Abstract
Glioma progression seriously correlates to the epigenetic context. This study aims to identify glioma subtypes by clustering analysis of patients using the multi-omics data of N6-methyladenosine (m6A) methylation regulators and to construct a risk signature for investigating the role of m6A methylation regulators in the prognosis of glioma. Multi-omics data of glioma and normal control tissues were obtained through The Cancer Genome Atlas (TCGA) database. The clustering analysis of multi-omics data of patients was conducted using the R package iClusterPlus software. The risk model was constructed by univariate and multivariate Cox analysis, and the glioma expression data and related clinical data were obtained by Chinese Glioma Genome Atlas (CGGA) datasets to verify the risk model. By analyzing the glioma data in TCGA, we found that the risk signature could be constructed according to the eight genes with m6A methylation modification function, including ALKBH5, HNRNPA2B1, IGF2BP2, IGF2BP3, RBM15, WTAP, YTHDF1, and YTHDF2. Meanwhile, we found that IGF2BP2 and IGF2BP3 were highly expressed in glioma subtypes with high-risk scores and closely related to the prognosis of glioma patients. m6A methylation regulators, especially IGF2BP2 and IGF2BP3, play important roles in the malignant progression of glioma. The risk signature constructed by eight m6A methylation regulators can predict the prognosis of glioma. IGF2BP2 and IGF2BP3 may be the key regulatory factors of m6A methylation regulators involved in the occurrence and development of glioma, and can serve as molecular markers for the prognosis of glioma.
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29 November 2021
A Correction to this paper has been published: https://doi.org/10.1007/s10571-021-01171-7
References
Allahverdi A, Arefian E, Soleimani M, Ai J, Nahanmoghaddam N, Yousefi-Ahmadipour A, Ebrahimi-Barough S (2020) MicroRNA-4731–5p delivered by AD-mesenchymal stem cells induces cell cycle arrest and apoptosis in glioblastoma. J Cell Physiol. https://doi.org/10.1002/jcp.29472
Bell JL, Wachter K, Muhleck B, Pazaitis N, Kohn M, Lederer M, Huttelmaier S (2013) Insulin-like growth factor 2 mRNA-binding proteins (IGF2BPs): post-transcriptional drivers of cancer progression? Cell Mol Life Sci 70:2657–2675. https://doi.org/10.1007/s00018-012-1186-z
Bhat K et al (2020) The dopamine receptor antagonist trifluoperazine prevents phenotype conversion and improves survival in mouse models of glioblastoma. Proc Natl Acad Sci U S A 117:11085–11096. https://doi.org/10.1073/pnas.1920154117
Chai RC et al (2019) m(6)A RNA methylation regulators contribute to malignant progression and have clinical prognostic impact in gliomas. Aging (albany NY) 11:1204–1225. https://doi.org/10.18632/aging.101829
Cheng J, Meng J, Zhu L, Peng Y (2020) Exosomal noncoding RNAs in Glioma: biological functions and potential clinical applications. Mol Cancer 19:66. https://doi.org/10.1186/s12943-020-01189-3
Cheng YC, Tsai WC, Sung YC, Chang HH, Chen Y (2018) Interference with PSMB4 expression exerts an anti-tumor effect by decreasing the invasion and proliferation of human glioblastoma cells. Cell Physiol Biochem 45:819–831. https://doi.org/10.1159/000487174
Cote DJ et al (2019) Glioma incidence and survival variations by county-level socioeconomic measures. Cancer 125:3390–3400. https://doi.org/10.1002/cncr.32328
Deng J et al (2016) Effects of hnRNP A2/B1 knockdown on inhibition of glioblastoma cell invasion, growth and survival. Mol Neurobiol 53:1132–1144. https://doi.org/10.1007/s12035-014-9080-3
