Gene Expression, Network Analysis, and Drug Discovery of Neurofibromatosis Type 2-Associated Vestibular Schwannomas Based on Bioinformatics Analysis

Hindawi Journal of Oncology Volume 2020, Article ID 5976465, 9 pages https://doi.org/10.1155/2020/5976465

Research Article Gene Expression, Network Analysis, and Drug Discovery of Neurofibromatosis Type 2-Associated Vestibular Schwannomas Based on Bioinformatics Analysis

Qiao Huang , Si-Jia Zhai , Xing-Wei Liao , Yu-Chao Liu, and Shi-Hua Yin

Department of Otolaryngology & Head and Neck Surgery, e Second Aﬃliated Hospital of Guangxi Medical University, Nanning 530007, China

Correspondence should be addressed to Shi-Hua Yin; [email protected]

Received 26 March 2020; Revised 27 May 2020; Accepted 1 June 2020; Published 15 July 2020

Academic Editor: Pierfrancesco Franco

Copyright © 2020 Qiao Huang 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.

Neurofibromatosis Type 2- (NF2-) associated vestibular schwannomas (VSs) are histologically benign tumors. ,is study aimed to determine disease-related genes, pathways, and potential therapeutic drugs associated with NF2-VSs using the bioinformatics method. Microarray data of GSE108524 were downloaded from the Gene Expression Omnibus (GEO) database, and differentially expressed genes (DEGs) were screened using GEO2R. ,e functional enrichment and pathway enrichment of DEGs were performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes Genomes (KEGG). Furthermore, the STRING and Cytoscape were used to analyze the protein-protein interaction (PPI) network of all differentially expressed genes and identify hub genes. Finally, the enriched gene sets belonging to the identified pathways were queried against the Drug-Gene Interaction database to find drug candidates for topical use in NF2-associated VSs. A total of 542 DEGs were identified, including 13 upregulated and 329 downregulated genes, which were mainly enriched in terms of focal adhesion, PI3K-Akt signaling pathway, ECM-receptor interaction, Toll-like receptor signaling pathway, Rap1 signaling pathway, and regulation of actin cytoskeleton. 28 hub genes were identified based on the subset of PPI network, and 31 drugs were selected based on the Drug-Gene Interaction database. Drug discovery using bioinformatics methods facilitates the identification of existing or potential therapeutic drugs to improve NF2 treatment.

1. Introduction accept the otolaryngology surgery and require improving or saving hearing. With the targeted molecular therapies be- VSs, also known as acoustic neuromas, are histologically coming increasingly common, drug therapy has gradually benign tumors originating from the eighth nerve. NF2 is a become possible. ,erefore, it is urgently required to de- rare autosomal dominant inherited disorder tumor caused termine effective drug targets for NF2-associated VSs by deletion or loss-of-function mutations in the NF2 gene therapies. ,e present study aimed to determine disease- encoding merlin [1]. ,e main characteristic of NF2-asso- related genes, pathways, and potential targeted therapeutic ciated VSs is the bilateral schwannomas of the vestibular drugs associated with NF2-associated VSs using the bio- nerve, which leads to sensorineural hearing loss, facial pa- informatics method. ralysis, vestibular dysfunction, brainstem compression, and even death [2]. Despite their benign nature, NF2-associated 2. Materials and Methods VSs have poor prognosis prone to recurrence, and there are no curative treatments. At present, the primary treatments 2.1. Microarray Datasets. ,e gene expression profile are follow-up observation, microsurgery, and radiosurgery GSE108524 of the NF2-associated VSs and normal nerve which are not always effective and sometimes cause neu- groups was obtained from the NCBI GEO database. ,ese rological deficits [3]. Patients with hearing loss sometimes microarray data were based on GPL17586 Platform [HTA- 2 Journal of Oncology

OLR1 3 Volcano plot HLA-DRB3 GPR83 16.34 RAB31 SLC24A2 CCL3L3 MIR4521 FCGBP CCL3 13.07 CDH2 LOC285758 SPP1 C10orf114 CCL4 SORL1 9.8 LINC00968 HBA1 L1CAM ALCAM IL1B IFI6 6.54 HLA-DQA1 P2RY13

