Research ArticleMicroarray Analysis of the Molecular Mechanism Involved inParkinson’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; cjj@jlu.edu.cn

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 samplesfrom 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 signalingpathway,” 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 theabovementioned six TFs might play important roles in PD development.

1. Introduction

Parkinson’s disease (PD) is one of the most common age-related neurodegenerative diseases [1].)e age at PD onset isapproximately 55 years, and the incidence in the populationaged> 65 years is approximately 1% [1–3]. PDmainly occursbecause of the death of dopaminergic neurons in the sub-stantia nigra [4]. Patients with PD present with symptomssuch as bradykinesia, resting tremor, rigidity, and posturalinstability [5]. )e current therapy for PD is targeted at itssymptoms rather than at dopaminergic neuron degeneration[1]. )e diagnosis of PD at the early stage is challenging, andsuccessfully managing PD is difficult at its later stages [4]. Todate, the cause of PD remains unknown; however, it appearsto involve the intricate interplay of environmental andgenetic factors [1, 4].

Much effort has been spent in investigating PD patho-genesis, and the misfolding, aggregation, and aberrance ofproteins are considered to be some of the main causes

[1, 4, 5]. Some key genes such as hydrogen sulfide, chro-mobox 5 (CBX5), and transcription factor 3 (TCF3) arerelated to PD [6, 7]. Several pathways have also beenidentified to be related to PD. Activation of the proteinkinase B (Akt)/glycogen synthase kinase 3 beta/(GSK3β)pathway by urate reportedly protects dopaminergic neuronsin a rat model of PD [8]. In addition, the E2-related factor 2(Nrf2)/antioxidant response element pathway reportedlycounteracts mitochondrial dysfunction, which is a prom-inent PD feature [9]. )e ubiquitin, lipid, nigrostriatal,autophagy-lysosome, and endosomal pathways are alsoinvolved in PD [10–15]. Furthermore, a recent study revealedseveral microRNAs (miRNAs) associated with PD; miR-205suppresses LRRK2 expression andmiR-205 expression levelsin the brains of patients with PD decreases [16]. Further-more, miR-34b and miR-34c are downregulated in thebrains of patients with PD, which is related to the reductionin the expression ofDJ-1 and PARKIN [17], andmiR-133 andmiR-7 are also associated with PD [18–20]. Numerous

HindawiParkinson’s DiseaseVolume 2018, Article ID 1590465, 12 pageshttps://doi.org/10.1155/2018/1590465

reports that have described the roles of transcription factors(TFs) in PD have also been published. �e TF paired-likehomeodomain 3 has roles in developing and maintainingdopaminergic neurons [21, 22], and engrailed l, which isdownregulated in the rat models, plays a role in the apo-ptosis of dopaminergic neurons and the symptoms of PD[23]. Moreover, Nrf2, nuclear factor kappa B (NF-κB),GATA2, and PHD �nger protein 10 are TFs involved in PD[24–27]. However, understanding the key mechanismsunderlying the development of PD remains unclear.

In a previous study, the microarray dataset GSE72267generated by Calligaris et al. [7] was used to identify keydi�erentially expressed genes (DEGs) such as CBX5, TCF3,

dedicator of cytokinesis 10, and mannosidase alpha class 1Cin the blood of patients with PD compared with those ofhealthy controls. Moreover, crucial pathways related tochromatin remodeling and methylation were revealed. Inthe current study, we downloaded this microarray dataset tocomprehensively analyze DEGs in patients with PD com-pared with those in matched controls by bioinformaticsapproaches and to describe their functional annotations.Compared with the previous analysis conducted by Calli-garis et al. [7], we performed additional analyses, includingthose for the protein-protein interaction (PPI), miRNA-target regulatory, and TF-target regulatory networks, tofurther elucidate the key mechanisms underlying PD. Our

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Figure 1: Boxplots for normalized gene expression data. Red represents the blood samples of patients with Parkinson’s disease, and whiterepresents the healthy matched control samples.

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2 Parkinson’s Disease

results may provide useful data for diagnosing andtreating PD.

