Role of gasdermin family proteins in cancers (Review)

Yang, Xin; Tang, Zhe

doi:10.3892/ijo.2023.5548

Cited by 6 publications

(2 citation statements)

References 122 publications

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“…3D ). Subsequently, the pathway analysis further revealed potential downregulation of the PDGFRB pathway, involved in stromal cell activity and associated with improved patient response [ 49 ], while upregulation of pathways for antimicrobial peptides, FLT3 signaling, ephrin B reverse signaling and potential therapeutics for SARS ( Fig. 3E ), suggesting enhanced immune surveillance and interaction with tumor cells.…”

Section: Resultsmentioning

confidence: 99%

“…Similarly, in the RCB-I (good response) components—RNA expression analysis revealed that lower expression of genes GPX1P1 and HBB are linked to less aggressive tumors [ 48 ], while those of thiosulfate sulfurtransferase (TST), NPIPA5 and GSDMB were overexpressed, linked to enhanced immune response and therapeutic effectiveness [ 49 , 50 ]. Mutational analysis showed positive association for therapeutic targets signatures TP53, MUC16 and RYR2 [ 51 , 52 ], but a negative in NEB, and CIN scores.…”

Section: Resultsmentioning

confidence: 99%

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Advancing drug-response prediction using multi-modal and -omics machine learning integration (MOMLIN): a case study on breast cancer clinical data

Rashid,

Selvarajoo

2024

Briefings in Bioinformatics

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The inherent heterogeneity of cancer contributes to highly variable responses to any anticancer treatments. This underscores the need to first identify precise biomarkers through complex multi-omics datasets that are now available. Although much research has focused on this aspect, identifying biomarkers associated with distinct drug responders still remains a major challenge. Here, we develop MOMLIN, a multi-modal and -omics machine learning integration framework, to enhance drug-response prediction. MOMLIN jointly utilizes sparse correlation algorithms and class–specific feature selection algorithms, which identifies multi-modal and -omics–associated interpretable components. MOMLIN was applied to 147 patients’ breast cancer datasets (clinical, mutation, gene expression, tumor microenvironment cells and molecular pathways) to analyze drug-response class predictions for non-responders and variable responders. Notably, MOMLIN achieves an average AUC of 0.989, which is at least 10% greater when compared with current state-of-the-art (data integration analysis for biomarker discovery using latent components, multi-omics factor analysis, sparse canonical correlation analysis). Moreover, MOMLIN not only detects known individual biomarkers such as genes at mutation/expression level, most importantly, it correlates multi-modal and -omics network biomarkers for each response class. For example, an interaction between ER-negative-HMCN1-COL5A1 mutations-FBXO2-CSF3R expression-CD8 emerge as a multimodal biomarker for responders, potentially affecting antimicrobial peptides and FLT3 signaling pathways. In contrast, for resistance cases, a distinct combination of lymph node-TP53 mutation-PON3-ENSG00000261116 lncRNA expression-HLA-E-T-cell exclusions emerged as multimodal biomarkers, possibly impacting neurotransmitter release cycle pathway. MOMLIN, therefore, is expected advance precision medicine, such as to detect context–specific multi-omics network biomarkers and better predict drug-response classifications.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Advancing drug-response prediction using multi-modal and -omics machine learning integration (MOMLIN): a case study on breast cancer clinical data

Rashid,

Selvarajoo

2024

Briefings in Bioinformatics

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GEFT inhibits the GSDM-mediated proptosis signalling pathway, promoting the progression and drug resistance of rhabdomyosarcoma

Yang,

Xia,

Zhao

et al. 2024

Cell Death Dis

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The metastasis or recurrence of rhabdomyosarcoma (RMS) is the primary cause of tumour-related deaths. Patients with high-risk RMS have poor prognosis with a 5-year overall survival rate of 20–30%. The lack of specific drug-targeted therapy and chemotherapy resistance are the main reasons for treatment failure. Drugs or molecular target inhibitors can induce the pyroptosis of tumour cells or increase their sensitivity to chemotherapy, making pyroptosis an effective strategy for antitumour therapies. Pyroptosis is mediated by gasdermin (GSDM) family members. Here, we found that the expression of NLRP3, caspase-1, caspase-3, GSDMD and GSDME in RMS was remarkably lower than that in skeletal muscle tissues. Nigericin and dactinomycin in RMS cells achieved their regulatory effect on pyroptosis through the NLRP3/caspase-1/GSDMD pathway and caspase-3/GSDME pathway, respectively. Necrosulfonamide reversed the pyroptosis-related changes induced by nigericin, and siGSDME converted the dactinomycin-induced pyroptosis into apoptosis. Additionally, GEFT inhibited the GSDMD and GSDME pyroptosis pathways, thereby promoting the progression and drug resistance of RMS. Mouse xenograft and tumour analysis confirmed that nigericin and dactinomycin can effectively improve the therapeutic effect of RMS by activating the pyroptosis pathway. To the best of our knowledge, this study was the first to focus on pyroptosis in RMS. Overall, our investigation demonstrated that nigericin and dactinomycin play therapeutic roles in tumours by promoting RMS cell pyroptosis. Interference with GEFT and drug combination can exert a great inhibitory effect on tumours.

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Inhibition of NLRP3 inflammasome contributes to paclitaxel efficacy in triple negative breast cancer treatment

Balahura Stămat,

Dinescu

2024

Sci Rep

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Chronic inflammation and NLRP3 inflammasome activation are among the determining factors of breast malignancies. Paclitaxel (PTX) is a drug used in breast cancer treatment which sustains prolonged inflammation, reducing the effectiveness of chemotherapy. Considering the impact of inflammatory processes in cancer progression, there is a strong concern to develop therapeutic strategy targeting NLRP3 inflammasome for triple-negative breast cancer (TNBC) treatment. Therefore, the aim of this study was to evaluate the potential of PTX and NLRP3 inflammasome modulation to counterbalance TNBC by inducing programmed cell death and inhibiting the activity of pro-inflammatory cytokines. The obtained results suggested the strong interaction between NLRP3 inflammasome and TNBC and revealed that pharmacological inhibition, using NLRP3-specific inhibitor MCC950, and genetic silencing of NLRP3 inflammasome using specific small interfering RNA, reduced inflammatory responses and facilitated PTX-determined tumor cell death. Thus, NLRP3 inflammasome manipulation in combination with anti-tumor drugs opens up new therapeutic perspectives for TNBC therapy.

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Role of gasdermin family proteins in cancers (Review)

Cited by 6 publications

References 122 publications

Advancing drug-response prediction using multi-modal and -omics machine learning integration (MOMLIN): a case study on breast cancer clinical data

Advancing drug-response prediction using multi-modal and -omics machine learning integration (MOMLIN): a case study on breast cancer clinical data

GEFT inhibits the GSDM-mediated proptosis signalling pathway, promoting the progression and drug resistance of rhabdomyosarcoma

Inhibition of NLRP3 inflammasome contributes to paclitaxel efficacy in triple negative breast cancer treatment

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