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Multiple cell-death patterns predict the prognosis and drug sensitivity of melanoma patients

Melanoma, a malignant tumor of the skin, presents challenges in its treatment process involving modalities such as surgery, chemotherapy, and targeted therapy. However, there is a need for an ideal model to assess prognosis and drug sensitivity. Programmed cell death (PCD) modes play a crucial role...

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Published in:Frontiers in pharmacology 2024-10, Vol.15, p.1295687
Main Authors: Chen, Zewei, Zhang, Ruopeng, Zhao, Zhoukai, Zhao, Baiwei, Zhang, Feiyang, Chen, Guoming, Chen, Xiaojiang, Wei, Chengzhi, Lin, Jun, Lin, Feizhi, Zheng, Ziqi, Jiang, Kaiming, Nie, Runcong, Chen, Yingbo
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container_title Frontiers in pharmacology
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creator Chen, Zewei
Zhang, Ruopeng
Zhao, Zhoukai
Zhao, Baiwei
Zhang, Feiyang
Chen, Guoming
Chen, Xiaojiang
Wei, Chengzhi
Lin, Jun
Lin, Feizhi
Zheng, Ziqi
Jiang, Kaiming
Nie, Runcong
Chen, Yingbo
description Melanoma, a malignant tumor of the skin, presents challenges in its treatment process involving modalities such as surgery, chemotherapy, and targeted therapy. However, there is a need for an ideal model to assess prognosis and drug sensitivity. Programmed cell death (PCD) modes play a crucial role in tumor progression and has the potential to serve as prognostic and drug sensitivity indicators for melanoma. We analyzed 13 PCD modes including apoptosis, necroptosis, ferroptosis, pyroptosis, netotic cell death, entotic cell death, lysosome-dependent cell death, parthanatos, autophagy-dependent cell death, oxeiptosis, disulfidptosis, and alkaliptosis. These modes were used to construct a model that incorporated genes related to these 13 PCD modes to establish a cell death index (CDI) to conduct prognosis analysis. Transcriptomic, genomic, and clinical data were collected from cohorts including TCGA-SKCM, GSE19234, and GSE65904 to validate this model. A CDI consisting of ten gene signatures was established using machine learning algorithms and divided into two groups based on CDI values. The high CDI group exhibited relatively lower numbers of immune-infiltrating cells and showed resistance to commonly used drugs such as docetaxel and axitinib. Our validation results demonstrated good discrimination in PCA analysis between CDI groups, and melanoma patients with higher CDI values had worse postoperative prognoses (all p < 0.01). The CDI model, incorporating multiple PCD modes, accurately predicts the clinical prognosis and drug sensitivity of melanoma patients.
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However, there is a need for an ideal model to assess prognosis and drug sensitivity. Programmed cell death (PCD) modes play a crucial role in tumor progression and has the potential to serve as prognostic and drug sensitivity indicators for melanoma. We analyzed 13 PCD modes including apoptosis, necroptosis, ferroptosis, pyroptosis, netotic cell death, entotic cell death, lysosome-dependent cell death, parthanatos, autophagy-dependent cell death, oxeiptosis, disulfidptosis, and alkaliptosis. These modes were used to construct a model that incorporated genes related to these 13 PCD modes to establish a cell death index (CDI) to conduct prognosis analysis. Transcriptomic, genomic, and clinical data were collected from cohorts including TCGA-SKCM, GSE19234, and GSE65904 to validate this model. A CDI consisting of ten gene signatures was established using machine learning algorithms and divided into two groups based on CDI values. 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subjects cell death index
drug sensitivity
melanoma
Pharmacology
postoperative prediction model
programmed cell death
title Multiple cell-death patterns predict the prognosis and drug sensitivity of melanoma patients
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