2. Materials and Methods

2.1. A�ymetrix Microarray Data. Gene expression pro�ledata GSE72267 was extracted from the Gene ExpressionOmnibus database (https://www.ncbi.nlm.nih.gov/geo/) [28].�e GSE72267 dataset was deposited by Calligaris et al. [7],

including blood samples from 40 PD patients and 19 healthymatched controls andwas based on the platform of the GPL571(HG-U133A-2) A�ymetrix Human Genome U133A 2.0 Array(A�ymetrix Inc., Santa Clara, California, USA). �is datasetwas downloaded and analyzed on October 2016.

2.2.DataPreprocessing andDEGScreening. �e downloadeddata in CEL �les were preprocessed using the A�y package

MTMR10FNDC3A

MCCC2

MLANA

CHATTPD52ACAA2

NTRK3

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ITGB4

HPSETRPC1

MAST4EZH1

EPOR

AIMP2

TNFRSF25

ABCC6

MCF2L CDYL

SLA ETFA

ITGB1BP2

ETFDH

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SPTBN1CBLBSYN3 PDP1CALR

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IL22RA1

NPAS3

SFTPBNPM3

CACYBP

HLA–DQB1

SH3BP5BRD3

CD83 IL9R

SYNE1NMT2

KIDINS220

HADHBALDH6A1

CROT

KCNMB4

PSPN

SCLY

UFSP2

SCNN1AKCND3

CNNM2ASXL1

DMXL2PRDX2ITGB3

MECOM HSPA1L

RPS6KA6

INPP5B

PIK3C2BCYLDEHMT2

KCNMA1AREG

GNAO1

VDAC2

STK38

MAP2K7

ICAM1CA2

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SMAD1

DTNB

NFKBIA

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PRKDC

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GNL3L

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LRRFIP2SMC5

TTC9MSMO1 BTG2LRRFIP1

SIX5POLR3C

ATG10

KHSRP

MAP2K6

WWOX

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HBS1L

GTF2H1FANCI

ATG16L1

NUAK1UBE2J1

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TIMM17A

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GATA3

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TFDP1

QSOX1

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Figure 3: �e protein-protein interaction (PPI) network of di�erentially expressed genes (DEGs). Red circles represent upregulated DEGs,and green diamonds represent downregulated DEGs.

Parkinson’s Disease 3

(version 1.50.0) [29] in R language, including backgroundcorrection, normalization, and expression calculation. An-notations to the probes were performed, and probes thatwere not matched to the gene symbol were excluded. )eaverage expression values were taken if different probesmapped to the same gene. DEGs in patients with PDcompared with those in healthy matched controls wereanalyzed using the limma package (version 3.10.3) [30] in Rlanguage. )e cutoff threshold was set to a p value of <0.05.

2.3. Pathway Enrichment Analysis. Gene ontology (GO)(http://www.geneontology.org/) analysis is commonly usedfor functional studies of large-scale genomic or tran-scriptomic data and classifies functions with respect to threeaspects: molecular function (MF), cellular component (CC),and biological process (BP) [31, 32].)e Kyoto Encyclopediaof Genes and Genomes (KEGG; http://www.kegg.jp/)pathway database [33] is widely used for systematic analysisof gene functions, linking genomic data with higher orderfunctional data. )e database for annotation, visualization,and integrated discovery (DAVID) is an integrated bi-ological knowledgebase with analytical tools used for sys-tematic and integrative analysis of large gene lists [34]. Inthis study, GO terms and KEGG pathway enrichment an-alyses for up- and downregulated DEGs were performedusing DAVID (version 6.8). )e cutoff thresholds were asfollows: an enrichment gene number count of ≥2 and a supergeometry inspection significance threshold p value of <0.05.

2.4. PPI Network Analysis. Search Tool for the Retrieval ofInteracting Genes/Proteins (STRING; http://www.string-db.org/) [35] is an online database that assesses and integratesPPIs. In this study, DEGs were mapped into the STRINGdatabase for PPI analysis, with a PPI score of 0.4 as theparameter setting.)e PPI network established by DEGs wasconstructed using the Cytoscape software (version 3.2.0)[36], and the topology scores of the nodes, including nodedegree in the PPI network, were analyzed using theCytoNCA plugin (version 2.1.6; http://apps.cytoscape.org/apps/cytonca) [37] (parameter setting: withoutweight). Degree was used for describing importance of

protein nodes in network. )e higher the degree was, themore important the nodes were in network. In addition,subnetworks were identified using the MCODE plugin [38]in the Cytoscape software, and subnetworks with a score of>5 were identified as key subnetworks. Finally, KEGGpathway enrichment analyses for the genes in the keysubnetworks were performed.