Deng X, Jiang Q, Liu Z, Chen W (2020) Clinical significance of an m6A reader gene, IGF2BP2, in head and neck squamous cell carcinoma. Front Mol Biosci 7:68. https://doi.org/10.3389/fmolb.2020.00068
Ding L, Wang L, Guo F (2017) microRNA188 acts as a tumour suppressor in glioma by directly targeting the IGF2BP2 gene. Mol Med Rep 16:7124–7130. https://doi.org/10.3892/mmr.2017.7433
Dong Z, Cui H (2020) The emerging roles of RNA modifications in glioblastoma. Cancers (basel). https://doi.org/10.3390/cancers12030736
Galardi S, Michienzi A, Ciafre SA (2020) Insights into the regulatory role of m(6)A epitranscriptome in glioblastoma. Int J Mol Sci. https://doi.org/10.3390/ijms21082816
Geng Y et al (2020) Identification of m6A-related genes and m6A RNA methylation regulators in pancreatic cancer and their association with survival. Ann Transl Med 8:387. https://doi.org/10.21037/atm.2020.03.98
Holzinger A, Malle B, Saranti A, Pfeifer B (2021) Towards multi-modal causability with graph neural networks enabling information fusion for explainable AI. Information Fusion 71:28–37. https://doi.org/10.1016/j.inffus.2021.01.008
Huang R et al (2020) Identification of an ATP metabolism-related signature associated with prognosis and immune microenvironment in gliomas. Cancer Sci 111:2325–2335. https://doi.org/10.1111/cas.14484
Jean-Quartier C et al (2021) Mutation-based clustering and classification analysis reveals distinctive age groups and age-related biomarkers for glioma. BMC Med Inform Decis Mak 21:77. https://doi.org/10.1186/s12911-021-01420-1
Jin P, Huang Y, Zhu P, Zou Y, Shao T, Wang O (2018) CircRNA circHIPK3 serves as a prognostic marker to promote glioma progression by regulating miR-654/IGF2BP3 signaling. Biochem Biophys Res Commun 503:1570–1574. https://doi.org/10.1016/j.bbrc.2018.07.081
Larsson I, Uhlen M, Zhang C, Mardinoglu A (2020) Genome-scale metabolic modeling of glioblastoma reveals promising targets for drug development. Front Genet 11:381. https://doi.org/10.3389/fgene.2020.00381
Lawrie TA et al (2019) Long-term neurocognitive and other side effects of radiotherapy, with or without chemotherapy, for glioma. Cochrane Database Syst Rev 8:CD013047. https://doi.org/10.1002/14651858.CD013047.pub2
Li F, Zhang C, Zhang G (2019) m6A RNA methylation controls proliferation of human glioma cells by influencing cell apoptosis. Cytogenet Genome Res 159:119–125. https://doi.org/10.1159/000499062
Lin S et al (2020) Prognosis analysis and validation of m(6)A signature and tumor immune microenvironment in glioma. Front Oncol 10:541401. https://doi.org/10.3389/fonc.2020.541401
Liu ZX, Li LM, Sun HL, Liu SM (2018) Link between m6A modification and cancers. Front Bioeng Biotechnol 6:89. https://doi.org/10.3389/fbioe.2018.00089
Mo Q, Shen R, Guo C, Vannucci M, Chan KS, Hilsenbeck SG (2018) A fully Bayesian latent variable model for integrative clustering analysis of multi-type omics data. Biostatistics 19:71–86. https://doi.org/10.1093/biostatistics/kxx017
Mu Q et al (2015) Imp2 regulates GBM progression by activating IGF2/PI3K/Akt pathway. Cancer Biol Ther 16:623–633. https://doi.org/10.1080/15384047.2015.1019185
Pan Y, Xiao K, Li Y, Li Y, Liu Q (2021) RNA N6-Methyladenosine regulator-mediated methylation modifications pattern and immune infiltration features in glioblastoma. Front Oncol 11:632934. https://doi.org/10.3389/fonc.2021.632934
Robinson MD, McCarthy DJ, Smyth GK (2010) edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26:139–140. https://doi.org/10.1093/bioinformatics/btp616
