Cuproptosis-related classification and personalized treatment in lower-grade gliomas to prompt precise oncology.
chemotherapy
cuproptosis
lower-grade glioma
prognosis
tumor microenvironment
Journal
The journal of gene medicine
ISSN: 1521-2254
Titre abrégé: J Gene Med
Pays: England
ID NLM: 9815764
Informations de publication
Date de publication:
06 2023
06 2023
Historique:
revised:
07
02
2023
received:
06
01
2023
accepted:
16
02
2023
medline:
5
6
2023
pubmed:
24
2
2023
entrez:
23
2
2023
Statut:
ppublish
Résumé
Cuproptosis is implicated in regulating tricarboxylic acid cycle and associated with tumor therapeutic sensitivity, patient outcomes and tumorigenesis. However, the classification and prognostic effect of cuproptosis-associated genes (CAGs), the relationship between cuproptosis and tumor microenvironment (TME) and the treatment of lower-grade glioma (LrGG) remain enigmatic. The genetic and transcriptional alterations, prognostic value and classification related to cuproptosis were systematically analyzed. Subtypes of cuproptosis and cuproptosis score (Cuscore) were constructed and further confirmed by two external cohorts. The relationships between cuproptosis and TME, prognosis, and treatment response were also evaluated. Four clusters were identified based on cuproptosis-associated genes. The associations between cuproptosis-associated clusters and clinical features, prognosis, immune cell infiltration, and chemotherapy sensitivity were observed. The Cuscore is an independent prognostic indicator in LrGG patients. The nomogram is constructed according to Cuscore and clinical characteristics, and has good predictive ability and calibration. Patients with high Cuscore had a worse prognosis and advanced performance. A higher Cuscore also indicated a higher stromal score, abundant immune infiltration, and increased tumor mutation burden. A high Cuscore was remarkably related to immune checkpoint inhibitors, immunotherapy response and immune phenotype. This study demonstrates the clinical effect of CAGs, and suggests that cuproptosis could be a potential therapeutic target in LrGG.
Sections du résumé
BACKGROUND
Cuproptosis is implicated in regulating tricarboxylic acid cycle and associated with tumor therapeutic sensitivity, patient outcomes and tumorigenesis. However, the classification and prognostic effect of cuproptosis-associated genes (CAGs), the relationship between cuproptosis and tumor microenvironment (TME) and the treatment of lower-grade glioma (LrGG) remain enigmatic.
METHODS
The genetic and transcriptional alterations, prognostic value and classification related to cuproptosis were systematically analyzed. Subtypes of cuproptosis and cuproptosis score (Cuscore) were constructed and further confirmed by two external cohorts. The relationships between cuproptosis and TME, prognosis, and treatment response were also evaluated.
RESULTS
Four clusters were identified based on cuproptosis-associated genes. The associations between cuproptosis-associated clusters and clinical features, prognosis, immune cell infiltration, and chemotherapy sensitivity were observed. The Cuscore is an independent prognostic indicator in LrGG patients. The nomogram is constructed according to Cuscore and clinical characteristics, and has good predictive ability and calibration. Patients with high Cuscore had a worse prognosis and advanced performance. A higher Cuscore also indicated a higher stromal score, abundant immune infiltration, and increased tumor mutation burden. A high Cuscore was remarkably related to immune checkpoint inhibitors, immunotherapy response and immune phenotype.
CONCLUSIONS
This study demonstrates the clinical effect of CAGs, and suggests that cuproptosis could be a potential therapeutic target in LrGG.
Substances chimiques
Copper
789U1901C5
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
e3486Informations de copyright
© 2023 John Wiley & Sons Ltd.
