دورية أكاديمية
Proteomics of tumor and serum samples from isocitrate dehydrogenase-wildtype glioblastoma patients: is the detoxification of reactive oxygen species associated with shorter survival?
العنوان: | Proteomics of tumor and serum samples from isocitrate dehydrogenase-wildtype glioblastoma patients: is the detoxification of reactive oxygen species associated with shorter survival? |
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المؤلفون: | Clavreul A; Département de Neurochirurgie, CHU d'Angers, France.; Inserm UMR 1307, CNRS UMR 6075, Université de Nantes, CRCI2NA, Université d'Angers, France., Guette C; Inserm UMR 1307, CNRS UMR 6075, Université de Nantes, CRCI2NA, Université d'Angers, France.; PROT'ICO - Plateforme Oncoprotéomique, Institut de Cancérologie de l'Ouest (ICO), Angers, France., Lasla H; Omics Data Science Unit, Institut de Cancérologie de l'Ouest (ICO), Nantes, France.; SIRIC ILIAD, Institut de Recherche en Santé, Université de Nantes, France., Rousseau A; Inserm UMR 1307, CNRS UMR 6075, Université de Nantes, CRCI2NA, Université d'Angers, France.; Département de Pathologie, CHU d'Angers, France., Blanchet O; Centre de Ressources Biologiques, BB-0033-00038, CHU d'Angers, France., Henry C; PROT'ICO - Plateforme Oncoprotéomique, Institut de Cancérologie de l'Ouest (ICO), Angers, France., Boissard A; PROT'ICO - Plateforme Oncoprotéomique, Institut de Cancérologie de l'Ouest (ICO), Angers, France., Cherel M; Département de Biologie Médicale, Centre Eugène Marquis, Unicancer, Rennes, France., Jézéquel P; Inserm UMR 1307, CNRS UMR 6075, Université de Nantes, CRCI2NA, Université d'Angers, France.; Omics Data Science Unit, Institut de Cancérologie de l'Ouest (ICO), Nantes, France.; SIRIC ILIAD, Institut de Recherche en Santé, Université de Nantes, France., Guillonneau F; Inserm UMR 1307, CNRS UMR 6075, Université de Nantes, CRCI2NA, Université d'Angers, France.; PROT'ICO - Plateforme Oncoprotéomique, Institut de Cancérologie de l'Ouest (ICO), Angers, France., Menei P; Département de Neurochirurgie, CHU d'Angers, France.; Inserm UMR 1307, CNRS UMR 6075, Université de Nantes, CRCI2NA, Université d'Angers, France., Lemée JM; Département de Neurochirurgie, CHU d'Angers, France.; Inserm UMR 1307, CNRS UMR 6075, Université de Nantes, CRCI2NA, Université d'Angers, France. |
المصدر: | Molecular oncology [Mol Oncol] 2024 May 27. Date of Electronic Publication: 2024 May 27. |
Publication Model: | Ahead of Print |
نوع المنشور: | Journal Article |
اللغة: | English |
بيانات الدورية: | Publisher: John Wiley & Sons, Inc Country of Publication: United States NLM ID: 101308230 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1878-0261 (Electronic) Linking ISSN: 15747891 NLM ISO Abbreviation: Mol Oncol Subsets: MEDLINE |
أسماء مطبوعة: | Publication: 2017- : Hoboken, New Jersey : John Wiley & Sons, Inc. Original Publication: Amsterdam : Elsevier |
مستخلص: | Proteomics has been little used for the identification of novel prognostic and/or therapeutic markers in isocitrate dehydrogenase (IDH)-wildtype glioblastoma (GB). In this study, we analyzed 50 tumor and 30 serum samples from short- and long-term survivors of IDH-wildtype GB (STS and LTS, respectively) by data-independent acquisition mass spectrometry (DIA-MS)-based proteomics, with the aim of identifying such markers. DIA-MS identified 5422 and 826 normalized proteins in tumor and serum samples, respectively, with only three tumor proteins and 26 serum proteins displaying significant differential expression between the STS and LTS groups. These dysregulated proteins were principally associated