2.5. miRNA-Target Regulatory Network Analysis. )emiR2disease (http://www.mir2disease.org/) database[39] is a manually curated database that providesa comprehensive resource of miRNA deregulation invarious human diseases. miRWalk2.0 (http://zmf.umm.uni-heidelberg.de/apps/zmf/mirwalk2/) [40] is a com-prehensive database that presents predicted and validateddata, regarding miRNA targets in human, mouse, andrats. In this study, miRNAs related to PD were extractedfrom the miR2disease database, and experimentallyverified miRNA-gene regulatory pairs were obtained bysearching miRWalk2.0. Finally, a miRNA-target regula-tory network was constructed by comparing DEGs withobtained miRNA-gene regulatory pairs using the Cyto-scape software.

2.6. TF-Target Regulatory Network Analysis. )e genes in thePPI network described above were further analyzed to identifyTF-target interaction pairs that were then used to constructa TF-target regulatory network. )e iRegulon plugin (version1.3; http://apps.cytoscape.org/apps/iRegulon) [41] in theCytoscape software collects multiple human TF-target in-teraction databases such as Transfac, Jaspar, and Encode usingtwo computational methods: Motif and Track. In this study, weanalyzed the TF-target pairs using the iRegulon plugin andcompared them with TFs with DEGs in the PPI network,followed by a TF-target regulatory network construction. )eparameter settings were as follows: minimum identity betweenorthologous genes, 0.05 and maximum false discovery rate onmotif similarity, 0.001.)e normalized enrichment score (NES)indicates the reliability of the results, and the cutoff thresholdwas NES of >3.

Table 1: List of top 10 differentially expressed genes with higher degrees in protein-protein interaction network.

Gene Full name Description DegreeMAPK14 Mitogen-activated protein kinase 14 Down 68ESR1 Estrogen receptor 1 Up 54PTEN Phosphatase and tensin homolog Down 52MTOR Mechanistic target of rapamycin Up 40ATM ATM serine/threonine kinase Up 35ICAM1 Intercellular adhesion molecule 1 Down 33CD40 CD40 molecule Up 32AURKA Aurora kinase A Down 31PRKDC Protein kinase, DNA-activated, catalytic polypeptide Down 29TK2 )ymidine kinase 2, mitochondrial Up 29Degree was used for describing the importance of protein nodes in network. )e higher the degree was, the more important the nodes were in network.

4 Parkinson’s Disease

3. Results

3.1.Analysis ofDEGs. �e boxplot of the preprocessed dataindicated good normalization (Figure 1). In total, 22,277probes were obtained, among which 971 probes weredi�erentially expressed. After annotation, 819 DEGs inpatients with PD compared with those in healthy matchedcontrols were identi�ed (Supplementary Table 1), in-cluding 359 upregulated DEGs and 460 downregulatedDEGs.

3.2. Pathway Enrichment Analysis. GO and KEGG pathwayenrichment analyses for the up- and downregulated DEGswere performed (Supplementary Table 2). �e signi�cantGO terms and KEGG pathways are shown in Figure 2. �eupregulated DEGs were signi�cantly enriched in four KEGGpathways, namely, metabolic pathways, inositol phosphatemetabolism, mRNA surveillance pathway, and RNA deg-radation, and GO terms such as transcription, DNA-template processing, and rRNA processing (Figure 2(a)).

GPR18

CCR6

PTGER3

MCHR1

CCR9

NPY1R

CCR4

FPR3

S1PR5

(a)

NOP14

RSL1D1

DDX27

GNL3L

GLTSCR2

RTCA

PA2G4

(b)

OAS3

GNAT1

F2RICAM1

OASLHLA-C

GNAI2

RGS2SP100

CDC14A

RRM2

CCNA2

CDK3

AURKA

PLK3

CCNB1

TFDP1

PML

CASP1

MCM4

PARP1

CASP8

BRCA2

PRKDCVCAM1

CD40

RIPK2

(c)

Figure 4: Subnetworks of di�erentially expressed genes (DEGs). (a) Subnetwork a; (b) subnetwork b; (c) subnetwork c. Red circles representupregulated DEGs, and green diamonds represent downregulated DEGs.

