246
Views
0
CrossRef citations to date
0
Altmetric
ORIGINAL RESEARCH

Identification of m6A-Related Biomarkers in Systemic Lupus Erythematosus: A Bioinformation-Based Analysis

, , , , , & show all
Pages 507-526 | Received 05 Nov 2023, Accepted 18 Jan 2024, Published online: 28 Jan 2024

References

  • Tsokos GC, Lo MS, Costa Reis P, et al. New insights into the immunopathogenesis of systemic lupus erythematosus. Nat Rev Rheumatol. 2016;12(12):716–730. doi:10.1038/nrrheum.2016.186
  • Crow MK. Pathogenesis of systemic lupus erythematosus: risks, mechanisms and therapeutic targets. Ann Rheum Dis. 2023;82(8):999–1014. doi:10.1136/ard-2022-223741
  • Roundtree IA, Evans ME, Pan T, et al. Dynamic RNA modifications in gene expression regulation. Cell. 2017;169(7):1187–1200. doi:10.1016/j.cell.2017.05.045
  • Cui L, Ma R, Cai J, et al. RNA modifications: importance in immune cell biology and related diseases. Signal Transduct Target Ther. 2022;7(1):334. doi:10.1038/s41392-022-01175-9
  • Zhao X, Ge L, Wang J, et al. Exploration of potential integrated models of N6-methyladenosine immunity in systemic lupus erythematosus by bioinformatic analyses. Front Immunol. 2021;12:752736. doi:10.3389/fimmu.2021.752736
  • Huang Y, Xue Q, Chang J, et al. M6A methylation modification in autoimmune diseases, a promising treatment strategy based on epigenetics. Arthritis Res Ther. 2023;25(1):189. doi:10.1186/s13075-023-03149-w
  • Li LJ, Fan YG, Leng RX, et al. Potential link between m(6)A modification and systemic lupus erythematosus. Mol Immunol. 2018;93:55–63. doi:10.1016/j.molimm.2017.11.009
  • Lv X, Liu X, Zhao M, et al. RNA methylation in systemic lupus erythematosus. Front Cell Dev Biol. 2021;9:696559. doi:10.3389/fcell.2021.696559
  • Lu S, Wei X, Zhu H, et al. m(6)A methyltransferase METTL3 programs CD4(+) T-cell activation and effector T-cell differentiation in systemic lupus erythematosus. Mol Med. 2023;29(1):46. doi:10.1186/s10020-023-00643-4
  • Zhao X, Dong R, Zhang L, et al. N6-methyladenosine-dependent modification of circGARS acts as a new player that promotes SLE progression through the NF-κB/A20 axis. Arthritis Res Ther. 2022;24(1):37. doi:10.1186/s13075-022-02732-x
  • Wu J, Deng L-J, Xia Y-R, et al. Involvement of N6-methyladenosine modifications of long noncoding RNAs in systemic lupus erythematosus. Mol Immunol. 2022;143:77–84. doi:10.1016/j.molimm.2022.01.006
  • Zhou W, Wang X, Chang J, et al. The molecular structure and biological functions of RNA methylation, with special emphasis on the roles of RNA methylation in autoimmune diseases. Crit Rev Clin Lab Sci. 2022;59(3):203–218. doi:10.1080/10408363.2021.2002256
  • Barrett T, Wilhite SE, Ledoux P, et al. NCBI GEO: archive for functional genomics data sets--update. Nucleic Acids Res. 2013;41(Database issue):D991–D995. doi:10.1093/nar/gks1193
  • Law CW, Chen Y, Shi W, et al. voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. 2014;15(2):R29. doi:10.1186/gb-2014-15-2-r29
  • Kolde R, Laur S, Adler P, et al. Robust rank aggregation for gene list integration and meta-analysis. Bioinformatics. 2012;28(4):573–580. doi:10.1093/bioinformatics/btr709
  • Leek JT, Johnson WE, Parker HS, et al. The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics. 2012;28(6):882–883. doi:10.1093/bioinformatics/bts034
  • Subramanian A, Tamayo P, Mootha VK, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A. 2005;102(43):15545–15550. doi:10.1073/pnas.0506580102
  • Liberzon A, Birger C, Thorvaldsdóttir H, et al. The Molecular Signatures Database (MSigDB) hallmark gene set collection. Cell Systems. 2015;1(6):417–425. doi:10.1016/j.cels.2015.12.004
  • Ritchie ME, Phipson B, Wu D, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43(7):e47. doi:10.1093/nar/gkv007
  • Kanehisa M, Goto S. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 2000;28(1):27–30. doi:10.1093/nar/28.1.27
  • Liaw A, Wiener MC. Classification and regression by randomforest. R News. 2002;2(3):18–22.