–log10 ( P value) LAPTM5 EPB41L4B FCGR1C 7 RNU4-9P 3.27 JAKMIP2 CCL4L1 GPR155 LY86 SEMA7A CYBB 0 FCGR3A FAM26E –8.12 –5.79 –3.46 –1.12 1.21 3.54 5.87 RNA5SP110 ALOX5AP CX3CR1 log2 (fold change) EDA2R FCGR1B PTPRJ SLC38A1 HBA2 PAPPA NRG1 STX11 LOC646543 RNU6-51 GPR65 NCAM1 EBF1 11 AKR1C2 AOC3

OR6B3 Gene expression CDO1 CAV1 ABCC9 MIR675 ADH1C SYNPO2 FIBIN PDGFRA BMP3 TM4SF1 VIT MYOC SFRP4 KLF5 CYP4X1 FLRT2 CFH ADIPOQ STEAP4 DRP2 FMOD PRELP 15 OMD CFD CXCL14 MME MYH11 PLN FAIM2 SLC27A6 GJC3 CLDN1 EBF2 ANGPTL7 PALMD FHL5 ASPN THBS4 MEOX2 PDZRN4 CCDC80 SLC2A1 FABP4 ADH1B SLC22A3 SFRP2 19 GSM2893469 GSM2893470 GSM2893471 GSM2893472 GSM2902750 GSM2902751 GSM2902752 GSM2902753 GSM2902754 GSM2902755 GSM2902756 GSM2902757 GSM2902758 GSM2902759 GSM2902760 GSM2902761 GSM2902762 GSM2902763 GSM2902764 GSM2902765 GSM2902766 (a) (b)

Figure 1: (a) DEGs were selected by volcano plot ﬁltering (|fold change | ≥ 1.5 and adj. P < 0.05). (b) ,e heat map of DEGs in NF2-associated VSs (top 100 upregulated and downregulated genes). Green represents a downregulated expression, and red indicates an upregulated level.

2_0] Affymetrix Human Transcriptome Array 2.0 [transcript 2.3. Functional and Pathway Enrichment Analysis of DEGs. (gene) version], including 17 NF2-associated VSs tissues and ,e Database for Annotation, Visualization, and Integrated 4 normal nerves. Discovery (DAVID) (Version 6.8, https://david.ncifcrf.gov/) was used to perform GO and KEGG pathway enrichment analysis of DEGs. GO analysis contains biological process 2.2. Identification of DEGs. GEO2R (http://www.ncbi.nlm. (BP), cellular component (CC), and molecular function nih.gov/geo/geo2r/), a web tool based on the analysis of (MF). GO term with the criterion of P < 0.05 and false variance or t-test, was used to identify DEGs between NF2- discovery rate (FDR) < 0.05 and KEGG pathway analysis associated VSs tissues and normal nerves. ,e DEGs were with the criterion of P < 0.05 were considered statistically identified as the genes with |log FC| ≥ 1.5 and adj. P < 0.05. significant. Journal of Oncology 3

Table 1: GO analysis of diﬀerentially expressed genes. Category Term Count P value FDR BP GO:0007155 ∼ cell adhesion 38 <0.001 <0.001 BP GO:0006954 ∼ inﬂammatory response 34 <0.001 <0.001 BP GO:0006955 ∼ immune response 32 <0.001 <0.001 BP GO:0007165 ∼ signal transduction 58 <0.001 0.0054 BP GO:0051897 ∼ positive regulation of protein kinase B signaling 12 <0.001 0.0203 BP GO:0070374 ∼ positive regulation of ERK1 and ERK2 cascade 17 <0.001 0.0261 BP GO:0043547 ∼ positive regulation of GTPase activity 34 <0.001 0.0265 BP GO:0030198 ∼ extracellular matrix organization 18 <0.001 0.0285 BP GO:0030335 ∼ positive regulation of cell migration 17 <0.001 0.0488 CC GO:0005615 ∼ extracellular space 87 <0.001 <0.001 CC GO:0005887 ∼ integral component of plasma membrane 88 <0.001 <0.001 CC GO:0005886 ∼ plasma membrane 177 <0.001 <0.001 CC GO:0005578 ∼ proteinaceous extracellular matrix 31 <0.001 <0.001 CC GO:0009986 ∼ cell surface 42 <0.001 <0.001 CC GO:0070062 ∼ extracellular exosome 121 <0.001 <0.001 CC GO:0005576 ∼ extracellular region 81 <0.001 <0.001 CC GO:0031012 ∼ extracellular matrix 28 <0.001 <0.001 CC GO:0009897 ∼ external side of plasma membrane 21 <0.001 <0.001 CC GO:0045121 ∼ membrane raft 20 <0.001 0.0020 CC GO:0016021 ∼ integral component of membrane 179 <0.001 0.0020 CC GO:0005925 ∼ focal adhesion 27 <0.001 0.0122 CC GO:0045202 ∼ synapse 17 <0.001 0.0224 MF GO:0008201 ∼ heparin binding 20 <0.001 <0.001 MF GO:0005178 ∼ integrin binding 14 <0.001 0.0041