with the detoxification of reactive oxygen species (ROS). In particular, GB patients in the STS group had high serum levels of malate dehydrogenase 1 (MDH1) and ribonuclease inhibitor 1 (RNH1) and low tumor levels of fatty acid-binding protein 7 (FABP7), which may have enabled them to maintain low ROS levels, counteracting the effects of the first-line treatment with radiotherapy plus concomitant and adjuvant temozolomide. A blood score built on the levels of MDH1 and RNH1 expression was found to be an independent prognostic factor for survival based on the serum proteome data for a cohort of 96 IDH-wildtype GB patients. This study highlights the utility of circulating MDH1 and RNH1 biomarkers for determining the prognosis of patients with IDH-wildtype GB. Furthermore, the pathways driven by these biomarkers, and the tumor FABP7 pathway, may constitute promising therapeutic targets for blocking ROS detoxification to overcome resistance to chemoradiotherapy in potential GB STS. (© 2024 The Author(s). Molecular Oncology published by John Wiley & Sons Ltd on behalf of Federation of European Biochemical Societies.) |
References: | Stupp R, Hegi ME, Mason WP, van den Bent MJ, Taphoorn MJB, Janzer RC, et al. Effects of radiotherapy with concomitant and adjuvant temozolomide versus radiotherapy alone on survival in glioblastoma in a randomised phase III study: 5‐year analysis of the EORTC‐NCIC trial. Lancet Oncol. 2009;10(5):459–466. Stupp R, Mason WP, van den Bent MJ, Weller M, Fisher B, Taphoorn MJB, et al. Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N Engl J Med. 2005;352(10):987–996. Poon MTC, Sudlow CLM, Figueroa JD, Brennan PM. Longer‐term (≥ 2 years) survival in patients with glioblastoma in population‐based studies pre‐ and post‐2005: a systematic review and meta‐analysis. Sci Rep. 2020;10(1):11622. Clavreul A, Autier L, Lemée J‐M, Augereau P, Soulard G, Bauchet L, et al. Management of recurrent glioblastomas: what can we learn from the French Glioblastoma Biobank? Cancer. 2022;14(22):5510. Decraene B, Vanmechelen M, Clement P, Daisne JF, Vanden Bempt I, Sciot R, et al. Cellular and molecular features related to exceptional therapy response and extreme long‐term survival in glioblastoma. Cancer Med. 2023;12:11107–11126. https://doi.org/10.1002/cam4.5681. Louis DN, Perry A, Wesseling P, Brat DJ, Cree IA, Figarella‐Branger D, et al. The 2021 WHO classification of tumors of the central nervous system: a summary. Neuro Oncol. 2021;23:1231–1251. https://doi.org/10.1093/neuonc/noab106. Métais A, Rousseau A. Histomolecular diagnosis of glial and glioneuronal tumours. Ann Pathol. 2021;41(2):137–153. Burgenske DM, Yang J, Decker PA, Kollmeyer TM, Kosel ML, Mladek AC, et al. Molecular profiling of long‐term IDH‐wildtype glioblastoma survivors. Neuro Oncol. 2019;21(11):1458–1469. Chen D, Chen D, Cao D, Hu J, Yao Y. A signature based on survival‐related genes identifies high‐risk glioblastomas harboring immunosuppressive and aggressive ECM characteristics. Zhong Nan Da Xue Xue Bao Yi Xue Ban. 2018;43(4):368–382. Fazi B, Felsani A, Grassi L, Moles A, D'Andrea D, Toschi N, et al. The transcriptome and miRNome profiling of glioblastoma tissues and peritumoral regions highlights molecular pathways shared by tumors and surrounding areas and reveals differences between short‐term and long‐term survivors. Oncotarget. 2015;6(26):22526–22552. Gately L, McLachlan SA, Philip J, Rathi V, Dowling A. Molecular profile of long‐term survivors of glioblastoma: a scoping review of the literature. J Clin Neurosci. 