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image of Using Multi-Omics Methods to Understand Gouty Arthritis

Abstract

Gouty arthritis is a common arthritic disease caused by the deposition of monosodium urate crystals in the joints and the tissues around it. The main pathogenesis of gout is the inflammation caused by the deposition of monosodium urate crystals. Omics studies help us evaluate global changes in gout during recent years, but most studies used only a single omics approach to illustrate the mechanisms of gout. In this review, we review the genomics, transcriptomics, epigenetics, proteomics, and metabolomics of gout, observing that different genes, DNA methylation, miRNAs, LncRNAs, circRNAs, proteins, and metabolites are found between hyperuricemia, acute gout arthritis, and chronic gout arthritis, and some of them are associated with disease activity, prognosis or treatment, which help us broaden our understanding of the pathogenesis and provide important clues for valuable biomarkers. To our knowledge, this is the first study that combines all omics studies from genomics to metabolomics and may serve as a reference for future studies to identify the key underlying pathways in gout.

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2025-01-13
2025-05-04

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References

  1. Roddy E. Doherty M. Gout. Epidemiology of gout. Arthritis Res. Ther. 2010 12 6 223 10.1186/ar3199 21205285
    [Google Scholar]
  2. Bodofsky S. Merriman T.R. Thomas T.J. Schlesinger N. Advances in our understanding of gout as an auto-inflammatory disease. Semin. Arthritis Rheum. 2020 50 5 1089 1100 10.1016/j.semarthrit.2020.06.015 32916560
    [Google Scholar]
  3. Mattiuzzi C. Lippi G. Recent updates on worldwide gout epidemiology. Clin. Rheumatol. 2020 39 4 1061 1063 10.1007/s10067‑019‑04868‑9 31836936
    [Google Scholar]
  4. Dehlin M. Jacobsson L. Roddy E. Global epidemiology of gout: Prevalence, incidence, treatment patterns and risk factors. Nat. Rev. Rheumatol. 2020 16 7 380 390 10.1038/s41584‑020‑0441‑1 32541923
    [Google Scholar]
  5. Singh J.A. Gaffo A. Gout epidemiology and comorbidities. Semin. Arthritis Rheum. 2020 50 3 S11 S16 10.1016/j.semarthrit.2020.04.008 32620196
    [Google Scholar]
  6. Chen G. Cheng J. Yu H. Huang X. Bao H. Qin L. Wang L. Song Y. Liu X. Peng A. Quantitative proteomics by iTRAQ-PRM based reveals the new characterization for gout. Proteome Sci. 2021 19 1 12 10.1186/s12953‑021‑00180‑0 34635120
    [Google Scholar]
  7. Bardin T. Richette P. Impact of comorbidities on gout and hyperuricaemia: An update on prevalence and treatment options. BMC Med. 2017 15 1 123 10.1186/s12916‑017‑0890‑9 28669352
    [Google Scholar]
  8. Gupta M.K. Singh J.A. Cardiovascular disease in gout and the protective effect of treatments including urate-lowering therapy. Drugs 2019 79 5 531 541 10.1007/s40265‑019‑01081‑5 30868398
    [Google Scholar]
  9. Xu Y.T. Leng Y.R. Liu M.M. Dong R.F. Bian J. Yuan L.L. Zhang J. Xia Y.Z. Kong L.Y. MicroRNA and long noncoding RNA involvement in gout and prospects for treatment. Int. Immunopharmacol. 2020 87 106842 10.1016/j.intimp.2020.106842 32738598
    [Google Scholar]