2.4. Protein-Protein Interaction (PPI) Network Analysis. SYBR Premix Ex Taq II (Takara) was used for qPCR. We We submitted DEGs in Search Tool for the Retrieval of used several sequences: EGFR forward primer 5′-CTA- Interacting Genes database (STRING, http://www.string-db. CAACCCCACCACGTACC-3′ and reverse primer 5′- org/) to screen the PPI pairs with a combined score of ≥0.4 CGCACTTCTTACACTTGCGG-3′; GAPDH forward and visualized the interaction using Cytoscape software primer 5′-CTTCGCTCTCTGCTCCTCCTGTTCG-3′ and (Version 3.7.0.). Finally, CentiScaPe and Molecular Com- reverse primer 5′-ACCAGGCGCCCAATACGACCAAAT- plex Detection (MCODE), a Cytoscape plugin, were utilized 3. ,e results were calculated using the 2−ΔΔCt method. to screen PPI network key genes. ,e default parameters of MCODE were used: degree cutoff ≥2, node score cutoff ≥0.2, k-score ≥ 2, and maximum depth � 100. 2.8. Statistical Analysis. Statistical analysis was conducted by SPSS 20.0 software. ,e statistical significance between groups was determined using a two-tailed Student’s t-test. 2.5. Drug-Gene Interaction Analysis. To better identify po- Values of P < 0.05 were considered to indicate statistically tential targeted therapeutic drugs for NF2-associated VSs, significant differences. the hub genes were mapped onto the Drug-Gene Interaction database (DGIdb; http://www.dgidb.org) to obtain potential 3. Results drug target genes and potential NF2-associated VSs treatment drugs. Visualization of the drug-gene interaction was 3.1.IdentificationofDEGs. A total of 542 DEGs, including 13 generated using Cytoscape software (Version 3.7.0.). In upregulated and 329 downregulated genes, were identified addition, ClinicalTrials.gov (https://clinicaltrials.gov) was by comparing 17 NF2-associated VSs tissues and 4 normal used to identify whether drugs have been previously in- nerves from GSE108524. ,e heat map and volcano plot vestigated or are being currently tested in clinical trials. showed these DEGs (Figure 1).

2.6. Human NF2-Associated VSs Specimens. Human NF2- 3.2. Functional Annotation and Pathway Enrichment Analysis associated VSs tissues with the matched normal adjacent of DEGs. GO functional annotation revealed that the DEGs specimens were obtained from the Second Hospital of were signiﬁcantly enriched in BP terms including cell ad- Guangxi Medical University. ,is study was approved by the hesion, inﬂammatory response, immune response, signal Ethics Committee of the Second Hospital of Guangxi transduction, positive regulation of protein kinase B sig- Medical University. naling, positive regulation of ERK1 and ERK2 cascade, and positive regulation of GTPase activity. In addition, the CC terms mainly showed plasma membrane, extracellular 2.7. Quantitative PCR (qPCR). Reverse transcription was exosome, extracellular region, extracellular matrix, and carried out using SYBR premix EX Taq (Takara, Japan), and membrane raft. MF enrichment indicated heparin binding 4 Journal of Oncology