2019;68:1–8. Geisenberger C, Mock A, Warta R, Rapp C, Schwager C, Korshunov A, et al. Molecular profiling of long‐term survivors identifies a subgroup of glioblastoma characterized by chromosome 19/20 co‐gain. Acta Neuropathol. 2015;130(3):419–434. Li F, Li Y, Zhang K, Li Y, He P, Liu Y, et al. FBLN4 as candidate gene associated with long‐term and short‐term survival with primary glioblastoma. Onco Targets Ther. 2017;10:387–395. Lu J, Cowperthwaite MC, Burnett MG, Shpak M. Molecular predictors of long‐term survival in glioblastoma multiforme patients. PLoS One. 2016;11(4):e0154313. Michaelsen SR, Urup T, Olsen LR, Broholm H, Lassen U, Poulsen HS. Molecular profiling of short‐term and long‐term surviving patients identifies CD34 mRNA level as prognostic for glioblastoma survival. J Neurooncol. 2018;137(3):533–542. Peng S, Dhruv H, Armstrong B, Salhia B, Legendre C, Kiefer J, et al. Integrated genomic analysis of survival outliers in glioblastoma. Neuro Oncol. 2017;19(6):833–844. Reifenberger G, Weber RG, Riehmer V, Kaulich K, Willscher E, Wirth H, et al. Molecular characterization of long‐term survivors of glioblastoma using genome‐ and transcriptome‐wide profiling. Int J Cancer. 2014;135(8):1822–1831. Biswas A, Salvucci M, Connor K, Düssmann H, Carberry S, Fichtner M, et al. Comparative analysis of deeply phenotyped GBM cohorts of “short‐term” and “long‐term” survivors. J Neurooncol. 2023;163(2):327–338. Yanovich‐Arad G, Ofek P, Yeini E, Mardamshina M, Danilevsky A, Shomron N, et al. Proteogenomics of glioblastoma associates molecular patterns with survival. Cell Rep. 2021;34(9):108787. Stetson LC, Ostrom QT, Schlatzer D, Liao P, Devine K, Waite K, et al. Proteins inform survival‐based differences in patients with glioblastoma. Neurooncol Adv. 2020;2(1):vdaa039. Ali H, Harting R, de Vries R, Ali M, Wurdinger T, Best MG. Blood‐based biomarkers for glioma in the context of gliomagenesis: a systematic review. Front Oncol. 2021;11:665235. Arora A, Patil V, Kundu P, Kondaiah P, Hegde AS, Arivazhagan A, et al. Serum biomarkers identification by iTRAQ and verification by MRM: S100A8/S100A9 levels predict tumor‐stroma involvement and prognosis in glioblastoma. Sci Rep. 2019;9(1):2749. Gautam P, Nair SC, Gupta MK, Sharma R, Polisetty RV, Uppin MS, et al. Proteins with altered levels in plasma from glioblastoma patients as revealed by iTRAQ‐based quantitative proteomic analysis. PLoS One. 2012;7(9):e46153. Gollapalli K, Ray S, Srivastava R, Renu D, Singh P, Dhali S, et al. Investigation of serum proteome alterations in human glioblastoma multiforme. Proteomics. 2012;12(14):2378–2390. Kumar DM, Thota B, Shinde SV, Prasanna KV, Hegde AS, Arivazhagan A, et al. Proteomic identification of haptoglobin α2 as a glioblastoma serum biomarker: implications in cancer cell migration and tumor growth. J Proteome Res. 2010;9(11):5557–5567. Linhares P, Carvalho B, Vaz R, Costa BM. Glioblastoma: is there any blood biomarker with true clinical relevance? Int J Mol Sci. 2020;21(16):5809. Miyauchi E, Furuta T, Ohtsuki S, Tachikawa M, Uchida Y, Sabit H, et al. Identification of blood biomarkers in glioblastoma by SWATH mass spectrometry and quantitative targeted absolute proteomics. PLoS One. 2018;13(3):e0193799. Osti D, Del Bene M, Rappa G, Santos M, Matafora V, Richichi C, et al. Clinical significance of extracellular vesicles in plasma from glioblastoma patients. Clin Cancer Res. 2019;25(1):266–276. Popescu ID, Codrici E, Albulescu L, Mihai S, Enciu A‐M, Albulescu R, et al. Potential serum biomarkers for glioblastoma diagnostic assessed by proteomic approaches. Proteome Sci. 2014;12(1):47. Krasny L, Huang PH. Data‐independent acquisition mass spectrometry (DIA‐MS) for proteomic applications in oncology. Mol Omics. 