Références
Cancer Genome Atlas Research N, Brat DJ, Verhaak RG, et al. Comprehensive, integrative genomic analysis of diffuse lower-grade gliomas. N Engl J Med. 2015;372(26):2481-2498. doi:10.1056/NEJMoa1402121
Youssef G, Miller JJ. Lower grade gliomas. Curr Neurol Neurosci Rep. 2020;20(7):21. doi:10.1007/s11910-020-01040-8
Vitucci M, Irvin DM, McNeill RS, et al. Genomic profiles of low-grade murine gliomas evolve during progression to glioblastoma. Neuro Oncol. 2017;19(9):1237-1247. doi:10.1093/neuonc/nox050
Kiran M, Chatrath A, Tang X, Keenan DM, Dutta A. A prognostic signature for lower grade gliomas based on expression of long non-coding RNAs. Mol Neurobiol. 2019;56(7):4786-4798. doi:10.1007/s12035-018-1416-y
Tsvetkov P, Coy S, Petrova B, et al. Copper induces cell death by targeting lipoylated TCA cycle proteins. Science. 2022;375(6586):1254-1261. doi:10.1126/science.abf0529
Chen J, Jiang Y, Shi H, Peng Y, Fan X, Li C. The molecular mechanisms of copper metabolism and its roles in human diseases. Pflugers Arch. 2020;472(10):1415-1429. doi:10.1007/s00424-020-02412-2
Oliveri V. Selective targeting of cancer cells by copper Ionophores: an overview. Front Mol Biosci. 2022;9:841814. doi:10.3389/fmolb.2022.841814
Voli F, Valli E, Lerra L, et al. Intratumoral copper modulates PD-L1 expression and influences tumor immune evasion. Cancer Res. 2020;80(19):4129-4144. doi:10.1158/0008-5472.CAN-20-0471
Jiang Y, Huo Z, Qi X, Zuo T, Wu Z. Copper-induced tumor cell death mechanisms and antitumor theragnostic applications of copper complexes. Nanomedicine (Lond). 2022;17(5):303-324. doi:10.2217/nnm-2021-0374
Steinbrueck A, Sedgwick AC, Brewster JT 2nd, et al. Transition metal chelators, pro-chelators, and ionophores as small molecule cancer chemotherapeutic agents. Chem Soc Rev. 2020;49(12):3726-3747. doi:10.1039/C9CS00373H
Yu G, Wang LG, Han Y, He QY. clusterProfiler: an R package for comparing biological themes among gene clusters. Omics. 2012;16(5):284-287. doi:10.1089/omi.2011.0118
Mayakonda A, Lin DC, Assenov Y, Plass C, Koeffler HP. Maftools: efficient and comprehensive analysis of somatic variants in cancer. Genome Res. 2018;28(11):1747-1756. doi:10.1101/gr.239244.118
Wilkerson MD, Hayes DN. ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking. Bioinformatics. 2010;26(12):1572-1573. doi:10.1093/bioinformatics/btq170
Mermel CH, Schumacher SE, Hill B, Meyerson ML, Beroukhim R, Getz G. GISTIC2.0 facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers. Genome Biol. 2011;12(4):R41.
Yoshihara K, Shahmoradgoli M, Martinez E, et al. Inferring tumour purity and stromal and immune cell admixture from expression data. Nat Commun. 2013;4(1):2612. doi:10.1038/ncomms3612
Geeleher P, Cox NJ, Huang RS. Clinical drug response can be predicted using baseline gene expression levels and in vitro drug sensitivity in cell lines. Genome Biol. 2014;15(3):R47. doi:10.1186/gb-2014-15-3-r47
Lu X, Meng J, Zhou Y, Jiang L, Yan F. MOVICS: an R package for multi-omics integration and visualization in cancer subtyping. Bioinformatics. 2021;36(22-23):5539-5541.
Schumacher TN, Kesmir C, van Buuren MM. Biomarkers in cancer immunotherapy. Cancer Cell. 2015;27(1):12-14. doi:10.1016/j.ccell.2014.12.004
Kursa MB, Rudnicki WR. Feature selection with the Boruta package. J Stat Softw. 2010;36(11):1-13.