  10. Jeong H. Chang Y.S. Jeon C.H. Gout comorbidities: Results from the Korean National health and nutrition examination survey. Adv. Rheumatol. 2024 64 1 76 10.1186/s42358‑024‑00413‑8 39334489
    [Google Scholar]
  11. Cipolletta E. Nakafero G. Richette P. Avery A.J. Mamas M.A. Tata L.J. Abhishek A. Short-term risk of cardiovascular events in people newly diagnosed with gout. Arthritis Rheumatol. 2024 art.42986 Epub ahead of print 10.1002/art.42986 39279144
    [Google Scholar]
  12. Sil P. Wicklum H. Surell C. Rada B. Macrophage-derived IL-1β enhances monosodium urate crystal-triggered NET formation. Inflamm. Res. 2017 66 3 227 237 10.1007/s00011‑016‑1008‑0 27853847
    [Google Scholar]
  13. Desai J. Steiger S. Anders H.J. Molecular pathophysiology of gout. Trends Mol. Med. 2017 23 8 756 768 10.1016/j.molmed.2017.06.005 28732688
    [Google Scholar]
  14. Major T.J. Dalbeth N. Stahl E.A. Merriman T.R. An update on the genetics of hyperuricaemia and gout. Nat. Rev. Rheumatol. 2018 14 6 341 353 10.1038/s41584‑018‑0004‑x 29740155
    [Google Scholar]
  15. Tseng C.C. Liao W.T. Wong M.C. Chen C.J. Lee S.C. Yen J.H. Chang S.J. Cell lineage-specific methylome and genome alterations in gout. Aging (Albany NY) 2021 13 3 3843 3865 10.18632/aging.202353 33493135
    [Google Scholar]
  16. Phipps-Green A.J. Hollis-Moffatt J.E. Dalbeth N. Merriman M.E. Topless R. Gow P.J. Harrison A.A. Highton J. Jones P.B.B. Stamp L.K. Merriman T.R. A strong role for the ABCG2 gene in susceptibility to gout in New Zealand Pacific Island and Caucasian, but not Māori, case and control sample sets. Hum. Mol. Genet. 2010 19 24 4813 4819 10.1093/hmg/ddq412 20858603
    [Google Scholar]
  17. Merriman T.R. Dalbeth N. The genetic basis of hyperuricaemia and gout. Joint Bone Spine 2011 78 1 35 40 10.1016/j.jbspin.2010.02.027 20472486
    [Google Scholar]
  18. Reginato A.M. Mount D.B. Yang I. Choi H.K. The genetics of hyperuricaemia and gout. Nat. Rev. Rheumatol. 2012 8 10 610 621 10.1038/nrrheum.2012.144 22945592
    [Google Scholar]
  19. Köttgen A. Albrecht E. Teumer A. Vitart V. Krumsiek J. Hundertmark C. Pistis G. Ruggiero D. O’Seaghdha C.M. Haller T. Yang Q. Tanaka T. Johnson A.D. Kutalik Z. Smith A.V. Shi J. Struchalin M. Middelberg R.P.S. Brown M.J. Gaffo A.L. Pirastu N. Li G. Hayward C. Zemunik T. Huffman J. Yengo L. Zhao J.H. Demirkan A. Feitosa M.F. Liu X. Malerba G. Lopez L.M. van der Harst P. Li X. Kleber M.E. Hicks A.A. Nolte I.M. Johansson A. Murgia F. Wild S.H. Bakker S.J.L. Peden J.F. Dehghan A. Steri M. Tenesa A. Lagou V. Salo P. Mangino M. Rose L.M. Lehtimäki T. Woodward O.M. Okada Y. Tin A. Müller C. Oldmeadow C. Putku M. Czamara D. Kraft P. Frogheri L. Thun G.A. Grotevendt A. Gislason G.K. Harris T.B. Launer L.J. McArdle P. Shuldiner A.R. Boerwinkle E. Coresh J. Schmidt H. Schallert M. Martin N.G. Montgomery G.W. Kubo M. Nakamura Y. Tanaka T. Munroe P.B. Samani N.J. Jacobs D.R. Jr Liu K. D’Adamo P. Ulivi S. Rotter J.I. Psaty B.M. Vollenweider P. Waeber G. Campbell S. Devuyst O. Navarro P. Kolcic I. Hastie N. Balkau B. Froguel P. Esko T. Salumets A. Khaw K.T. Langenberg C. Wareham N.J. Isaacs A. Kraja A. Zhang Q. Wild P.S. Scott R.J. Holliday e.g. Org E. Viigimaa M. Bandinelli S. Metter J.E. Lupo