PANoptosis-related signature in melanoma: Transcriptomic mapping and clinical prognostication.
PANoptosis
immunology
melanoma
prognosis
programmed cell death
Journal
Environmental toxicology
ISSN: 1522-7278
Titre abrégé: Environ Toxicol
Pays: United States
ID NLM: 100885357
Informations de publication
Date de publication:
08 Jan 2024
08 Jan 2024
Historique:
revised:
19
12
2023
received:
23
11
2023
accepted:
25
12
2023
medline:
8
1
2024
pubmed:
8
1
2024
entrez:
8
1
2024
Statut:
aheadofprint
Résumé
Programmed cell death plays a pivotal role in maintaining tissue homeostasis, and recent advancements in cell biology have uncovered PANoptosis-a novel paradigm integrating pyroptosis, apoptosis, and necroptosis. This study investigates the implications of PANoptosis in melanoma, a formidable skin cancer known for its metastatic potential and resistance to conventional therapies. Leveraging bulk and single-cell transcriptome analyses, machine learning modeling, and immune correlation assessments, we unveil the molecular intricacies of PANoptosis in melanoma. Single-cell sequencing identifies diverse cell types involved in PANoptosis, while bulk transcriptome analysis reveals key gene sets correlated with PANoptosis. Machine learning algorithms construct a robust prognostic model, demonstrating consistent predictive power across diverse cohorts. Patients with different cohorts can be divided into high-risk and low-risk groups according to this PANoptosis score, with the high-risk group having a significantly worse prognosis. Immune correlation analyses unveil a link between PANoptosis and immunotherapy response, with potential therapeutic implications. Mutation analysis and enrichment studies provide insights into the mutational landscape associated with PANoptosis. Finally, we used cell experiments to verify the expression and function of key gene PARVA, showing that PARVA was highly expressed in melanoma cell lines, and after PARVA is knocked down, cell invasion, migration, and colony formation ability were significantly decreased. This study advances our understanding of PANoptosis in melanoma, offering a comprehensive framework for targeted therapeutic interventions and personalized medicine strategies in combating this aggressive malignancy.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : National Natural Science Foundation of China
ID : 82073019
Informations de copyright
© 2024 Wiley Periodicals LLC.
Références
Nowak-Sliwinska P, Griffioen AW. Rising impact of cell death research. Apoptosis. 2023;28(11-12):1503-1504. doi:10.1007/s10495-023-01895-5
Bertheloot D, Latz E, Franklin BS. Necroptosis, pyroptosis and apoptosis: an intricate game of cell death. Cell Mol Immunol. 2021;18(5):1106-1121. doi:10.1038/s41423-020-00630-3
Chen Q, Sun Y, Wang S, Xu J. New prospects of cancer therapy based on pyroptosis and pyroptosis inducers. Apoptosis. 2023. doi:10.1007/s10495-023-01906-5
Zheng M, Kanneganti TD. The regulation of the ZBP1-NLRP3 inflammasome and its implications in pyroptosis, apoptosis, and necroptosis (PANoptosis). Immunol Rev. 2020;297(1):26-38. doi:10.1111/imr.12909
Pandian N, Kanneganti TD. PANoptosis: a unique innate immune inflammatory cell death modality. J Immunol. 2022;209(9):1625-1633. doi:10.4049/jimmunol.2200508
Zheng Y, Li J, Liu B, et al. Global trends in PANoptosis research: bibliometrics and knowledge graph analysis. Apoptosis. 2023. doi:10.1007/s10495-023-01889-3
Pan H, Pan J, Li P, Gao J. Characterization of PANoptosis patterns predicts survival and immunotherapy response in gastric cancer. Clin Immunol. 2022;238:109019. doi:10.1016/j.clim.2022.109019
Jiang M, Qi L, Li L, Wu Y, Song D, Li Y. Caspase-8: a key protein of cross-talk signal way in "PANoptosis" in cancer. Int J Cancer. 2021;149(7):1408-1420. doi:10.1002/ijc.33698