2021;17(1):29–42. Li KW, Gonzalez‐Lozano MA, Koopmans F, Smit AB. Recent developments in data independent acquisition (DIA) mass spectrometry: application of quantitative analysis of the brain proteome. Front Mol Neurosci. 2020;13:564446. Weke K, Kote S, Faktor J, Al Shboul S, Uwugiaren N, Brennan PM, et al. DIA‐MS proteome analysis of formalin‐fixed paraffin‐embedded glioblastoma tissues. Anal Chim Acta. 2022;1204:339695. Clavreul A, Soulard G, Lemée J‐M, Rigot M, Fabbro‐Peray P, Bauchet L, et al. The French glioblastoma biobank (FGB): a national clinicobiological database. J Transl Med. 2019;17(1):133. Quillien V, Lavenu A, Ducray F, Joly M‐O, Chinot O, Fina F, et al. Validation of the high‐performance of pyrosequencing for clinical MGMT testing on a cohort of glioblastoma patients from a prospective dedicated multicentric trial. Oncotarget. 2016;7(38):61916–61929. Guillon J, Petit C, Moreau M, Toutain B, Henry C, Roché H, et al. Regulation of senescence escape by TSP1 and CD47 following chemotherapy treatment. Cell Death Dis. 2019;10(3):199. Valo I, Raro P, Boissard A, Maarouf A, Jézéquel P, Verriele V, et al. OLFM4 expression in ductal carcinoma in situ and in invasive breast cancer cohorts by a SWATH‐based proteomic approach. Proteomics. 2019;19(21–22):e1800446. Zhou Y, Zhou B, Pache L, Chang M, Khodabakhshi AH, Tanaseichuk O, et al. Metascape provides a biologist‐oriented resource for the analysis of systems‐level datasets. Nat Commun. 2019;10(1):1523. Szklarczyk D, Morris JH, Cook H, Kuhn M, Wyder S, Simonovic M, et al. The STRING database in 2017: quality‐controlled protein‐protein association networks, made broadly accessible. Nucleic Acids Res. 2017;45(D1):D362–D368. Chin C‐H, Chen S‐H, Wu H‐H, Ho C‐W, Ko M‐T, Lin C‐Y. cytoHubba: identifying hub objects and sub‐networks from complex interactome. BMC Syst Biol. 2014;8(Suppl 4):S11. Bader GD, Hogue CWV. An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics. 2003;4:2. Nabi R, Su X. coxphMIC: an R package for sparse estimation of cox proportional hazards models. arXiv. 2016. https://doi.org/10.48550/arxiv.1606.07868. Kagawa Y, Umaru BA, Ariful I, Shil SK, Miyazaki H, Yamamoto Y, et al. Role of FABP7 in tumor cell signaling. Adv Biol Regul. 2019;71:206–218. Kawabe H, Nakanishi H, Asada M, Fukuhara A, Morimoto K, Takeuchi M, et al. Pilt, a novel peripheral membrane protein at tight junctions in epithelial cells. J Biol Chem. 2001;276(51):48350–48355. Yan H, Kanki H, Matsumura S, Kawano T, Nishiyama K, Sugiyama S, et al. MiRNA‐132/212 regulates tight junction stabilization in blood‐brain barrier after stroke. Cell Death Discov. 2021;7(1):380. Favero ME, Costa FF. Alpha‐hemoglobin‐stabilizing protein: an erythroid molecular chaperone. Biochem Res Int. 2011;2011:373859. Wang L‐B, Karpova A, Gritsenko MA, Kyle JE, Cao S, Li Y, et al. Proteogenomic and metabolomic characterization of human glioblastoma. Cancer Cell. 2021;39(4):509–528.e20. Hou L, Zhou H, Wang Y, Liu J, Zhang D, Li Y, et al. Identification of FABP7 as a potential biomarker for predicting prognosis and antiangiogenic drug efficacy of glioma. Dis Markers. 2022;2022:2091791. Choi W‐S, Xu X, Goruk S, Wang Y, Patel S, Chow M, et al. FABP7 facilitates uptake of docosahexaenoic acid in glioblastoma neural stem‐like cells. Nutrients. 