A. Trabetti E. Sorice R. Döring A. Lattka E. Strauch K. Theis F. Waldenberger M. Wichmann H.E. Davies G. Gow A.J. Bruinenberg M. Stolk R.P. Kooner J.S. Zhang W. Winkelmann B.R. Boehm B.O. Lucae S. Penninx B.W. Smit J.H. Curhan G. Mudgal P. Plenge R.M. Portas L. Persico I. Kirin M. Wilson J.F. Leach I.M. van Gilst W.H. Goel A. Ongen H. Hofman A. Rivadeneira F. Uitterlinden A.G. Imboden M. von Eckardstein A. Cucca F. Nagaraja R. Piras M.G. Nauck M. Schurmann C. Budde K. Ernst F. Farrington S.M. Theodoratou E. Prokopenko I. Stumvoll M. Jula A. Perola M. Salomaa V. Shin S.Y. Spector T.D. Sala C. Ridker P.M. Kähönen M. Viikari J. Hengstenberg C. Nelson C.P. Meschia J.F. Nalls M.A. Sharma P. Singleton A.B. Kamatani N. Zeller T. Burnier M. Attia J. Laan M. Klopp N. Hillege H.L. Kloiber S. Choi H. Pirastu M. Tore S. Probst-Hensch N.M. Völzke H. Gudnason V. Parsa A. Schmidt R. Whitfield J.B. Fornage M. Gasparini P. Siscovick D.S. Polašek O. Campbell H. Rudan I. Bouatia-Naji N. Metspalu A. Loos R.J.F. van Duijn C.M. Borecki I.B. Ferrucci L. Gambaro G. Deary I.J. Wolffenbuttel B.H.R. Chambers J.C. März W. Pramstaller P.P. Snieder H. Gyllensten U. Wright A.F. Navis G. Watkins H. Witteman J.C.M. Sanna S. Schipf S. Dunlop M.G. Tönjes A. Ripatti S. Soranzo N. Toniolo D. Chasman D.I. Raitakari O. Kao W.H.L. Ciullo M. Fox C.S. Caulfield M. Bochud M. Gieger C. Genome-wide association analyses identify 18 new loci associated with serum urate concentrations. Nat. Genet. 2013 45 2 145 154 10.1038/ng.2500 23263486
    [Google Scholar]
  20. Merriman T.R. An update on the genetic architecture of hyperuricemia and gout. Arthritis Res. Ther. 2015 17 1 98 10.1186/s13075‑015‑0609‑2 25889045
    [Google Scholar]
  21. Matsuo H. Yamamoto K. Nakaoka H. Nakayama A. Sakiyama M. Chiba T. Takahashi A. Nakamura T. Nakashima H. Takada Y. Danjoh I. Shimizu S. Abe J. Kawamura Y. Terashige S. Ogata H. Tatsukawa S. Yin G. Okada R. Morita E. Naito M. Tokumasu A. Onoue H. Iwaya K. Ito T. Takada T. Inoue K. Kato Y. Nakamura Y. Sakurai Y. Suzuki H. Kanai Y. Hosoya T. Hamajima N. Inoue I. Kubo M. Ichida K. Ooyama H. Shimizu T. Shinomiya N. Genome-wide association study of clinically defined gout identifies multiple risk loci and its association with clinical subtypes. Ann. Rheum. Dis. 2016 75 4 652 659 10.1136/annrheumdis‑2014‑206191 25646370
    [Google Scholar]
  22. Merriman T. Genomic influences on hyperuricemia and Gout. Rheum. Dis. Clin. North Am. 2017 43 3 389 399 10.1016/j.rdc.2017.04.004 28711141
    [Google Scholar]
  23. Tai V. Merriman T.R. Dalbeth N. Genetic advances in gout: Potential applications in clinical practice. Curr. Opin. Rheumatol. 2019 31 2 144 151 10.1097/BOR.0000000000000571 30575597
    [Google Scholar]
  24. Lee M.G. Hsu T.C. Chen S.C. Lee Y.C. Kuo P.H. Yang J.H. Chang H.H. Lee C.C. Integrative genome-wide association studies of eQTL and GWAS data for gout disease susceptibility. Sci. Rep. 2019 9 1 4981 10.1038/s41598‑019‑41434‑4 30899057
    [Google Scholar]
