by univariate Cox regression analysis of combined data of TCGA and GSE cohorts, prognostic APGs was defined. Then patients from both TCGA and GEO cohort were classified into three clusters by consensus clustering algorithm. Overlapped APGs between three clusters were identified as intersecting genes, based on expression of which, individuals are again assigned to two different gene clusters. Eventually, we successfully developed a PCA scoring signature and a nomogram system to accurately predict the prognosis and immunotherapy efficacy of breast cancer patients. Results: Patients were classified into three clusters based on APGs’ expression. Cluster A was featured by longest OS. According to the expression profile of 300 intersecting genes, patients were again divided into two different gene clusters. Subtype B is characterized with poorer diagnosis. Meanwhile, by means of principal component analysis, we successfully predicted clinical outcomes and treatment response to immunotherapy. Finally, we constructed an APG score-associated nomogram model to predict prognosis. Conclusion: We successfully established a scoring system based on anoikis and pyroptosis-related genes, as well as combined with clinicopathological features, to serve as a biomarker for prediction of clinical outcomes and immunotherapy efficacy in breast cancer.

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Section: Discussionmentioning

confidence: 99%

A novel signature based on crosstalk between anoikis- and pyroptosis-associated genes for prediction of clinical outcomes, TME and treatment response of breast cancer

Liu

et al. 2023

Preprint

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“…These pathways play crucial roles in eliminating undesirable or damaged cells from the organism [ 68 , 69 , 70 ]. However, in BC, PCD becomes dysregulated, resulting in the survival and proliferation of cancer cells [ 71 , 72 , 73 ]. The process of PCD can be delineated into three stages: initiation, execution, and degradation and clearance.…”

Section: Stress Signaling and Programmed Cell Death In Bcmentioning

confidence: 99%

Nanoparticle-Based Antioxidants in Stress Signaling and Programmed Cell Death in Breast Cancer Treatment

et al. 2023

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Breast cancer (BC) is a complex and heterogeneous disease, and oxidative stress is a hallmark of BC. Oxidative stress is characterized by an imbalance between the production of reactive oxygen species (ROS) and antioxidant defense mechanisms. ROS has been implicated in BC development and progression by inducing DNA damage, inflammation, and angiogenesis. Antioxidants have been shown to scavenge ROS and protect cells from oxidative damage, thereby regulating signaling pathways involved in cell growth, survival, and death. Plants contain antioxidants like ascorbic acid, tocopherols, carotenoids, and flavonoids, which have been found to regulate stress signaling and PCD in BC. Combining different antioxidants has shown promise in enhancing the effectiveness of BC treatment. Antioxidant nanoparticles, when loaded with antioxidants, can effectively target breast cancer cells and enhance their cellular uptake. Notably, these nanoparticles have shown promising results in inducing PCD and sensitizing breast cancer cells to chemotherapy, even in cases where resistance is observed. This review aims to explore how nanotechnology can modulate stress signaling and PCD in breast cancer. By summarizing current research, it underscores the potential of nanotechnology in enhancing antioxidant properties for the treatment of breast cancer.

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“…[8] Currently, a single treatment can hardly fulfill the therapeutic needs of BRCA. Emerging evidences suggest that features based on cell death patterns, such as ferroptosis, [9] pyroptosis, [10] and necroptosis, [11] have significant value in predicting the prognosis, tumor immune microenvironment, and immune therapy response of BRCA patients. Researchers established a cell death index model by comprehensively analyzing diverse cell death patterns, which can accurately predict clinical prognosis and drug sensitivity of triple negative breast cancer (TNBC) after surgery.…”

Section: Introductionmentioning

confidence: 99%

A novel cuproptosis-related genes model in breast cancer prognosis

Zheng¹,

Shi²,

Zhang

et al. 2023

Medicine

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Breast cancer (BRCA) is a highly heterogeneous malignancy with an urgent need to build a proper model to predict its prognosis. Cuproptosis is a recently discovered form of cell death, mediated by protein fatty acylation and tightly associated with mitochondrial metabolism. The role of cuproptosis-related genes (CRGs) in BRCA remains to be explored. We aimed to investigate the applications of CRGs in BRCA prognosis in different clinical contexts, including chemotherapy and immunotherapy, via bioinformatics analysis of the messenger RNA profiles and clinical data obtained from public databases. Molecular subtyping of CRGs was performed through consistent clustering analysis. Differentially expressed genes between different CRG clusters were identified. The differentially expressed genes were then used to build a risk assessment model using least absolute shrinkage and selection operator regression to predict patient survival with BRCA. The model was then validated with the data from the Molecular Taxonomy of Breast Cancer International Consortium, GSE96058, and GSE20685. Differences in somatic mutations, copy number variations, hallmark pathways, drug responses, and prognosis of immunotherapy and chemotherapy were analyzed by comparing the high-risk and low-risk groups. Patients with high-risk scores showed worse overall survival than those with low-risk scores. The results indicated significant differences between the 2 groups immune-related biological pathways and the variable immune status. It also suggests the differential sensitivity to chemotherapy between the 2 groups. The CRGs model showed the promise to predict the prognosis of BRCA patients and shed light on their treatment.

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Targeting pyroptosis in breast cancer: biological functions and therapeutic potentials on It

Cited by 13 publications

References 143 publications

A novel signature based on crosstalk between anoikis- and pyroptosis-associated genes for prediction of clinical outcomes, TME and treatment response of breast cancer

A novel signature based on crosstalk between anoikis- and pyroptosis-associated genes for prediction of clinical outcomes, TME and treatment response of breast cancer

Nanoparticle-Based Antioxidants in Stress Signaling and Programmed Cell Death in Breast Cancer Treatment

A novel cuproptosis-related genes model in breast cancer prognosis

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