Parkinson’s Disease 5

�e downregulated DEGs were enriched in pathways such asthose of in£uenza A, viral myocarditis, and TNF signalingand GO terms such as cytoplasm, cell surface, and interferongamma-mediated signaling pathway (Figure 2(b)).

3.3. PPI Network Analysis. �e PPI network, including 605nodes and 1937 PPI pairs, is shown in Figure 3. �e top 10DEGs with the highest degree included �ve upregulatedDEGs such as estrogen receptor 1 (ESR1), mechanistic targetof rapamycin (MTOR), ATM serine/threonine kinase(ATM), CD40 molecule (CD40) and thymidine kinase 2,mitochondrial (TK2), and �ve downregulated DEGs such asmitogen-activated protein kinase 14 (MAPK14), phosphatase

and tensin homolog (PTEN), intercellular adhesion molecule1 (ICAM1), aurora kinase A (AURKA), and protein kinase,DNA-activated, catalytic polypeptide (PRKDC) (Table 1).�ree subnetworks were identi�ed (subnetworks a–c). Sub-network a (Figure 4(a)) included nine nodes and 36 PPI pairs,and these genes were signi�cantly enriched in three KEGGpathways (Table 2), including neuroactive ligand-receptorinteraction, chemokine signaling pathway, and cytokine-cytokine receptor interaction. Subnetwork b (Figure 4(b))included seven nodes and 21 PPI pairs, and these geneswere not enriched in any KEGG pathway. Subnetwork c(Figure 4(c)) included 27 nodes and 81PPI pairs, and these geneswere enriched in 12 KEGG pathways (Table 2), such as cellcycle, herpes simplex infection, and NF-κB signaling pathways.

Table 2: List of KEGG pathways of subnetworks.

Subnetwork PathwayID Pathway name Count p value Genes

Subnetworka

hsa04080 Neuroactive ligand-receptor interaction 5 1.40E−04 MCHR1, PTGER3, S1PR5, FPR3, NPY1Rhsa04062 Chemokine signaling pathway 3 1.80E−02 CCR9, CCR6, CCR4hsa04060 Cytokine-cytokine receptor interaction 3 2.74E−02 CCR9, CCR6, CCR4

Subnetworkc

hsa04110 Cell cycle 6 1.31E−04 CCNB1, CDC14A, PRKDC, CCNA2, MCM4,TFDP1

hsa05416 Viral myocarditis 4 1.62E−03 ICAM1, CASP8, HLA-C, CD40hsa05168 Herpes simplex infection 5 6.03E−03 SP100, CASP8, OAS3, PML, HLA-Chsa04514 Cell adhesion molecules 4 1.70E−02 VCAM1, ICAM1, HLA-C, CD40hsa05144 Malaria 3 1.78E−02 VCAM1, ICAM1, CD40hsa04621 NOD-like receptor signaling pathway 3 2.00E−02 CASP8, RIPK2, CASP1hsa04115 p53 signaling pathway 3 2.93E−02 CCNB1, RRM2, CASP8hsa05164 In£uenza A 4 3.32E−02 ICAM1, OAS3, PML, CASP1

hsa04914 Progesterone-mediated oocytematuration 3 4.30E−02 CCNB1, GNAI2, CCNA2

hsa05169 Epstein–Barr virus infection 4 4.42E−02 ICAM1, HLA-C, CD40, CCNA2hsa05203 Viral carcinogenesis 4 4.60E−02 SP100, CASP8, HLA-C, CCNA2hsa04064 NF-kappa B signaling pathway 3 4.84E−02 VCAM1, ICAM1, CD40

KEGG, Kyoto Encyclopedia of Genes and Genomes.

hsa-miR-133b

PTPLAD1

EDN1

SERBP1

PKM

hsa-miR-433-3p

YIPF6

POLE3

BTG2

ACVR1B

APLP2

SULT1C2

ATP1B3

CBS

PDE4D

C1orf21

PARP11

NIPAL3

TMEM38B

FAM63B

PPP2R1B

ZNF264

PARP1

KPNA6

ACTN2

XIAP

RPRD2AGPAT1

NREP

TCL1B

hsa-miR-7-5p

TSN

EFHD2

EXOSC2

KIF5B

MFSD9

PA2G4

GPR135

PTPRK

STK38

ASXL1

SPATS2L

RNF114

Figure 5: MicroRNA- (miRNA-) target regulatory networks of di�erentially expressed genes (DEGs). Triangles represent miRNAs, redcircles represent upregulated DEGs, and green diamonds represent downregulated DEGs.

