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Research Article Microarray Analysis of the Molecular Mechanism Involved in Parkinson’S Disease

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Research Article Microarray Analysis of the Molecular Mechanism Involved in Parkinson’S Disease Hindawi Parkinson’s Disease Volume 2018, Article ID 1590465, 12 pages https://doi.org/10.1155/2018/1590465 Research Article Microarray Analysis of the Molecular Mechanism Involved in Parkinson’s Disease Cheng Tan, Xiaoyang Liu, and Jiajun Chen Department of Neurology, China-Japan Union Hospital of Jilin University, Changchun, Jilin 130033, China Correspondence should be addressed to Jiajun Chen; [email protected] Received 24 May 2017; Revised 21 August 2017; Accepted 18 October 2017; Published 1 March 2018 Academic Editor: Amnon Sintov Copyright © 2018 Cheng Tan et al. )is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Purpose. )is study aimed to investigate the underlying molecular mechanisms of Parkinson’s disease (PD) by bioinformatics. Methods. Using the microarray dataset GSE72267 from the Gene Expression Omnibus database, which included 40 blood samples from PD patients and 19 matched controls, differentially expressed genes (DEGs) were identified after data preprocessing, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Protein-protein interaction (PPI) network, microRNA- (miRNA-) target regulatory network, and transcription factor- (TF-) target regulatory networks were constructed. Results. Of 819 DEGs obtained, 359 were upregulated and 460 were downregulated. Two GO terms, “rRNA processing” and “cytoplasm,” and two KEGG pathways, “metabolic pathways” and “TNF signaling pathway,” played roles in PD development. Intercellular adhesion molecule 1 (ICAM1) was the hub node in the PPI network; hsa- miR-7-5p, hsa-miR-433-3p, and hsa-miR-133b participated in PD pathogenesis. Six TFs, including zinc finger and BTB domain- containing 7A, ovo-like transcriptional repressor 1, GATA-binding protein 3, transcription factor dp-1, SMAD family member 1, and quiescin sulfhydryl oxidase 1, were related to PD. Conclusions. “rRNA processing,” “cytoplasm,” “metabolic pathways,” and “TNF signaling pathway” were key pathways involved in PD. ICAM1, hsa-miR-7-5p, hsa-miR-433-3p, hsa-miR-133b, and the abovementioned six TFs might play important roles in PD development. 1. Introduction [1, 4, 5]. Some key genes such as hydrogen sulfide, chro- mobox 5 (CBX5), and transcription factor 3 (TCF3) are Parkinson’s disease (PD) is one of the most common age- related to PD [6, 7]. Several pathways have also been related neurodegenerative diseases [1]. )e age at PD onset is identified to be related to PD. Activation of the protein approximately 55 years, and the incidence in the population kinase B (Akt)/glycogen synthase kinase 3 beta/(GSK3β) aged > 65 years is approximately 1% [1–3]. PD mainly occurs pathway by urate reportedly protects dopaminergic neurons because of the death of dopaminergic neurons in the sub- in a rat model of PD [8]. In addition, the E2-related factor 2 stantia nigra [4]. Patients with PD present with symptoms (Nrf2)/antioxidant response element pathway reportedly such as bradykinesia, resting