2021;13(8):2664. Elsherbiny ME, Emara M, Godbout R. Interaction of brain fatty acid‐binding protein with the polyunsaturated fatty acid environment as a potential determinant of poor prognosis in malignant glioma. Prog Lipid Res. 2013;52(4):562–570. Xie Q, Xiao Y‐S, Jia S‐C, Zheng J‐X, Du Z‐C, Chen Y‐C, et al. FABP7 is a potential biomarker to predict response to neoadjuvant chemotherapy for breast cancer. Cancer Cell Int. 2020;20(1):562. Castelli S, De Falco P, Ciccarone F, Desideri E, Ciriolo MR. Lipid catabolism and ROS in cancer: a bidirectional liaison. Cancer. 2021;13(21):5484. Harvey KA, Xu Z, Saaddatzadeh MR, Wang H, Pollok K, Cohen‐Gadol AA, et al. Enhanced anticancer properties of lomustine in conjunction with docosahexaenoic acid in glioblastoma cell lines. J Neurosurg. 2015;122(3):547–556. Kim S, Jing K, Shin S, Jeong S, Han S‐H, Oh H, et al. ω3‐polyunsaturated fatty acids induce cell death through apoptosis and autophagy in glioblastoma cells: in vitro and in vivo. Oncol Rep. 2018;39(1):239–246. Leonardi F, Attorri L, Di Benedetto R, Di Biase A, Sanchez M, Nardini M, et al. Effect of arachidonic, eicosapentaenoic and docosahexaenoic acids on the oxidative status of C6 glioma cells. Free Radic Res. 2005;39(8):865–874. Bougnoux P, Hajjaji N, Ferrasson MN, Giraudeau B, Couet C, Le Floch O. Improving outcome of chemotherapy of metastatic breast cancer by docosahexaenoic acid: a phase II trial. Br J Cancer. 2009;101(12):1978–1985. West L, Yin Y, Pierce SR, Fang Z, Fan Y, Sun W, et al. Docosahexaenoic acid (DHA), an omega‐3 fatty acid, inhibits tumor growth and metastatic potential of ovarian cancer. Am J Cancer Res. 2020;10(12):4450–4463. Newell M, Mackey JR, Bigras G, Alvarez‐Camacho M, Goruk S, Ghosh S, et al. Comparing docosahexaenoic acid (DHA) concomitant with neoadjuvant chemotherapy versus neoadjuvant chemotherapy alone in the treatment of breast cancer (DHA WIN): protocol of a double‐blind, phase II, randomised controlled trial. BMJ Open. 2019;9(9):e030502. Hanse EA, Ruan C, Kachman M, Wang D, Lowman XH, Kelekar A. Cytosolic malate dehydrogenase activity helps support glycolysis in actively proliferating cells and cancer. Oncogene. 2017;36(27):3915–3924. Zhang B, Tornmalm J, Widengren J, Vakifahmetoglu‐Norberg H, Norberg E. Characterization of the role of the malate dehydrogenases to lung tumor cell survival. J Cancer. 2017;8(11):2088–2096. Wang Y‐P, Zhou W, Wang J, Huang X, Zuo Y, Wang T‐S, et al. Arginine methylation of MDH1 by CARM1 inhibits glutamine metabolism and suppresses pancreatic cancer. Mol Cell. 2016;64(4):673–687. Zarzuela L, Durán RV, Tomé M. Metabolism and signaling crosstalk in glioblastoma progression and therapy resistance. Mol Oncol. 2023. https://doi.org/10.1002/1878‐0261.13571. Li Z, Sun C, Qin Z. Metabolic reprogramming of cancer‐associated fibroblasts and its effect on cancer cell reprogramming. Theranostics. 2021;11(17):8322–8336. Bikfalvi A, da Costa CA, Avril T, Barnier J‐V, Bauchet L, Brisson L, et al. Challenges in glioblastoma research: focus on the tumor microenvironment. Trends Cancer. 2023;9(1):9–27. Clavreul A, Menei P. Mesenchymal stromal‐like cells in the glioma microenvironment: what are these cells? Cancer. 2020;12(9):2628. Cui XY, Fu PF, Pan DN, Zhao Y, Zhao J, Zhao BC. The antioxidant effects of ribonuclease inhibitor. Free Radic Res. 2003;37(10):1079–1085. Monti DM, Montesano Gesualdi N, Matousek J, Esposito F, D'Alessio G. The cytosolic ribonuclease inhibitor contributes to intracellular redox homeostasis. FEBS Lett. 2007;581(5):930–934. Zhu Y, Das K, Wu J, Lee MH, Tan P. RNH1 regulation of reactive oxygen species contributes to histone deacetylase inhibitor resistance in gastric cancer cells. Oncogene. 2014;33(12):1527–1537. Kun S, Duan Q, Liu G, Lu J‐M. Prognostic value of DNA repair genes based on stratification of glioblastomas. Oncotarget. 