6 Parkinson’s Disease

In addition, ICAM1 was involved in six KEGG pathwaysof subnetwork c, such as viral myocarditis, cell adhesionmolecules (CAMs), and NF-κB signaling pathways (Table 2).�e detailed information existed in PPI network, and threesubnetworks are shown in Supplementary Table 3.

3.4. miRNA-Target Regulatory Network Analysis. Accordingto the data from the miR2disease database, six miRNAs wereidenti�ed to be associated with PD and 698 miRNA-genepairs were obtained by searching miRWalk2.0. A total of40 miRNA-target interaction pairs were obtained by com-paring miRNA-gene pairs with DEGs, and subsequently, the

miRNA-target regulatory network was constructed. �enetwork (Figure 5) contained 40 miRNA-target interactionpairs and 43 nodes (Supplementary Table 4), among whichthree miRNAs (hsa-miR-7-5p, hsa-miR-433-3p, and hsa-miR-133b) were included.

3.5. TF-Target Regulatory Network Analysis. According theinformation of TF-target interaction databases such asTransfac, Jaspar, and Encode in the Cytoscape software,a total of 83 TFs were identi�ed from the PPI network,forming 5371 TF-gene pairs. Among the 83 TFs, six weredi�erentially expressed: three upregulated ones, that is, zinc

SLC7A7

FERMT2

PLCH1

ASS1

GPSM2

TAL1

CADM1ZBTB7A

EFHD2

DUSP2PARP11

KCNMA1

CIB2

NFKBIA

CBX4

OGT

CYLD

ADCK3TSPAN13

SYNJ2

NCOA1

LRRFIP2

KYNU

SYT11

FANCI

CMKLR1

MTMR3

PLSCR1

ICAM1

TSPAN31

KLF9ARRB2

AKAP13NR1D1

CD86

FKBP5

TNFAIP3

PTPRK

ARIH1

CD40

GFRA2

SLA

TNPO1

ITSN2

SEMA3C

SP2

DSPPPFIBP1SPTBN1

TCF3

HLF PTN

HLA-C

MAPK4

B3GALT2

PFAS

TTBK2

BRD3KLF7

IVNS1ABP

AHCYL1TNIP3GATA3

GTF2A1

BANK1FRK

BMP6

ST14

TLX2

WIPF2

SATB2

OTUD7B

TRPC5NOP14

NUAK1

NPAS3MSI1

RORB

KIF17

SYNCRIP

OVOL1PDE4D

FNDC3A

ESR1

LARGE

ENC1

SCN3A

PROX1

DHODH

PDE3A

LRRN3

PRKDCGALNT10

STK38

GREB1L

NOTCH2

HOXC4

CAMTA1MID1

ZNF362SMG7

CORO2B

SAP30

NPY1R

KDELR1H2AFY

BFSP1

MR1

NEK1

TRPM3

VCANDUSP6

TRAF3IP2

QSOX1

CCR6MLLT10

PPP2R3AGLI3

ACSBG1

DPP4RBM15B

CTBP2

SLC6A14DOCK10

SMAD1

MAST4

ANXA4GNAO1

PCNX

CBLB

LRP1

TRPC4

SPRY2

ERCC3RRM2

SART3

ATM

MCM4

SBNO1

ANKH

SERBP1HPSE

ZCWPW1

AMFR

CHAF1AATP5G2

CCR9EZH1

SSRP1

MYO6

BTG1

NR1H4 DHFR

MECOM

CA2

SCN8A

TFDP1

TPD52

TOX DDX6

ASXL1

ETV1

SORT1

SH3BP5

IL22RA1CD96

Figure 6:�e TF-target regulatory network of di�erentially expressed genes (DEGs). Blue boxed �gures represent TFs, red circles representupregulated genes, and green diamonds represent downregulated genes. TF, transcription factor.