tremor, rigidity, and postural counteracts mitochondrial dysfunction, which is a prom- instability [5]. )e current therapy for PD is targeted at its inent PD feature [9]. )e ubiquitin, lipid, nigrostriatal, symptoms rather than at dopaminergic neuron degeneration autophagy-lysosome, and endosomal pathways are also [1]. )e diagnosis of PD at the early stage is challenging, and involved in PD [10–15]. Furthermore, a recent study revealed successfully managing PD is difficult at its later stages [4]. To several microRNAs (miRNAs) associated with PD; miR-205 date, the cause of PD remains unknown; however, it appears suppresses LRRK2 expression and miR-205 expression levels to involve the intricate interplay of environmental and in the brains of patients with PD decreases [16]. Further- genetic factors [1, 4]. more, miR-34b and miR-34c are downregulated in the Much effort has been spent in investigating PD patho- brains of patients with PD, which is related to the reduction genesis, and the misfolding, aggregation, and aberrance of in the expression of DJ-1 and PARKIN [17], and miR-133 and proteins are considered to be some of the main causes miR-7 are also associated with PD [18–20]. Numerous dierentially expressed genes (DEGs)generated such as by Calligaris et al. [7]underlying was the used development to of[24–27]. identify PD key remains However, unclear. GATA2, understanding and PHD nger the protein[23]. 10 are key Moreover, TFs involved mechanisms in Nrf2,ptosis PD of nuclear dopaminergic factor neuronsdownregulated and in kappa the the B symptoms ratdopaminergic of models, neurons (NF- PD plays [21, ahomeodomain 22], role 3 and in has engrailed the(TFs) roles l, in apo- in which PD developing is havereports and that also maintaining have been described the published. roles e of transcription TF factors paired-like e numbers on the cyclopedia of Genes and Genomes (KEGG)Figure pathways of upregulated DEGs and (b) GO terms and KEGG pathways of downregulated DEGs. represents the healthy matchedFigure control samples. 2 In a previous study, the microarray dataset GSE72267 1: Boxplots for normalized gene expression data. Red represents the blood samples of patients with Parkinson’s disease, and white 2: Functional enrichment analyses of dierentially expressed genes (DEGs). (a) Gene Ontology (GO) terms and the Kyoto En- Gene expression 10 12 14 2 4 6 8 x -axis were the ID of pathways or GO terms. e numbers on the Gene counts 10 15 20 25 30 35 40 45 50 0 5 HC01 HC02 hsa00562: inositol phoshate metabolism HC03 HC04 hsa01100: metabolic pathways KEGG pathway BP HC05 hsa03015: mRNA surveillance pathway HC06 HC07 hsa03018: RNA degradation HC08 HC09 GO:0032355~response to estradiol HC10 κ (a) B), GO:0006364~rRNA processing HC11 HC12 GO:0090102~cochlea development HC13 GO:0006351~transciption, DNA template HC14 HC15 GO:0006417~regulation of translation MF CC HC16 HC17 CBX5 GO:0001518~voltage-gated sodium channel complex HC18 GO:000178~exosome (RNase complex) HC19 PD01 GO:0005261~cation channel activity PD02 , GO:0003723~RNA binding PD03 TCF3 PD04 GO:0001078~transcriptional repressor activity, etc. PD05 PD06 , PD07 Gene counts Data normalization PD08 180 100 120 140 160 20 40 60 80 0 PD09 PD10 further