Identification of copper metabolism-related markers in Parkinson's disease.

Jie Lin, Guifeng Zhang, Bo Lou, Yi Sun, Xiaodong Jia, Meidan Wang, Jing Zhou, Zhangyong Xia
Author Information
  1. Jie Lin: Department of Joint Laboratory for Translational Medicine Research, Liaocheng People's Hospital, Liaocheng, P.R. China. ORCID
  2. Guifeng Zhang: Department of Neurology, Liaocheng People's Hospital and Liaocheng Hospital Affiliated to Shandong First Medical University, Liaocheng, P.R. China. ORCID
  3. Bo Lou: Department of Neurology, The Third People's Hospital of Liaocheng, Liaocheng, P.R. China. ORCID
  4. Yi Sun: Department of Sports Medicine, Peking University Shenzhen Hospital, Shenzhen, P.R. China. ORCID
  5. Xiaodong Jia: Department of Joint Laboratory for Translational Medicine Research, Liaocheng People's Hospital, Liaocheng, P.R. China. ORCID
  6. Meidan Wang: Faculty of Biology, University of Freiburg, Freiburg, Germany. ORCID
  7. Jing Zhou: Department of Neurology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, P.R. China. ORCID
  8. Zhangyong Xia: Department of Neurology, Liaocheng People's Hospital, Shandong University, Jinan, P.R. China. ORCID

Abstract

OBJECTIVES: This study aimed to identify key genes related to copper metabolism in Parkinson's disease (PD), providing insight into their roles in disease progression.
METHODS: Using bioinformatic analyses, the study identified hub genes related to copper metabolism in PD patients. Differentially expressed genes (DEGs) were identified using the limma package, and copper-metabolism-related genes (CMRGs) were sourced from the Genecard database. Immune cell-related genes were derived through immune infiltration and Weighted Gene Co-expression Network Analysis (WGCNA). Hub genes were pinpointed by integrating DEGs, CMRGs, and immune cell-related genes. Functional analyses included Receiver Operating Characteristic (ROC) analysis, Ingenuity Pathway Analysis (IPA), and networks for miRNA-mRNA-transcription factor (TF), Competitive Endogenous RNA (ceRNA), and hub gene-drug interactions. Validation was performed in cerebrospinal fluid (CSF) samples from PD patients, while in vitro experiments utilized GBE1- overexpressing SH-SY5Y cells to examine cell proliferation, migration, and viability.
RESULTS: Nine hub genes (HPRT1, GLS, SNCA, MDH1, GBE1, DDC, STXBP1, ACHE, and AGTR1) were identified from 753 CMRGs, 416 DEGs, and 951 immune cell-related genes. ROC analysis showed high predictive accuracy for PD, and principal component analysis (PCA) effectively distinguished PD patients from controls. IPA identified 20 significant pathways, and various networks highlighted miRNA, TF, and drug interactions with the hub genes. Hub gene expression was validated in PD CSF samples. GBE1-overexpressing cells displayed enhanced proliferation, migration, and viability.
CONCLUSIONS: The study identified nine copper metabolism-related genes as potential therapeutic targets in PD, highlighting their relevance in PD pathology and possible treatment pathways.