Table 2: KEGG pathway analysis of differentially expressed genes. P Term Count Genes value hsa05150: staphylococcus aureus C3AR1, C3, HLA-DRB3, FPR1, C1R, C1S, HLA-DQA1, FCGR1A, CFH, 13 <0.001 infection FCGR3A, CFD, SELPLG, FCGR3B MRC1, NOS1, OLR1, C3, TUBB2A, HLA-DRB3, TLR2, HLA-C, C1R, TLR6, hsa04145: phagosome 19 <0.001 HLA-DQA1, CYBB, CD36, FCGR1A, COMP, CLEC7A, FCGR3A, FCGR3B, THBS4 hsa04514: cell adhesion molecules CLDN19, HLA-DRB3, HLA-C, L1CAM, NLGN3, CDH2, HLA-DQA1, CDH5, 16 <0.001 (CAMs) ALCAM, NCAM1, CD86, CD34, ITGA8, CLDN1, CD4, SELPLG CR1, CD37, CD36, CD34, HLA-DRB3, FCGR1A, MME, IL1B, CD4, ANPEP, hsa04640: hematopoietic cell lineage 12 <0.001 CSF2RA, CSF1R hsa05144: malaria 9 <0.001 CR1, CD36, COMP, TLR2, IL1B, HBA2, HBA1, HBB, THBS4 hsa04610: complement and 10 <0.001 C3AR1, VWF, CR1, C3, F13A1, CFH, TFPI, C1R, C1S, CFD coagulation cascades PIK3CG, EGFR, CAV1, TNXB, TNC, FLNB, MYL9, VWF, CCND1, PAK3, hsa04510: focal adhesion 17 0.0017 CCND2, COMP, ITGA8, COL6A3, PDGFRA, SPP1, THBS4 hsa04060: cytokine-cytokine receptor EGFR, CCL3, TGFBR1, LIFR, EDA2R, CCL4L1, CCL4, CXCL12, IL17RA, LEP, 18 0.0022 interaction PPBP, CXCL14, CCL3L3, CX3CR1, PDGFRA, IL1B, CSF2RA, CSF1R hsa05140: leishmaniasis 9 0.0027 CR1, C3, HLA-DRB3, FCGR1A, TLR2, IL1B, FCGR3A, FCGR3B, HLA-DQA1 EGFR, PIK3CG, FGF7, TNXB, TNC, TLR2, FGF10, IRS1, DDIT4, VWF, CCND1, hsa04151: PI3K-akt signaling pathway 23 0.0033 LPAR5, CCND2, COMP, ITGA8, COL6A3, PDGFRA, MDM2, ANGPT1, FGF1, SPP1, THBS4, CSF1R hsa00350: tyrosine metabolism 6 0.0063 MAOA, AOX1, ADH1C, ADH1B, ADH1A, AOC3 hsa03320: PPAR signaling pathway 8 0.0075 LPL, CD36, OLR1, PLIN1, SLC27A6, FABP4, ACADL, ADIPOQ hsa04512: ECM-receptor interaction 9 0.0094 VWF, CD36, TNXB, COMP, TNC, ITGA8, COL6A3, SPP1, THBS4 hsa04620: Toll-like receptor signaling 10 0.0100 PIK3CG, CD86, CCL3, CCL3L3, TLR2, CCL4L1, IL1B, TLR6, CCL4, SPP1 pathway CD86, CCL3, HLA-DRB3, CCL3L3, TLR2, IL1B, ANGPT1, CXCL12, HLA- hsa05323: rheumatoid arthritis 9 0.0101 DQA1 hsa05218: melanoma 8 0.0103 PIK3CG, EGFR, CCND1, FGF7, PDGFRA, MDM2, FGF10, FGF1 FYB, PIK3CG, EGFR, FGF7, FPR1, FGF10, APBB1IP, DOCK4, PLCB4, LPAR5, hsa04015: Rap1 signaling pathway 15 0.0126 