2017;8(35):58222–58230. Woo SY, Kim S. Determination of cutoff values for biomarkers in clinical studies. Precis Future Med. 2020;4(1):2–8. Chandrashekar DS, Bashel B, Balasubramanya SAH, Creighton CJ, Ponce‐Rodriguez I, Chakravarthi BVSK, et al. UALCAN: a portal for facilitating tumor subgroup gene expression and survival analyses. Neoplasia. 2017;19(8):649–658. Chandrashekar DS, Karthikeyan SK, Korla PK, Patel H, Shovon AR, Athar M, et al. UALCAN: an update to the integrated cancer data analysis platform. Neoplasia. 2022;25:18–27. Liu S, Dong L, Shi W, Zheng Z, Liu Z, Meng L, et al. Potential targets and treatments affect oxidative stress in gliomas: an overview of molecular mechanisms. Front Pharmacol. 2022;13:921070. Olivier C, Oliver L, Lalier L, Vallette FM. Drug resistance in glioblastoma: the two faces of oxidative stress. Front Mol Biosci. 2020;7:620677. Ostrowski RP, Pucko EB. Harnessing oxidative stress for anti‐glioma therapy. Neurochem Int. 2022;154:105281. Ramírez‐Expósito MJ, Martínez‐Martos JM. The delicate equilibrium between oxidants and antioxidants in brain glioma. Curr Neuropharmacol. 2019;17(4):342–351. Rinaldi M, Caffo M, Minutoli L, Marini H, Abbritti RV, Squadrito F, et al. ROS and brain gliomas: an overview of potential and innovative therapeutic strategies. Int J Mol Sci. 2016;17(6):984. Oh S, Yeom J, Cho HJ, Kim J‐H, Yoon S‐J, Kim H, et al. Integrated pharmaco‐proteogenomics defines two subgroups in isocitrate dehydrogenase wild‐type glioblastoma with prognostic and therapeutic opportunities. Nat Commun. 2020;11(1):3288. Campos‐Sandoval JA, Gómez‐García MC, Santos‐Jiménez JL, Matés JM, Alonso FJ, Márquez J. Antioxidant responses related to temozolomide resistance in glioblastoma. Neurochem Int. 2021;149:105136. Chien C‐H, Hsueh W‐T, Chuang J‐Y, Chang K‐Y. Dissecting the mechanism of temozolomide resistance and its association with the regulatory roles of intracellular reactive oxygen species in glioblastoma. J Biomed Sci. 2021;28(1):18. Murphy MP, Bayir H, Belousov V, Chang CJ, Davies KJA, Davies MJ, et al. Guidelines for measuring reactive oxygen species and oxidative damage in cells and in vivo. Nat Metab. 2022;4(6):651–662. Martin DD, Robbins ME, Spector AA, Wen BC, Hussey DH. The fatty acid composition of human gliomas differs from that found in nonmalignant brain tissue. Lipids. 1996;31(12):1283–1288. Nasrollahzadeh J, Siassi F, Doosti M, Eshraghian MR, Shokri F, Modarressi MH, et al. The influence of feeding linoleic, gamma‐linolenic and docosahexaenoic acid rich oils on rat brain tumor fatty acids composition and fatty acid binding protein 7 mRNA expression. Lipids Health Dis. 2008;7:45. Perez‐Riverol Y, Bai J, Bandla C, García‐Seisdedos D, Hewapathirana S, Kamatchinathan S, et al. The PRIDE database resources in 2022: a hub for mass spectrometry‐based proteomics evidences. Nucleic Acids Res. 2022;50(D1):D543–D552. Perez‐Riverol Y, Xu Q‐W, Wang R, Uszkoreit J, Griss J, Sanchez A, et al. PRIDE inspector toolsuite: moving toward a universal visualization tool for proteomics data standard formats and quality assessment of ProteomeXchange datasets. Mol Cell Proteomics. 2016;15(1):305–317. |
معلومات مُعتمدة: | Association en avant la vie |
فهرسة مساهمة: | Keywords: IDH‐wildtype; glioblastoma; metabolism; prognosis; proteomics; reactive oxygen species |
تواريخ الأحداث: | Date Created: 20240528 Latest Revision: 20240528 |
رمز التحديث: | 20240528 |
DOI: | 10.1002/1878-0261.13668 |
PMID: | 38803161 |
قاعدة البيانات: | MEDLINE |
تدمد: | 1878-0261 |
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DOI: | 10.1002/1878-0261.13668 |