Keywords

References

  1. J Neurol. 2022 Jun;269(6):2854-2861 [PMID: 34999962]
  2. PLoS One. 2013 Dec 10;8(12):e83060 [PMID: 24340079]
  3. OMICS. 2012 May;16(5):284-7 [PMID: 22455463]
  4. J Parkinsons Dis. 2016 Apr 2;6(2):423-31 [PMID: 27061063]
  5. Restor Neurol Neurosci. 2016 Nov 22;34(6):877-895 [PMID: 27858721]
  6. Curr Alzheimer Res. 2012 Jul;9(6):746-58 [PMID: 21875407]
  7. Mol Aspects Med. 2015 Dec;46:85-100 [PMID: 26278982]
  8. Neurobiol Dis. 2006 Oct;24(1):183-93 [PMID: 16901708]
  9. Behav Brain Res. 2015 Aug 1;289:19-28 [PMID: 25907749]
  10. Front Aging Neurosci. 2021 Feb 09;13:605970 [PMID: 33633562]
  11. Lancet Neurol. 2006 Mar;5(3):235-45 [PMID: 16488379]
  12. Oxid Med Cell Longev. 2014;2014:147251 [PMID: 24672633]
  13. Sci Rep. 2016 May 17;6:25592 [PMID: 27185277]
  14. Neurology. 2019 Jul 16;93(3):114-123 [PMID: 31221716]
  15. Clin Neurol Neurosurg. 2020 Jan;188:105587 [PMID: 31733593]
  16. Nutr Rev. 2002 Dec;60(12):410-3 [PMID: 12521146]
  17. J Invest Dermatol. 2017 Feb;137(2):e11-e16 [PMID: 28110712]
  18. Brain. 2006 May;129(Pt 5):1201-17 [PMID: 16549399]
  19. Front Neurosci. 2019 Sep 24;13:1028 [PMID: 31611767]
  20. Neurobiol Aging. 2014 Apr;35(4):858-66 [PMID: 24176624]
  21. Science. 2003 Oct 31;302(5646):819-22 [PMID: 14593166]
  22. Mov Disord. 1993;8(1):87-92 [PMID: 8419812]
  23. Redox Biol. 2021 Jan;38:101795 [PMID: 33232911]
  24. Toxicol Lett. 2023 Sep 15;387:14-27 [PMID: 37717680]
  25. Int J Genomics. 2022 Mar 20;2022:9305081 [PMID: 35359580]
  26. Aging Dis. 2018 Aug 1;9(4):716-728 [PMID: 30090659]
  27. Genomics. 1992 Jul;13(3):788-96 [PMID: 1639405]
  28. Neuroscience. 2014 May 16;267:114-21 [PMID: 24613722]
  29. Bioorg Med Chem. 2020 Oct 15;28(20):115698 [PMID: 33069080]
  30. Orphanet J Rare Dis. 2007 Dec 08;2:48 [PMID: 18067674]
  31. Cells. 2023 Mar 25;12(7): [PMID: 37048085]
  32. Nucleic Acids Res. 2015 Apr 20;43(7):e47 [PMID: 25605792]
  33. J Pharmacol Sci. 2020 Nov;144(3):151-164 [PMID: 32807662]
  34. Lancet. 1987 Aug 1;2(8553):238-41 [PMID: 2886715]
  35. Carcinogenesis. 1993 Jul;14(7):1303-11 [PMID: 8392444]
  36. FEBS Open Bio. 2021 Sep;11(9):2525-2540 [PMID: 34231338]
  37. Can J Neurol Sci. 1990 Aug;17(3):286-91 [PMID: 2207882]
  38. Neurobiol Aging. 2003 Mar-Apr;24(2):197-211 [PMID: 12498954]
  39. ACS Chem Neurosci. 2019 Nov 20;10(11):4659-4668 [PMID: 31600047]
  40. Neurology. 2007 Jan 30;68(5):384-6 [PMID: 17082464]
  41. Biol Trace Elem Res. 2022 Feb;200(2):669-677 [PMID: 33740180]
  42. Parkinsonism Relat Disord. 2008;14 Suppl 2:S116-8 [PMID: 18583172]
  43. Curr Pharm Des. 2005;11(8):999-1016 [PMID: 15777250]
  44. Science. 2022 Mar 18;375(6586):1231-1232 [PMID: 35298241]
  45. J Inherit Metab Dis. 2021 May;44(3):534-543 [PMID: 33141444]
  46. J Neurochem. 1993 Oct;61(4):1538-41 [PMID: 8377003]
  47. Inorg Chem. 2014 May 5;53(9):4350-8 [PMID: 24725094]
  48. Lancet Neurol. 2015 Aug;14(8):855-866 [PMID: 26050140]
  49. Ann Neurol. 1998 Sep;44(3 Suppl 1):S53-7 [PMID: 9749573]

MeSH Term

Humans
Parkinson Disease
Copper
Computational Biology
Gene Regulatory Networks
Female
Male
Biomarkers
Gene Expression Profiling
Cell Proliferation
MicroRNAs
Middle Aged
Aged

Chemicals

Copper
Biomarkers
MicroRNAs

Word Cloud

Created with Highcharts 10.0.0genesPDcopperidentifiedhubdiseasestudymetabolismpatientsDEGsCMRGscell-relatedimmuneanalysisrelatedParkinson'sanalysesAnalysisHubROCIPAnetworksTFinteractionsCSFsamplescellsproliferationmigrationviabilityGBE1pathwaysmetabolism-relatedOBJECTIVES:aimedidentifykeyprovidinginsightrolesprogressionMETHODS:UsingbioinformaticDifferentiallyexpressedusinglimmapackagecopper-metabolism-relatedsourcedGenecarddatabaseImmunederivedinfiltrationWeightedGeneCo-expressionNetworkWGCNApinpointedintegratingFunctionalincludedReceiverOperatingCharacteristicIngenuityPathwaymiRNA-mRNA-transcriptionfactorCompetitiveEndogenousRNAceRNAgene-drugValidationperformedcerebrospinalfluidvitroexperimentsutilizedGBE1-overexpressingSH-SY5YexaminecellRESULTS:NineHPRT1GLSSNCAMDH1DDCSTXBP1ACHEAGTR1753416951showedhighpredictiveaccuracyprincipalcomponentPCAeffectivelydistinguishedcontrols20significantvarioushighlightedmiRNAdruggeneexpressionvalidatedGBE1-overexpressingdisplayedenhancedCONCLUSIONS:ninepotentialtherapeutictargetshighlightingrelevancepathologypossibletreatmentIdentificationmarkersParkinson’sbiomarker

Similar Articles

Cited By