Lin JF, Hu PS, Wang YY, et al. Phosphorylated NFS1 weakens oxaliplatin-based chemosensitivity of colorectal cancer by preventing PANoptosis. Signal Transduct Target Ther. 2022;7(1):54. doi:10.1038/s41392-022-00889-0
Wang X, Sun R, Chan S, et al. PANoptosis-based molecular clustering and prognostic signature predicts patient survival and immune landscape in colon cancer. Front Genet. 2022;14(13):955355. doi:10.3389/fgene.2022.955355
Qi Z, Zhu L, Wang K, Wang N. PANoptosis: emerging mechanisms and disease implications. Life Sci. 2023;333:122158. doi:10.1016/j.lfs.2023.122158
Mattia G, Puglisi R, Ascione B, Malorni W, Carè A, Matarrese P. Cell death-based treatments of melanoma:conventional treatments and new therapeutic strategies. Cell Death Dis. 2018;9(2):112. doi:10.1038/s41419-017-0059-7
Zou Y, Yaguchi T. Programmed cell death-1 blockade therapy in melanoma: resistance mechanisms and combination strategies. Exp Dermatol. 2023;32(3):264-275. doi:10.1111/exd.14750
Manzari Tavakoli G, Mirzapour MH, Razi S, Rezaei N. Targeting ferroptosis as a cell death pathway in melanoma: from molecular mechanisms to skin cancer treatment. Int Immunopharmacol. 2023;119:110215. doi:10.1016/j.intimp.2023.110215
Bogunovic D, O'Neill DW, Belitskaya-Levy I, et al. Immune profile and mitotic index of metastatic melanoma lesions enhance clinical staging in predicting patient survival. Proc Natl Acad Sci U S A. 2009;106(48):20429-20434.
Jönsson G, Busch C, Knappskog S, et al. Gene expression profiling-based identification of molecular subtypes in stage IV melanomas with different clinical outcome. Clin Cancer Res. 2010;16(13):3356-3367.
Cabrita R, Lauss M, Sanna A, et al. Tertiary lymphoid structures improve immunotherapy and survival in melanoma. Nature. 2020;577(7791):561-565.
Jerby-Arnon L, Shah P, Cuoco MS, et al. A cancer cell program promotes T cell exclusion and resistance to checkpoint blockade. Cell. 2018;175(4):984-997.e24.
Stelzer G, Rosen N, Plaschkes I, et al. The GeneCards suite: from gene data mining to disease genome sequence analyses. Curr Protoc Bioinformatics. 2016;20(54):1.30.1-1.30.33. doi:10.1002/cpbi.5
Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics. 2008;29(9):559. doi:10.1186/1471-2105-9-559
Zhang N, Zhang H, Wu W, et al. Machine learning-based identification of tumor-infiltrating immune cell-associated lncRNAs for improving outcomes and immunotherapy responses in patients with low-grade glioma. Theranostics. 2022;12(13):5931-5948. doi:10.7150/thno.74281
Li T, Fan J, Wang B, et al. TIMER: a web server for comprehensive analysis of tumor-infiltrating immune cells. Cancer Res. 2017;77(21):e108-e110. doi:10.1158/0008-5472.CAN-17-0307
Karki R, Sundaram B, Sharma BR, et al. ADAR1 restricts ZBP1-mediated immune response and PANoptosis to promote tumorigenesis. Cell Rep. 2021;37(3):109858. doi:10.1016/j.celrep.2021.109858
Samir P, Malireddi RKS, Kanneganti TD. The PANoptosome: a deadly protein complex driving pyroptosis, apoptosis, and necroptosis (PANoptosis). Front Cell Infect Microbiol. 2020;10:238. doi:10.3389/fcimb.2020.00238
Yan WT, Zhao WJ, Hu XM, et al. PANoptosis-like cell death in ischemia/reperfusion injury of retinal neurons. Neural Regen Res. 2023;18(2):357-363. doi:10.4103/1673-5374.346545
Katzenelenbogen Y, Sheban F, Yalin A, et al. Coupled scRNA-seq and intracellular protein activity reveal an immunosuppressive role of TREM2 in cancer. Cell. 2020;182(4):872-885.e19. doi:10.1016/j.cell.2020.06.032
Tian Z, He W, Tang J, et al. Identification of important modules and biomarkers in breast cancer based on WGCNA. Onco Targets Ther. 2020;13:6805-6817. doi:10.2147/OTT.S258439