  • Harrell JFE. Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis. New York: Springer; 2001.
  • Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinf. 2008;9(1):559. doi:10.1186/1471-2105-9-559
  • Langfelder P, Horvath S. Fast R functions for robust correlations and hierarchical clustering. J Stat Softw. 2012;46(11). doi:10.18637/jss.v046.i11
  • Hänzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinf. 2013;14(1):7. doi:10.1186/1471-2105-14-7
  • Hemani G, Zheng J, Elsworth B, et al. The MR-Base platform supports systematic causal inference across the human phenome. Elife. 2018;7:e34408.
  • Robin X, Turck N, Hainard A, et al. pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinf. 2011;12(1):77. doi:10.1186/1471-2105-12-77
  • Wu T, Hu E, Xu S, et al. clusterProfiler 4.0: a universal enrichment tool for interpreting omics data. Innovation. 2021;2(3):100141. doi:10.1016/j.xinn.2021.100141
  • Li HB, Tong J, Zhu S, et al. m(6)A mRNA methylation controls T cell homeostasis by targeting the IL-7/STAT5/SOCS pathways. Nature. 2017;548(7667):338–342. doi:10.1038/nature23450
  • Dong L, Chen C, Zhang Y, et al. The loss of RNA N(6)-adenosine methyltransferase Mettl14 in tumor-associated macrophages promotes CD8(+) T cell dysfunction and tumor growth. Cancer Cell. 2021;39(7):945–57.e10. doi:10.1016/j.ccell.2021.04.016
  • Ito-Kureha T, Leoni C, Borland K, et al. The function of Wtap in N(6)-adenosine methylation of mRNAs controls T cell receptor signaling and survival of T cells. Nat Immunol. 2022;23(8):1208–1221. doi:10.1038/s41590-022-01268-1
  • Zheng Z, Zhang L, Cui XL, et al. Control of early B cell development by the RNA N(6)-methyladenosine methylation. Cell Rep. 2020;31(13):107819. doi:10.1016/j.celrep.2020.107819
  • Han D, Liu J, Chen C, et al. Anti-tumour immunity controlled through mRNA m(6)A methylation and YTHDF1 in dendritic cells. Nature. 2019;566(7743):270–274. doi:10.1038/s41586-019-0916-x
  • Wang H, Hu X, Huang M, et al. Mettl3-mediated mRNA m(6)A methylation promotes dendritic cell activation. Nat Commun. 2019;10(1):1898. doi:10.1038/s41467-019-09903-6
  • Zhou MJ, Liu W, Zhang JY, et al. RNA m(6)A modification in immunocytes and DNA repair: the biological functions and prospects in clinical application. Front Cell Develop Biol. 2021;9:13. doi:10.3389/fcell.2021.794754
  • Sciascia S, Roccatello D, Radin M, et al. Differentiating between UCTD and early-stage SLE: from definitions to clinical approach. Nat Rev Rheumatol. 2022;18(1):9–21. doi:10.1038/s41584-021-00710-2
  • Zhao M, Zhou Y, Zhu B, et al. IFI44L promoter methylation as a blood biomarker for systemic lupus erythematosus. Ann Rheum Dis. 2016;75(11):1998–2006. doi:10.1136/annrheumdis-2015-208410
  • Zhao X, Zhang L, Wang J, et al. Identification of key biomarkers and immune infiltration in systemic lupus erythematosus by integrated bioinformatics analysis. J Transl Med. 2021;19(1):35. doi:10.1186/s12967-020-02698-x
  • Shen L, Lan L, Zhu T, et al. Identification and validation of IFI44 as key biomarker in lupus nephritis. Front Med Lausanne. 2021;8:762848. doi:10.3389/fmed.2021.762848
  • Zheng Q, Wang D, Lin R, et al. IFI44 is an immune evasion biomarker for SARS-CoV-2 and Staphylococcus aureus infection in patients with RA. Front Immunol. 2022;13:1013322. doi:10.3389/fimmu.2022.1013322