  25. Kawamura Y. Nakaoka H. Nakayama A. Okada Y. Yamamoto K. Higashino T. Sakiyama M. Shimizu T. Ooyama H. Ooyama K. Nagase M. Hidaka Y. Shirahama Y. Hosomichi K. Nishida Y. Shimoshikiryo I. Hishida A. Katsuura-Kamano S. Shimizu S. Kawaguchi M. Uemura H. Ibusuki R. Hara M. Naito M. Takao M. Nakajima M. Iwasawa S. Nakashima H. Ohnaka K. Nakamura T. Stiburkova B. Merriman T.R. Nakatochi M. Ichihara S. Yokota M. Takada T. Saitoh T. Kamatani Y. Takahashi A. Arisawa K. Takezaki T. Tanaka K. Wakai K. Kubo M. Hosoya T. Ichida K. Inoue I. Shinomiya N. Matsuo H. Genome-wide association study revealed novel loci which aggravate asymptomatic hyperuricaemia into gout. Ann. Rheum. Dis. 2019 78 10 1430 1437 10.1136/annrheumdis‑2019‑215521 31289104
    [Google Scholar]
  26. Narang R.K. Topless R. Cadzow M. Gamble G. Stamp L.K. Merriman T.R. Dalbeth N. Interactions between serum urate-associated genetic variants and sex on gout risk: Analysis of the UK biobank. Arthritis Res. Ther. 2019 21 1 13 10.1186/s13075‑018‑1787‑5 30626429
    [Google Scholar]
  27. Tseng C.C. Wong M.C. Liao W.T. Chen C.J. Lee S.C. Yen J.H. Chang S.J. Systemic investigation of promoter-wide methylome and genome variations in Gout. Int. J. Mol. Sci. 2020 21 13 4702 10.3390/ijms21134702 32630231
    [Google Scholar]
  28. Tseng C.C. Chen C.J. Yen J.H. Huang H.Y. Chang J.G. Chang S.J. Liao W.T. Next-generation sequencing profiling of mitochondrial genomes in gout. Arthritis Res. Ther. 2018 20 1 137 10.1186/s13075‑018‑1637‑5 29976239
    [Google Scholar]
  29. Matsuo H. Chiba T. Nagamori S. Nakayama A. Domoto H. Phetdee K. Wiriyasermkul P. Kikuchi Y. Oda T. Nishiyama J. Nakamura T. Morimoto Y. Kamakura K. Sakurai Y. Nonoyama S. Kanai Y. Shinomiya N. Mutations in glucose transporter 9 gene SLC2A9 cause renal hypouricemia. Am. J. Hum. Genet. 2008 83 6 744 751 10.1016/j.ajhg.2008.11.001 19026395
    [Google Scholar]
  30. Woodward O.M. Köttgen A. Coresh J. Boerwinkle E. Guggino W.B. Köttgen M. Identification of a urate transporter, ABCG2, with a common functional polymorphism causing gout. Proc. Natl. Acad. Sci. USA 2009 106 25 10338 10342 10.1073/pnas.0901249106 19506252
    [Google Scholar]
  31. Preitner F. Bonny O. Laverrière A. Rotman S. Firsov D. Da Costa A. Metref S. Thorens B. Glut9 is a major regulator of urate homeostasis and its genetic inactivation induces hyperuricosuria and urate nephropathy. Proc. Natl. Acad. Sci. USA 2009 106 36 15501 15506 10.1073/pnas.0904411106 19706426
    [Google Scholar]
  32. Nakayama A. Matsuo H. Shimizu T. Ogata H. Takada Y. Nakashima H. Nakamura T. Shimizu S. Chiba T. Sakiyama M. Ushiyama C. Takada T. Inoue K. Kawai S. Hishida A. Wakai K. Hamajima N. Ichida K. Sakurai Y. Kato Y. Shimizu T. Shinomiya N. A common missense variant of monocarboxylate transporter 9 (MCT9/SLC16A9) gene is associated with renal overload gout, but not with all gout susceptibility. Hum. Cell 2013 26 4 133 136 10.1007/s13577‑013‑0073‑8 23990105
    [Google Scholar]
  33. Nakayama A. Nakaoka H. Yamamoto K. Sakiyama M. Shaukat A. Toyoda Y. Okada Y. Kamatani Y. Nakamura T. Takada T. Inoue K. Yasujima T. Yuasa H. Shirahama Y. Nakashima H. Shimizu S. Higashino T. Kawamura Y. Ogata H. Kawaguchi M. Ohkawa Y. Danjoh I. Tokumasu A. Ooyama K. Ito T. Kondo T. Wakai K. Stiburkova B. Pavelka K. Stamp L.K. Dalbeth N. Sakurai Y. Suzuki H. Hosoyamada M. Fujimori S. Yokoo T. Hosoya T. Inoue I. Takahashi A. Kubo M. Ooyama H. Shimizu T. Ichida K. Shinomiya N. Merriman T.R. Matsuo H. GWAS of clinically defined gout and subtypes identifies multiple susceptibility loci that include urate transporter genes. Ann. Rheum. Dis. 2017 76 5 869 877 10.1136/annrheumdis‑2016‑209632 27899376