RASGRP3, PDGFRA, ANGPT1, FGF1, CSF1R hsa05416: viral myocarditis 7 0.0126 CAV1, CD86, CCND1, HLA-DRB3, SGCD, HLA-C, HLA-DQA1 hsa04730: long-term depression 7 0.0160 PLA2G4A, PLCB4, NOS1, GRIA2, LYN, GUCY1A2, GUCY1B3 EGFR, TNXB, CYP1B1, TNC, MIRLET7F1, MIR99A, ZEB1, MIR222, MIR221, hsa05206: microRNAs in cancer 18 0.0176 IRS1, DDIT4, NOTCH3, CCND1, CCND2, PDGFRA, MDM2, MARCKS, MIR181B2 MRC1, CR1, ITGAX, C3, FCGR1A, HLA-DRB3, TLR2, IL1B, CLEC7A, FCGR3A, hsa05152: tuberculosis 13 0.0178 TLR6, FCGR3B, HLA-DQA1 PIK3CG, EGFR, CAV1, LUM, FZD1, TLR2, DCN, FLNB, CCND1, CBLB, GPC3, hsa05205: proteoglycans in cancer 14 0.0191 RRAS2, MDM2, PTCH1 hsa05143: African trypanosomiasis 5 0.0250 PLCB4, IL1B, HBA2, HBA1, HBB hsa05332: graft-versus-host disease 5 0.0250 CD86, HLA-DRB3, IL1B, HLA-C, HLA-DQA1 hsa05142: Chagas disease (American 9 0.0254 PIK3CG, CCL3, PLCB4, C3, TGFBR1, CCL3L3, TLR2, IL1B, TLR6 trypanosomiasis) EGFR, PIK3CG, FGF7, TGFBR1, FZD1, RUNX1T1, FGF10, CXCL12, CBLB, hsa05200: pathways in cancer 22 0.0267 CCND1, PLCB4, LPAR5, RASGRP3, SLC2A1, PDGFRA, MDM2, PTCH1, PTCH2, HHIP, FGF1, CSF2RA, CSF1R hsa04810: regulation of actin PIK3CG, EGFR, FGF7, FGF10, NCKAP1L, MYL9, ARPC1B, ITGAX, CHRM3, 14 0.0282 cytoskeleton PAK3, ITGA8, RRAS2, PDGFRA, FGF1 PIK3CG, CYBB, FCGR1A, TGFBR1, IL1B, FCGR3A, TREM2, FCGR3B, CSF1R, hsa04380: osteoclast differentiation 10 0.0349 BLNK and integrin binding (Table 1). Furthermore, KEGG path- STRING database (Figure 2(a)). We identified 28 hub way enrichment analysis revealed focal adhesion, PI3K-Akt genes with connectivity degree ≥20 (Figure 2(b), Table 3). signaling pathway, ECM-receptor interaction, Toll-like re- ,en, using MCODE, three modules with scores >4.5 and ceptor signaling pathway, Rap1 signaling pathway, and a number of nodes >18 were selected. Module 1 with a regulation of actin cytoskeleton (Table 2). score of 9.368 consisted of 20 nodes and 89 edges (Figure 2(c)), module 2 with a score of 4.588 comprised 18 nodes and 39 edges (Figure 2(d)), and module 3 with a 3.3. PPI Network Analysis. In total, we made the PPI score of 4.455 comprised 23 nodes and 49 edges network of 369 nodes and 1,322 edges, based on the (Figure 2(e)). Journal of Oncology 5