Shannon P et al (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13:2498–2504. https://doi.org/10.1101/gr.1239303
Subramanian I, Verma S, Kumar S, Jere A, Anamika K (2020) Multi-omics data integration, interpretation, and its application. Bioinform Biol Insights 14:1177932219899051. https://doi.org/10.1177/1177932219899051
Szklarczyk D et al (2019) STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res 47:D607–D613. https://doi.org/10.1093/nar/gky1131
Wang S et al (2017) Gene expression meta-analysis in diffuse low-grade glioma and the corresponding histological subtypes. Sci Rep 7:11741. https://doi.org/10.1038/s41598-017-12087-y
Wang S et al (2019) Integrated analysis of 34 microarray datasets reveals CBX3 as a diagnostic and prognostic biomarker in glioblastoma. J Transl Med 17:179. https://doi.org/10.1186/s12967-019-1930-3
Wang W, Li J, Lin F, Guo J, Zhao J (2020a) Identification of N(6)-methyladenosine-related lncRNAs for patients with primary glioblastoma. Neurosurg Rev. https://doi.org/10.1007/s10143-020-01238-x
Wang Z, Tang W, Yuan J, Qiang B, Han W, Peng X (2020b) Integrated analysis of rna-binding proteins in glioma. Cancers (basel). https://doi.org/10.3390/cancers12040892
Xu X, Yu Y, Zong K, Lv P, Gu Y (2019) Up-regulation of IGF2BP2 by multiple mechanisms in pancreatic cancer promotes cancer proliferation by activating the PI3K/Akt signaling pathway. J Exp Clin Cancer Res 38:497. https://doi.org/10.1186/s13046-019-1470-y
Yang L et al (2020a) Melatonin restores the pluripotency of long-term-cultured embryonic stem cells through melatonin receptor-dependent m6A RNA regulation. J Pineal Res. https://doi.org/10.1111/jpi.12669
Yang Z et al (2020b) Albumin-based nanotheranostic probe with hypoxia alleviating potentiates synchronous multimodal imaging and phototherapy for glioma. ACS Nano 14:6191–6212. https://doi.org/10.1021/acsnano.0c02249
Yu G, Wang LG, Han Y, He QY (2012) clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16:284–287. https://doi.org/10.1089/omi.2011.0118
Zhang S et al (2017) m(6)A Demethylase ALKBH5 maintains tumorigenicity of glioblastoma stem-like cells by sustaining FOXM1 expression and cell proliferation program. Cancer Cell 31(591–606):e596. https://doi.org/10.1016/j.ccell.2017.02.013
Zhao WJ, Ou GY, Lin WW (2021) Integrative analysis of neuregulin family members-related tumor microenvironment for predicting the prognosis in gliomas. Front Immunol 12:682415. https://doi.org/10.3389/fimmu.2021.682415
Zheng J et al (2021) Identification of critical m(6)A RNA methylation regulators with prognostic value in lower-grade glioma. Biomed Res Int 2021:9959212. https://doi.org/10.1155/2021/9959212
Zhou J et al (2019) Gene signatures and prognostic values of m6A regulators in clear cell renal cell carcinoma - a retrospective study using TCGA database. Aging (albany NY) 11:1633–1647. https://doi.org/10.18632/aging.101856
Zou Y et al (2020) Single siRNA nanocapsules for effective siRNA brain delivery and glioblastoma treatment. Adv Mater 32:e2000416. https://doi.org/10.1002/adma.202000416
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Study design SG and YH; Research performed YS and LZ; Data collection and analysis SG; Article writing YH and YS; Review the data and article SG, YH, YS and LZ.
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Guan, S., He, Y., Su, Y. et al. A Risk Signature Consisting of Eight m6A Methylation Regulators Predicts the Prognosis of Glioma. Cell Mol Neurobiol 42, 2733–2743 (2022). https://doi.org/10.1007/s10571-021-01135-x
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DOI: https://doi.org/10.1007/s10571-021-01135-x