Sotiriou C, Wirapati P, Loi S, et al. Gene expression profiling in breast cancer: understanding the molecular basis of histologic grade to improve prognosis. J Natl Cancer Inst. 2006;98(4):262-272. doi:10.1093/jnci/djj052
Newman AM, Liu CL, Green MR, et al. Robust enumeration of cell subsets from tissue expression profiles. Nat Methods. 2015;12(5):453-457. doi:10.1038/nmeth.3337
Hu J, Yu A, Othmane B, et al. Siglec15 shapes a non-inflamed tumor microenvironment and predicts the molecular subtype in bladder cancer. Theranostics. 2021;11(7):3089-3108. doi:10.7150/thno.53649
Mariathasan S, Turley SJ, Nickles D, et al. TGFbeta attenuates tumour response to PD-L1 blockade by contributing to exclusion of T cells. Nature. 2018;554(7693):544-548. doi:10.1038/nature25501
Liu D, Schilling B, Liu D, et al. Integrative molecular and clinical modeling of clinical outcomes to PD1 blockade in patients with metastatic melanoma. Nat Med. 2019;25(12):1916-1927. doi:10.1038/s41591-019-0654-5
Hua X, Zhao W, Pesatori AC, et al. Genetic and epigenetic intratumor heterogeneity impacts prognosis of lung adenocarcinoma. Nat Commun. 2020;11(1):2459. doi:10.1038/s41467-020-16295-5
Yang C, Huang X, Li Y, Chen J, Lv Y, Dai S. Prognosis and personalized treatment prediction in TP53-mutant hepatocellular carcinoma: an in silico strategy towards precision oncology. Brief Bioinform. 2021;22(3):bbaa164.
Jiang P, Gu S, Pan D, et al. Signatures of T cell dysfunction and exclusion predict cancer immunotherapy response. Nat Med. 2018;24(10):1550-1558. doi:10.1038/s41591-018-0136-1
Duffau H. Diffuse low-grade glioma, oncological outcome and quality of life: a surgical perspective. Curr Opin Oncol. 2018;30(6):383-389. doi:10.1097/CCO.0000000000000483
Lu L, Hu Y, Wang C, Jiang F, Wu C. Methylation and expression of the exercise-related TLR1 gene is associated with low grade glioma prognosis and outcome. Front Mol Biosci. 2021;8:747933. doi:10.3389/fmolb.2021.747933
Li S, Gao P, Dai X, Ye L, Wang Z, Cheng H. New prognostic biomarker CMTM3 in low grade glioma and its immune infiltration. Ann Transl Med. 2022;10(4):206. doi:10.21037/atm-22-526
Percival SS. Copper and immunity. Am J Clin Nutr. 1998;67(5 Suppl):1064S-1068S. doi:10.1093/ajcn/67.5.1064S
Prajapati N, Karan A, Khezerlou E, DeCoster MA. The immunomodulatory potential of copper and silver based self-assembled metal organic biohybrids nanomaterials in cancer Theranostics. Front Chem. 2020;8:629835. doi:10.3389/fchem.2020.629835
Tsang T, Davis CI, Brady DC. Copper biology. Curr Biol. 2021;31(9):R421-R427. doi:10.1016/j.cub.2021.03.054
Tang D, Chen X, Kroemer G. Cuproptosis: a copper-triggered modality of mitochondrial cell death. Cell Res. 2022;32(5):417-418. doi:10.1038/s41422-022-00653-7
Dunn GP, Old LJ, Schreiber RD. The immunobiology of cancer immunosurveillance and immunoediting. Immunity. 2004;21(2):137-148. doi:10.1016/j.immuni.2004.07.017
Zhang H, Wang Z, Dai Z, et al. Novel immune infiltrating cell signature based on cell pair algorithm is a prognostic marker in cancer. Front Immunol. 2021;12:694490. doi:10.3389/fimmu.2021.694490
Zhang N, Zhang H, Wang Z, et al. Immune infiltrating cells-derived risk signature based on large-scale analysis defines immune landscape and predicts immunotherapy responses in glioma tumor microenvironment. Front Immunol. 2021;12:691811. doi:10.3389/fimmu.2021.691811