    [Google Scholar]
  34. Sakiyama M. Matsuo H. Nakaoka H. Kawamura Y. Kawaguchi M. Higashino T. Nakayama A. Akashi A. Ueyama J. Kondo T. Wakai K. Sakurai Y. Yamamoto K. Ooyama H. Shinomiya N. Common variant of BCAS3 is associated with gout risk in Japanese population: The first replication study after gout GWAS in Han Chinese. BMC Med. Genet. 2018 19 1 96 10.1186/s12881‑018‑0583‑z 29879923
    [Google Scholar]
  35. Yanan F. Rui L. Xiaoying L. Shuang Z. Feng Z. Yingnan W. Tianshuang J. Xuan Y. Xiaolei Y. Litao S. Association between ACVR2A gene polymorphisms and risk of hypertensive disorders of pregnancy in the northern Chinese population. Placenta 2020 90 1 8 10.1016/j.placenta.2019.11.004 31790936
    [Google Scholar]
  36. Wang H.D. Zhang C.S. Li M.W. Lin Q. Zhang Q. Liu D.F. Ma Z.Y. Dong J. The Association of Trp64Arg polymorphism in the beta-adrenergic receptor with insulin resistance: Meta-analysis. Front. Endocrinol. (Lausanne) 2021 12 708139 10.3389/fendo.2021.708139 34512548
    [Google Scholar]
  37. Li Y. Jin L. Jiang F. Yan J. Lu Y. Yang Q. Zhang Y. Zhang H. Yu H. Zhang Y. He Z. Zhang R. Yang J. Hu C. Mutations of NRG4 Contribute to the pathogenesis of nonalcoholic fatty liver disease and related metabolic disorders. Diabetes 2021 70 10 2213 2224 10.2337/db21‑0064 34261740
    [Google Scholar]
  38. Dávalos-Rodríguez N.O. Rincón-Sánchez A.R. Madrigal Ruiz P.M. Flores-Alvarado L.J. López-Toledo S. Villafán-Bernal J.R. Castro-Juárez C.J. Guzmán-López R. Siliceo-Murrieta J.I. Ramirez-Garcia S.A. VNTR (CAG)n polymorphism of the ATXN2 gene and metabolic parameters of cardiovascular risk associated with the degree of obesity in the Amerindian population of Oaxaca. Endocrinol Diabetes Nutr 2022 69 1 15 24 10.1016/j.endien.2021.04.001 35232555
    [Google Scholar]
  39. Deaton A.M. Dubey A. Ward L.D. Dornbos P. Flannick J. Yee E. Ticau S. Noetzli L. Parker M.M. Hoffing R.A. Willis C. Plekan M.E. Holleman A.M. Hinkle G. Fitzgerald K. Vaishnaw A.K. Nioi P. Rare loss of function variants in the hepatokine gene INHBE protect from abdominal obesity. Nat. Commun. 2022 13 1 4319 10.1038/s41467‑022‑31757‑8 35896531
    [Google Scholar]
  40. Song Y. Raheel T.M. Jia A. Dai G. Liu L. Long X. He C. rs10865710 polymorphism in PPARG promoter is associated with the severity of type 2 diabetes mellitus and coronary artery disease in a Chinese population. Postgrad. Med. J. 2022 98 1164 778 787 10.1136/postgradmedj‑2021‑140354 37062988
    [Google Scholar]
  41. Lee E.S. Guo T. Srivastava R.K. Shabbir A. Ibáñez C.F. Activin receptor ALK4 promotes adipose tissue hyperplasia by suppressing differentiation of adipocyte precursors. J. Biol. Chem. 2023 299 1 102716 10.1016/j.jbc.2022.102716 36403856
    [Google Scholar]
  42. Xiong L. Liu H. Zhou C. Yang X. Huang L. Jie H. Zeng Z. Zheng X. Li W. Liu Z. Kang L. Liang Z. A novel protein encoded by circINSIG1 reprograms cholesterol metabolism by promoting the ubiquitin-dependent degradation of INSIG1 in colorectal cancer. Mol. Cancer 2023 22 1 72 10.1186/s12943‑023‑01773‑3 37087475