  • Jiang Z, Shao M, Dai X, et al. Identification of diagnostic biomarkers in systemic lupus erythematosus based on bioinformatics analysis and machine learning. Front Genet. 2022;13:865559. doi:10.3389/fgene.2022.865559
  • Wang Y, Huang Z, Xiao Y, et al. The shared biomarkers and pathways of systemic lupus erythematosus and metabolic syndrome analyzed by bioinformatics combining machine learning algorithm and single-cell sequencing analysis. Front Immunol. 2022;13:1015882. doi:10.3389/fimmu.2022.1015882
  • Liu C, Zhou Y, Zhou Y, et al. Identification of crucial genes for predicting the risk of atherosclerosis with system lupus erythematosus based on comprehensive bioinformatics analysis and machine learning. Comput Biol Med. 2023;152:106388. doi:10.1016/j.compbiomed.2022.106388
  • Li H, Zhang X, Shang J, et al. Identification of NETs-related biomarkers and molecular clusters in systemic lupus erythematosus. Front Immunol. 2023;14:1150828. doi:10.3389/fimmu.2023.1150828
  • Kapadia M, Zhao H, Ma D, et al. Sustained immunosuppression alters olfactory function in the MRL model of CNS lupus. J Neuroimmune Pharmacol. 2017;12(3):555–564. doi:10.1007/s11481-017-9745-6
  • Bombini MF, Peres FA, Lapa AT, et al. Olfactory function in systemic lupus erythematosus and systemic sclerosis. A longitudinal study and review of the literature. Autoimmun Rev. 2018;17(4):405–412. doi:10.1016/j.autrev.2018.02.002
  • Bao X, Zhang Y, Li H, et al. RM2Target: a comprehensive database for targets of writers, erasers and readers of RNA modifications. Nucleic Acids Res. 2023;51(D1):D269–D279. doi:10.1093/nar/gkac945
  • Faber-Elmann A, Sthoeger Z, Tcherniack A, et al. Activity of matrix metalloproteinase-9 is elevated in sera of patients with systemic lupus erythematosus. Clin Exp Immunol. 2002;127(2):393–398. doi:10.1046/j.1365-2249.2002.01758.x
  • de Almeida LGN, Thode H, Eslambolchi Y, et al. Matrix metalloproteinases: from molecular mechanisms to physiology, pathophysiology, and pharmacology. Pharmacol Rev. 2022;74(3):712–768. doi:10.1124/pharmrev.121.000349
  • Ugarte-Berzal E, Boon L, Martens E, et al. MMP-9/gelatinase B degrades immune complexes in systemic lupus erythematosus. Front Immunol. 2019;10:538. doi:10.3389/fimmu.2019.00538
  • Carmona-Rivera C, Zhao W, Yalavarthi S, et al. Neutrophil extracellular traps induce endothelial dysfunction in systemic lupus erythematosus through the activation of matrix metalloproteinase-2. Ann Rheum Dis. 2015;74(7):1417–1424. doi:10.1136/annrheumdis-2013-204837
  • Phillips TM, Fadia M, Lea-Henry TN, et al. MMP2 and MMP9 associate with crescentic glomerulonephritis. Clin Kidney J. 2017;10(2):215–220. doi:10.1093/ckj/sfw111
  • Jiang Z, Sui T, Wang B. Relationships between MMP-2, MMP-9, TIMP-1 and TIMP-2 levels and their pathogenesis in patients with lupus nephritis. Rheumatol Int. 2010;30(9):1219–1226. doi:10.1007/s00296-009-1135-9
  • Beroun A, Mitra S, Michaluk P, et al. MMPs in learning and memory and neuropsychiatric disorders. Cell Mol Life Sci. 2019;76(16):3207–3228. doi:10.1007/s00018-019-03180-8
  • Vafadari B, Salamian A, Kaczmarek L. MMP-9 in translation: from molecule to brain physiology, pathology, and therapy. J Neurochem. 2016;139(Suppl 2):91–114. doi:10.1111/jnc.13415
  • Turner RJ, Sharp FR. Implications of MMP9 for blood brain barrier disruption and hemorrhagic transformation following ischemic stroke. Front Cell Neurosci. 2016;10:56. doi:10.3389/fncel.2016.00056