Parkinson’s Disease 7

finger and BTB domain-containing 7A (ZBTB7A), ovo-liketranscriptional repressor 1 (OVOL1), and GATA-bindingprotein 3, and three downregulated ones, that is, tran-scription factor dp-1 (TFDP1), SMAD family member 1(SMAD1), and quiescin sulfhydryl oxidase 1 (QSOX1). )eTF-target regulatory network (Figure 6) was constructed andincluded 166 nodes and 288 interaction pairs (Supple-mentary Table 5). )e top 20 nodes with the highest degreeare listed in Table 3, including the six TFs described aboveand 14 other DEGs, such as ectodermal-neural cortex 1,fibronectin type III domain-containing 3A, and midline 1,which were coregulated by the six TFs.

4. Discussion

PD is the second most common age-related neurodegen-erative disease. However, the pathogenesis and genes in-volved in PD are not well known [42]. In this study, weperformed a comprehensive bioinformatics analysis of theblood gene expression profile using the GSE72267 dataset.)e results suggested that four key pathways (metabolicpathways, TNF signaling pathway, rRNA processing, andcytoplasm), the key gene ICAM1, three miRNAs (hsa-miR-7-5p, hsa-miR-433-3p, and hsa-miR-133b), and six TFs(ZBTB7A, OVOL1, GATA3, TFDP1, SMAD1, and QSOX)might play important roles in PD development.

Our results revealed that the upregulated DEGs wereenriched in the KEGG pathway “metabolic pathways” and the

GO term “rRNA processing,” and the downregulated DEGswere enriched in the KEGG pathway “TNF signaling path-way” and the GO term “cytoplasm.” A previous study [43]demonstrated that some metabolic patterns were altered inpatients with advanced PD. Multiple metabolic pathways arealso involved in PD [44], which supports our study results.Cytoplasmic inclusions are a pathological hallmark of PD[45]. Lewy body pathology is involved [46, 47], and glialcytoplasmic inclusions are associated with Lewy bodies [48].)us, the GO term “cytoplasm” may play a role in PD.Furthermore, TNF receptor-associated protein is excludedfrom the nucleolus and is sequestered to the cytoplasm byTNF receptor-associated factor 6, thereby altering ribosomalRNA (rRNA) biogenesis [49]. )e TNF signaling pathway isalso involved in PD [50], and rRNA transcription is repressedin patients with PD [51]. )erefore, the GO term “rRNAprocessing” and the KEGG pathway “TNF signaling pathway”may play important roles in PD. Altogether, the metabolicpathways, TNF signaling pathway, rRNA processing, andcytoplasm are essentially involved in PD pathogenesis.

ICAM1 was among the top 10 DEGs in the PPI network.Moreover, ICAM1 gene was involved in six KEGG pathwaysfor subnetwork c. ICAM1 is involved in the adhesion andtransmigration of leukocytes across the endothelium,promoting brain inflammation and resulting in braindiseases [52]. T helper 17 cells can exert a neurotoxic effect inthe brain parenchyma of patients with PD by interacting withICAM1 and leukocyte function-associated antigen 1 [53]. In

Table 3: List of top 20 nodes with higher degree in transcription factor-target regulatory network.

Gene Full name Description DegreeTFDP1∗ Transcription factor Dp-1 Down 62ZBTB7A∗ Zinc finger and BTB domain-containing 7A Up 55OVOL1∗ Ovo-like transcriptional repressor 1 Up 46SMAD1∗ SMAD family member 1 Down 45QSOX1∗ Quiescin sulfhydryl oxidase 1 Down 44GATA3∗ GATA-binding protein 3 Up 38ENC1 Ectodermal-neural cortex 1 Down 6FNDC3A Fibronectin type III domain-containing 3A Up 6MID1 Midline 1 Down 6PDE4D Phosphodiesterase 4D Down 5ZNF362 Zinc finger protein 362 Up 5CBLB Cbl proto-oncogene B Down 4LARGE LARGE xylosyl- and glucuronyltransferase Up 4

TRPC4 Transient receptor potential cation channel subfamilyC member 4 Down 4

CTBP2 C-terminal binding protein 2 Up 4GLI3 GLI family zinc finger 3 Down 4SCN3A Sodium voltage-gated channel alpha subunit 3 Up 4

TAL1 TAL BHLH transcription factor 1, erythroiddifferentiation factor Down 4

LRRN3 Leucine rich repeat neuronal 3 Up 3

MAST4 Microtubule-associated serine/threonine kinasefamily member 4 Up 3

∗Transcription factor.