    [Google Scholar]
  43. Rasheed H. Phipps-Green A. Topless R. Hollis-Moffatt J.E. Hindmarsh J.H. Franklin C. Dalbeth N. Jones P.B. White D.H.N. Stamp L.K. Merriman T.R. Association of the lipoprotein receptor-related protein 2 gene with gout and non-additive interaction with alcohol consumption. Arthritis Res. Ther. 2013 15 6 R177 10.1186/ar4366 24286387
    [Google Scholar]
  44. Sakiyama M. Matsuo H. Nakaoka H. Yamamoto K. Nakayama A. Nakamura T. Kawai S. Okada R. Ooyama H. Shimizu T. Shinomiya N. Identification of rs671, a common variant of ALDH2, as a gout susceptibility locus. Sci. Rep. 2016 6 1 25360 10.1038/srep25360 27181629
    [Google Scholar]
  45. Rasheed H. Stamp L.K. Dalbeth N. Merriman T.R. Interaction of the GCKR and A1CF loci with alcohol consumption to influence the risk of gout. Arthritis Res. Ther. 2017 19 1 161 10.1186/s13075‑017‑1369‑y 28679452
    [Google Scholar]
  46. Zhong Z. Huang Y. Huang Q. Zheng S. Huang Z. Deng W. Li T. Serum metabolic profiling analysis of gout patients based on UPLC-Q-TOF/MS. Clin. Chim. Acta 2021 515 52 60 10.1016/j.cca.2020.12.028 33388309
    [Google Scholar]
  47. Hollis-Moffatt J.E. Gow P.J. Harrison A.A. Highton J. Jones P.B.B. Stamp L.K. Dalbeth N. Merriman T.R. The SLC2A9 nonsynonymous Arg265His variant and gout: Evidence for a population-specific effect on severity. Arthritis Res. Ther. 2011 13 3 R85 10.1186/ar3356 21658257
    [Google Scholar]
  48. Gao S. Luo H. Zhang H. Zuo X. Wang L. Zhu H. Using multi-omics methods to understand dermatomyositis/polymyositis. Autoimmun. Rev. 2017 16 10 1044 1048 10.1016/j.autrev.2017.07.021 28778709
    [Google Scholar]
  49. Wang Z. Zhao Y. Phipps-Green A. Liu-Bryan R. Ceponis A. Boyle D.L. Wang J. Merriman T.R. Wang W. Terkeltaub R. Differential DNA methylation of networked signaling, transcriptional, innate and adaptive immunity, and osteoclastogenesis genes and pathways in Gout. Arthritis Rheumatol. 2020 72 5 802 814 10.1002/art.41173 31738005
    [Google Scholar]
  50. Bohatá J. Horváthová V. Pavlíková M. Stibůrková B. Circulating microRNA alternations in primary hyperuricemia and gout. Arthritis Res. Ther. 2021 23 1 186 10.1186/s13075‑021‑02569‑w 34246297
    [Google Scholar]
  51. Aune T.M. Spurlock C.F. III Long non-coding RNAs in innate and adaptive immunity. Virus Res. 2016 212 146 160 10.1016/j.virusres.2015.07.003 26166759
    [Google Scholar]
  52. Ernst E.H. Nielsen J. Ipsen M.B. Villesen P. Lykke-Hartmann K. Transcriptome analysis of long non-coding rnas and genes encoding paraspeckle proteins during human ovarian follicle development. Front. Cell Dev. Biol. 2018 6 78 10.3389/fcell.2018.00078 30087896
    [Google Scholar]
  53. Yu Y. Zhang W. Li A. Chen Y. Ou Q. He Z. Zhang Y. Liu R. Yao H. Song E. Association of long noncoding RNA Biomarkers With Clinical immune subtype and prediction of immunotherapy response in patients with cancer. JAMA Netw. Open 2020 3 4 e202149 10.1001/jamanetworkopen.2020.2149 32259264
    [Google Scholar]
  54. Qing Y.F. Zheng J.X. Tang Y.P. Dai F. Dong Z.R. Zhang Q.B. LncRNAs Landscape in the patients of primary gout by microarray analysis. PLoS One 2021 16 2 e0232918 10.1371/journal.pone.0232918 33600466