Hackler J, Heller RA, Sun Q, et al. Relation of serum copper status to survival in COVID-19. Nutrients. 2021;13(6):1898. doi:10.3390/nu13061898
Mitra S, Keswani T, Ghosh N, et al. Copper induced immunotoxicity promote differential apoptotic pathways in spleen and thymus. Toxicology. 2013;306:74-84. doi:10.1016/j.tox.2013.01.001
Liao P, Wang W, Wang W, et al. CD8(+) T cells and fatty acids orchestrate tumor ferroptosis and immunity via ACSL4. Cancer Cell. 2022;40(4):365-378.e6. doi:10.1016/j.ccell.2022.02.003
Wang W, Green M, Choi JE, et al. CD8(+) T cells regulate tumour ferroptosis during cancer immunotherapy. Nature. 2019;569(7755):270-274. doi:10.1038/s41586-019-1170-y
Facciabene A, Motz GT, Coukos G. T-regulatory cells: key players in tumor immune escape and angiogenesis. Cancer Res. 2012;72(9):2162-2171. doi:10.1158/0008-5472.CAN-11-3687
Arce Vargas F, Furness AJS, Solomon I, et al. Fc-optimized anti-CD25 depletes tumor-infiltrating regulatory T cells and synergizes with PD-1 blockade to eradicate established tumors. Immunity. 2017;46(4):577-586. doi:10.1016/j.immuni.2017.03.013
Noy R, Pollard JW. Tumor-associated macrophages: from mechanisms to therapy. Immunity. 2014;41(1):49-61. doi:10.1016/j.immuni.2014.06.010
De Palma M, Lewis CE. Macrophage regulation of tumor responses to anticancer therapies. Cancer Cell. 2013;23(3):277-286. doi:10.1016/j.ccr.2013.02.013
Lupo KB, Matosevic S. CD155 immunoregulation as a target for natural killer cell immunotherapy in glioblastoma. J Hematol Oncol. 2020;13(1):76. doi:10.1186/s13045-020-00913-2
Chen DS, Mellman I. Elements of cancer immunity and the cancer-immune set point. Nature. 2017;541(7637):321-330. doi:10.1038/nature21349
Yang K, Wu Z, Zhang H, et al. Glioma targeted therapy: insight into future of molecular approaches. Mol Cancer. 2022;21(1):39. doi:10.1186/s12943-022-01513-z
Hellmann MD, Ciuleanu TE, Pluzanski A, et al. Nivolumab plus Ipilimumab in lung cancer with a high tumor mutational burden. N Engl J Med. 2018;378(22):2093-2104. doi:10.1056/NEJMoa1801946
Venteicher AS, Tirosh I, Hebert C, et al. Decoupling genetics, lineages, and microenvironment in IDH-mutant gliomas by single-cell RNA-seq. Science. 2017;355(6332):eaai8478. doi:10.1126/science.aai8478
Amankulor NM, Kim Y, Arora S, et al. Mutant IDH1 regulates the tumor-associated immune system in gliomas. Genes Dev. 2017;31(8):774-786. doi:10.1101/gad.294991.116
Bao JH, Lu WC, Duan H, et al. Identification of a novel cuproptosis-related gene signature and integrative analyses in patients with lower-grade gliomas. Front Immunol. 2022;13:933973. doi:10.3389/fimmu.2022.933973
Slawinska-Brych A, Zdzisinska B, Kandefer-Szerszen M. Fluvastatin inhibits growth and alters the malignant phenotype of the C6 glioma cell line. Pharmacol Rep. 2014;66(1):121-129. doi:10.1016/j.pharep.2014.01.002
Souberan A, Cappai J, Chocry M, et al. Inhibitor of apoptosis proteins determines glioblastoma stem-like cell fate in an oxygen-dependent manner. Stem Cells. 2019;37(6):731-742. doi:10.1002/stem.2997
Chen WS, Hong L, Wang F, Li JJ. Investigation of dacomitinib on reducing cell necrosis and enhancing cell apoptosis in C6 glioma rat model by MRI. Biosci Rep. 2019;39(3):BSR20190006. doi:10.1042/BSR20190006