8 Parkinson’s Disease

addition, ICAM1 is involved in persistent inflammation inPD [54]. Our results from the KEGG pathway analysis forgenes in subnetworks revealed that ICAM1 might play rolesin viral myocarditis and CAMs and thus contributed to PD.

)e miRNA-target regulatory network analysis identifiedthree miRNAs involved in PD, namely, hsa-miR-7-5p, hsa-miR-433-3p, and hsa-miR-133b. A study described miR-7-2dysregulation (the stem loop of hsa-miR-7-5p) in Parkinson’spatient’s leukocytes [55] and revealed that hsa-miR-7-5pexpression decreased in PD, possibly upregulating α-SYN,a PD-related gene [56].)e variation of the hsa-miR-433- (thestem loop of hsa-miR-433-3p-) binding site of fibroblastgrowth factor 20 can lead to α-SYN overexpression, in-creasing the risk for PD [57]. hsa-miR-133b expression isincreased in the cerebrospinal fluid of patients with PD[58]; however, its expression levels in serum is decreased,which is related to low serum ceruloplasmin levels [59].hsa-miR-133b is also deficient in the midbrain tissue ofpatients with PD and is associated with the maturation andfunction of midbrain dopaminergic neurons [60]. Notably,reduced circulating levels of miR-433 and miR-133b areconsidered as promising biomarkers for PD [61].)erefore,we speculate that the three miRNAs, including hsa-miR-7-5p, hsa-miR-433-3p, and hsa-miR-133b may play impor-tant roles in PD.

TFs are important regulators of target gene expressions[53, 62]. In this study, we analyzed DEGs in the PPI networkto screen TFs involved in PD. Among the 83 TFs identifiedin the PPI network, six were found to be differentiallyexpressed. ZBTB7A, OVOL1, and GATA3 were upregulatedin patients with PD compared with those in healthy matchedcontrols, whereas TFDP1, SMAD1, and QSOX1 weredownregulated. ZBTB7A is a tumor suppressor, which isinvolved in several cancers such as prostate and nonsmallcell lung cancers [63–65]. OVOL1, encoding a zinc fingerprotein, is expressed in embryonic epidermal progenitorcells and is an inducer of mesenchymal-to-epithelial tran-sition in human cancers [66, 67]. GATA3, a member of theGATA family, is a regulator of T-cell development and playsroles in endothelial cells [68, 69]. TFDP1 is involved in thecell cycle and contributes to hepatocellular carcinomas[70, 71], SMAD1 is involved in multiple pathways [72, 73],andQSOX1 plays roles in some cancers such as breast cancerand neuroblastoma [74–76]. However, there are few reportsregarding the involvement of these TFs in PD. Hence,further studies regarding the associations between the TFsidentified in this study and PD are warranted.

In conclusion, our data demonstrated that the metabolicpathways, TNF signaling pathway, rRNA processing, andcytoplasm play important roles in PD pathogenesis; ICAM1might also play a vital role. Besides six TFs, three miRNAs,including hsa-miR-7-5p, hsa-miR-433-3p, and hsa-miR-133b, may be involved in PD. However, because of thestudy limitations, further investigation remains to be per-formed in the future.

Conflicts of Interest

)e authors declare that there are no conflicts of interestregarding the publication of this article.

Acknowledgments

)is study was supported by the Construction of AccurateTechnology Innovation Centers of Nervous System Disease,Jilin Province (no. 20170623006TC), and Study on theMechanism of the Parkinson lncrna, Jilin Provincial De-partment of Finance Project.

Supplementary Materials

Supplementary 1. Table 1: all up- and downregulated dif-ferentially expressed genes.Supplementary 2. Table 2: GO and KEGG pathway en-richment analyses for the up- and downregulated differ-entially expressed genes. GO, Gene Ontology; KEGG, KyotoEncyclopedia of Genes and Genomes.Supplementary 3. Table 3: the detailed information existingin protein-protein interaction (PPI) network and threesubnetworks.Supplementary 4. Table 4: the detailed information about themiRNA-target regulatory network.Supplementary 5. Table 5: the detailed information abouttranscription factor- (TF-) target regulatory network.

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