  • Yafasova A, Fosbøl EL, Schou M, et al. Long-term cardiovascular outcomes in systemic lupus erythematosus. J Am Coll Cardiol. 2021;77(14):1717–1727. doi:10.1016/j.jacc.2021.02.029
  • Gao N, Kong M, Li X, et al. Systemic lupus erythematosus and cardiovascular disease: a Mendelian randomization study. Front Immunol. 2022;13:908831. doi:10.3389/fimmu.2022.908831
  • Tektonidou MG, Wang Z, Ward MM. Brief report: trends in hospitalizations due to acute coronary syndromes and stroke in patients with systemic lupus erythematosus, 1996 to 2012. Arthritis Rheumatol. 2016;68(11):2680–2685. doi:10.1002/art.39758
  • Shaban A, Leira EC. Neurological complications in patients with systemic lupus erythematosus. Curr Neurol Neurosci Rep. 2019;19(12):97. doi:10.1007/s11910-019-1012-1
  • Di Biase L, Bonura A, Pecoraro PM, et al. Unlocking the potential of stroke blood biomarkers: early diagnosis, ischemic vs. haemorrhagic differentiation and haemorrhagic transformation risk: a comprehensive review. Int J Mol Sci. 2023;24(14):11545. doi:10.3390/ijms241411545
  • Chaturvedi M, Kaczmarek L. MMP-9 inhibition: a therapeutic strategy in ischemic stroke. Mol Neurobiol. 2014;49(1):563–573. doi:10.1007/s12035-013-8538-z
  • Jickling GC, Liu D, Stamova B, et al. Hemorrhagic transformation after ischemic stroke in animals and humans. J Cereb Blood Flow Metab. 2014;34(2):185–199. doi:10.1038/jcbfm.2013.203
  • Wen D, Du X, Nie S-P, et al. Association between matrix metalloproteinase family gene polymorphisms and ischemic stroke: a meta-analysis. Mol Neurobiol. 2014;50(3):979–985. doi:10.1007/s12035-014-8687-8
  • Misra S, Talwar P, Kumar A, et al. Association between matrix metalloproteinase family gene polymorphisms and risk of ischemic stroke: a systematic review and meta-analysis of 29 studies. Gene. 2018;672:180–194. doi:10.1016/j.gene.2018.06.027
  • Buraczynska K, Kurzepa J, Ksiazek A, et al. Matrix Metalloproteinase-9 (MMP-9) gene polymorphism in stroke patients. Neuromolecular Med. 2015;17(4):385–390. doi:10.1007/s12017-015-8367-5
  • Fan D, Zheng C, Wu W, et al. MMP9 SNP and MMP SNP-SNP interactions increase the risk for ischemic stroke in the Han Hakka population. Brain Behav. 2022;12(2):e2473.
  • Zhong C, Yang J, Xu T, et al. Serum matrix metalloproteinase-9 levels and prognosis of acute ischemic stroke. Neurology. 2017;89(8):805–812. doi:10.1212/WNL.0000000000004257
  • Zhong C, Bu X, Xu T, et al. Serum matrix metalloproteinase-9 and cognitive impairment after acute ischemic stroke. J Am Heart Assoc. 2018;7(1):e007776.
  • Zhang M, Meng X, Pan Y, et al. Predictive values of baseline matrix metalloproteinase 9 levels in peripheral blood on 3-month outcomes of high-risk patients with minor stroke or transient ischemic attack. Eur J Neurol. 2022;29(10):2976–2986. doi:10.1111/ene.15342
  • Wang J, Liu R, Hasan MN, et al. Role of SPAK-NKCC1 signaling cascade in the choroid plexus blood-CSF barrier damage after stroke. J Neuroinflammation. 2022;19(1):91. doi:10.1186/s12974-022-02456-4
  • Emdin CA, Khera AV, Kathiresan S. Mendelian randomization. JAMA. 2017;318(19):1925–1926. doi:10.1001/jama.2017.17219
  • Hartwig FP, Davies NM, Hemani G, et al. Two-sample Mendelian randomization: avoiding the downsides of a powerful, widely applicable but potentially fallible technique. Int J Epidemiol. 2016;45(6):1717–1726. doi:10.1093/ije/dyx028