    [Google Scholar]
  55. Zhou W.Y. Cai Z.R. Liu J. Wang D.S. Ju H.Q. Xu R.H. Circular RNA: Metabolism, functions and interactions with proteins. Mol. Cancer 2020 19 1 172 10.1186/s12943‑020‑01286‑3 33317550
    [Google Scholar]
  56. Dai F. Zhang Q.B. Tang Y.P. He Y.X. Yi T. Qing Y.F. Expression profile and potential function of circular rnas in peripheral blood mononuclear cells in male patients with primary Gout. Front. Genet. 2021 12 728091 10.3389/fgene.2021.728091 34764979
    [Google Scholar]
  57. Huo S. Wang H. Yan M. Xu P. Song T. Li C. Tian R. Chen X. Bao K. Xie Y. Xu P. Zhu W. Liu F. Mao W. Shao C. Urinary proteomic characteristics of hyperuricemia and their possible links with the occurrence of its concomitant diseases. ACS Omega 2021 6 14 9500 9508 10.1021/acsomega.0c06229 33869930
    [Google Scholar]
  58. Zhang Y. Zhang H. Chang D. Guo F. Pan H. Yang Y. Metabolomics approach by 1H NMR spectroscopy of serum reveals progression axes for asymptomatic hyperuricemia and gout. Arthritis Res. Ther. 2018 20 1 111 10.1186/s13075‑018‑1600‑5 29871692
    [Google Scholar]
  59. Huang Y. Xiao M. Ou J. Lv Q. Wei Q. Chen Z. Wu J. Tu L. Jiang Y. Zhang X. Qi J. Qiu M. Cao S. Gu J. Identification of the urine and serum metabolomics signature of gout. Rheumatology (Oxford) 2020 59 10 2960 2969 10.1093/rheumatology/keaa018 32134107
    [Google Scholar]
  60. Shen X. Wang C. Liang N. Liu Z. Li X. Zhu Z.J. Merriman T.R. Dalbeth N. Terkeltaub R. Li C. Yin H. Serum metabolomics identifies dysregulated pathways and potential metabolic biomarkers for hyperuricemia and Gout. Arthritis Rheumatol. 2021 73 9 1738 1748 10.1002/art.41733 33760368
    [Google Scholar]
  61. Suh Y.S. Noh H.S. Kim H.J. Cheon Y.H. Kim M. Lee H. Kim H.O. Lee S.I. Differences in clinical and dietary characteristics, serum adipokine levels, and metabolomic profiles between early- and late-onset Gout. Metabolites 2021 11 6 399 10.3390/metabo11060399 34207250
    [Google Scholar]
  62. Luo Y. Wang L. Liu X.Y. Chen X. Song Y.X. Li X.H. Jiang C. Peng A. Liu J.Y. Plasma profiling of amino acids distinguishes acute gout from asymptomatic hyperuricemia. Amino Acids 2018 50 11 1539 1548 10.1007/s00726‑018‑2627‑2 30073607
    [Google Scholar]
  63. Li Q. Wei S. Wu D. Wen C. Zhou J. Urinary metabolomics study of patients with Gout using gas chromatography-mass spectrometry. BioMed Res. Int. 2018 2018 1 9 10.1155/2018/3461572 30410926
    [Google Scholar]
  64. Shao T. Shao L. Li H. Xie Z. He Z. Wen C. Combined signature of the fecal microbiome and metabolome in patients with Gout. Front. Microbiol. 2017 8 268 10.3389/fmicb.2017.00268 28270806
    [Google Scholar]
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Using Multi-Omics Methods to Understand Gouty Arthritis
Bentham Science Publishers ; https://doi.org/10.2174/0115733971329652241119060025
/content/journals/crr/10.2174/0115733971329652241119060025
/content/journals/crr/10.2174/0115733971329652241119060025
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  • Article Type:     Review Article
Keywords: Gouty arthritis ; metabolomics ; proteomics ; epigenetics ; transcriptomics ; genetics ; biomarkers

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