elucidate the key mechanisms underlyingtarget PD. Our regulatory, and TF-targetthose regulatory for the networks, protein-protein to garis interaction et al. (PPI), [7], miRNA- Compared with theapproaches previous analysis and conducted topared by describe Calli- with their thosecomprehensively functional in analyze annotations. DEGs matchedthe in controls current patients study, by we with downloaded this bioinformatics chromatin PD microarray remodeling com- dataset to andhealthy methylation were controls. revealed. Moreover,in In the crucial blood pathwaysdedicator of of patients cytokinesis related 10, with and to PD mannosidase alpha compared class 1C with those of hsa05416: viral myocarditis PD11 PD12 hsa05164: influenza A PD13 BP KEGG pathway hsa05332: gra-versus-host disease PD14 hsa04668: TNF signaling pathway PD15 PD16 hsa05032: morphine addiction PD17 GO:0060333~interferon gamma-mediated signaling pathway PD18 PD19 GO:0070373~negative regulation of ERK1 and ERK2 cascades PD20 we performed additional analyses, including GO:0034142~toll-like receptor 4 signaling pathway PD21 (b) PD22 y GO:0032088~negative regulation of NF-kappaB transcription, etc. -axis were gene counts. PD23 GO:0035335~peptidyl-tyrosine dephosphorylation PD24 GO:0042612~MHC class I protein complex PD25 PD26 GO:0012507~ER to Golgi transport vesicle membrane PD27 MF CC GO:0009986~cell surface PD28 GO:0005737~cytoplasm PD29 PD30 GO:0071556~integral component of lumenal side, etc. PD31 GO:0042605~peptide antigen binding PD32 GO:0019001~guanyl nucleotide binding PD33 PD34 GO:0004725~protein tyrosine phosphatase activity PD35 GO:0050840~extracellular matrix binding PD36 PD37 GO:005178~integrin binding PD38 Parkinson’s Disease PD39 PD40 Parkinson’s Disease 3 BPNT1 KCNG1 NUDT6 HLA–G CD160 FERMT2 OLFML2B QSOX1 MR1 HGSNAT CSH1 STS QSOX1 PLOD1 STAP1 CFDP1 EFCAB2 ZNF500 KCNK1 SLC12A3 HFE SATB2 COL16A1 PARP11 ABCC6 IFNW1 CD96 VPREB3 CD151 SYT11 ITGB1BP2 NPM3SLC11A2 SFTPB CTDSPL VCAN TRPM6 BANK1 LETM1 LARGE KLF9 COL5A1 KCNMB4 NPAS3 SLA ETFA PCDH17 FBLN1KLHL3 FSTL1 CACYBP CGGBP1 RNF115 BMX ALKBH1 TRPM3 GFRA2 PHF8 CAMK1G RNF114 CROT TNFRSF25 ZMYND11 IL22RA1 FAM110B TPO HPCAL4 COL6A1 PSPN ETFDH UFSP2 ECHDC3 MCF2L BRD3 ENC1 CAPN6 LDLRAD4 CDYL SH3BP5 CNNM2 ARIH2 CD83 IL9R ASXL1 ALDH6A1 SCNN1A MCCC2 MTMR10 SCLY MYO6 KLHL22 HADHB HLA–DQB1 KCND3 NINL PEF1 FNDC3A AKTIP NTRK3 SPRY2 HLA–C TSC22D3 KIDINS220 MLANA APPBP2 SLC27A2 AVIL AIMP2 KIF18A ASS1 ADCK3 TRPC4 MLLT11 ITGB4 SYNE1 ACAA2 ACTL6B MYL12A PDCD6 NMT2 CHAT TSPO2 MAST4 ZCWPW1 TPD52 THPO SYNCRIP KLC2 EZH1 ABI2 HOMER1 SYN3 SETBP1 SUV39H2 ANXA4 PDP1 EPOR SLC25A20 CALR CBLB SPTBN1 TRPC1 AGMAT HPSE SYNGR1 SAP30 IVNS1ABP MTMR1 FLT3LG KIF5B HIST1H2BN DIAPH2 CADM1 IRAK3 LTK COX16 ZBTB38 SYN1 DUSP2 FKBP5 CBX5 CMKLR1 URB1 CD86 FLT3 PCGF2 DUSP6 DDX6 AGPAT1 INPP5B TAL1 MLLT10 TLX2 BAALC SLK ICAM4 GP2 KCNMA1 TSN ID3 ACSBG1 SOCS1 KAT6B CBX4 KIF17 AREG SCN3A XAF1 SCN11A TNFAIP3 MYO10 KYNU COG5 SORT1CRHR2 ITGA2B NRD1 KDELR1 EHMT2 FGFR3 RUFY1 CYLD WDR1 DTNB SMAD1 PSMD1 HLF PIK3C2B SSH1 BMP6 PAIP1 DLGAP4 INPP4A MED6 CREM PTN COG2 LDLR ITSN2 AASDHPPT MID1 SCN8AFGFR1OP AKAP13 VDAC2 TRIP12 PHC3 LRP1 ACVR1B FGF7 SP100 PTPN2 ACTN2 PDE3A SMAD1 GDF9 RGS5
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