GABRA2MOB3BPODNLRRC32LRIG3LGR5SPOCK1 SDPRABCA10ABCC9 CYP4X1CYP4B1GPR133 LGI2CASQ2 KCNK2RERGL PRRX1TFPI RERGABCA9 FNDC1SVEP1 CCDC80SHOX2 ECM2 LDB2 TXNIP CLIC5 ADAMTS1 SHISA6 IL1B CXCL12 FHL5 SPARCL1 LRRC7 SLC27A6 TM4SF1 SLC12A2 ELTD1 SLC7A2 VWF MTSS1 SLIT3 MBP SLC36A2 SPP1 SOX9 PRICKLE1 THRSP MAN1A1 RCAN2 KRT19 ARRDC3 ANGPTL2 MYO1D SRPX USP46 GLRBSLC15A3 IGFBP6 PDGFRA CD37ADAM28PTX3 MTSS1LP2RX7GABRE ITGAX TCF4 LATS2WARS SORL1RNASE2 SLC2A3 TUBB2A SLC38A1 RUNX1T1 LACTB C12orf5 PDLIM1 MYOM1 CHD7 LY86 DARC TRPM3 OLR1 GPX1 FUT8 FRAS1 MYOC ANKRD22 DDIT4 NALCN DUSP6FYB CYP1B1 TREM2 SELPLG BMP3 DCN NDP C1orf162 CLEC9A FAM162B CSF1R IL33 ALOX5AP PCDH15 CPA3 FOLH1 SCN2A MARCKS PLEKHH2 ZNF521 ANGPTL1 ADAP2 SEMA7A RASD1 TSPAN8 ARHGAP21 NR2F1 NAALAD2 FHL1 DOCK8 HHIP IL17RA ELOVL7 ARHGAP10 SAMD9L KCNB2 FILIP1 CD86 CD34 ASPN HLA-DQA1 SLC2A5 CPXM2 NEXN RASGRP3 HAS2 SLFN12 SLC2A1 CD53 CRIM1 RASSF4 AKR1C2 LAPTM5 LGI1 AKR1C1 FRMD4A MYCT1 PREX1 TAGLN BIN2 PAWR PTPRG KIF5A FAIM2 CCND1 AGAP5 MYO16 CDO1 MID1 CRISPLD2 ACACB CLEC5A FLNB REC8 MAPK8IP1 MYO5C EMB ERG FLRT3 MYO1B ZNF347 HCN1 COMP GPR83 SVIP CCL3L3 TNXB CCND2 BNC2 ZEB1 IER3 VIT NCAM1 CCND1 SFRP4 XPO6 CES1 ACTA2 ANPEP GPR65 XG C3 SFRP2 CR1 ARPC1B CUX1 FZD1 HBB CFH NCKAP1L DPT HBA1 MYH11 EPB41L3 EDN1 CXCL14 ACTA2 HBA2 C3 THBS4 NLGN3 ACTG2 PIK3CG RASGRF2 PAPPA LEP LIFR RRAS2 ADH1B PAPPA-AS1 CNN1 IRS1 ADH1A PTPRB APBB1IP EGFR RSAD2 TNNC1 LGALS3BP CBLB TLR2 CD34 IFIT3 EFNB1 HBA1 QSOX1 PDGFRL C10orf116 LGALS9 CAV1 PLIN4 ALCAM CFD HAVCR2 ANGPT1 CYBB MYL9 IFI6 ACADL P2RY12 ADIPOQ ACTG2 TIMP4 EPHA5 FABP4 CSF1R AOC3 LYN GLDC FCGR3B LPAR5 MME NRG1 CD36 DPP4 P2RY13 EDN1 FCGR3A KIF5C FCGR1B NOS1 FCGR3B NT5E SPP1 LPL MTHFD1L PDE7B CX3CR1 C1S PIK3CG PCDH7 TLR6 C1R CD36 PLA2G4A TLR2 FLRT2 NTRK2 NOTCH3 CLDN19 IL1B TGFBR1 JAG1 PELI2 CHRM3 C3AR1 MYLIP NEDD4L PAPSS2 SYNE2 FPR1 BLNK TJP2 PDE3A MDM2 VLDLR CLDN1 FCGR3A PAK3 PLCH1 FCGR1A EPS8 CD4 L1CAM LEP PRKAR2B CCL3 HLA-C TGFBR3 COL6A3 CLEC7A NCAM1 MFAP5 PTPRJCSF2RA ITGAXCD86 PTCH2 MMP14GLUD2PPBPCCL4CCL4L1CDH2 GRIA2 CALCRL LMOD1 RRAS2 CD109 ACACB FGFBP2 PLIN1 CAV1 CDH7 MS4A2 LYN FBXO32 DCN LMO7 LUM EGFR FGF10 FBLN1 OMD VWF NOX4 GLUL AVPR1A AOX1 P2RY14 FGF1 PLN NRP1 MAOA FGF7 ERRFI1 CDH5 EBF1KLF5 GUCY1A2CXCL12 PTGFRSTON2 FBLN2GUCY1B3 S1PR1TNC IL16MRC1 ITGA8MLLT4 PTCH1GPC3 PRXDRP2PLCB4PRELPFMODPTGISPTGDSPDGFRAF13A1 (a) (b)

FCGR1A CAV1

CD36 FGF1 CD86 TLR2 CDH5 F13A1 ITGAX FCGR3A EGFR ANGPT1 CCL3 LGALS3BP FCGR3B CD34 LYN VWF CX3CR1 CCL4 QSOX1 FPR1 CXCL12

C3 PPBP TAGLN MYH11

FMOD LPAR5 S1PR1 P2RY14 LMOD1 PRKAR2B CNN1 P2RY12 P2RY13 C3AR1 PRELP

SOX9 MYLIP

FBXO32 SPP1 LMO7 PTCH1

TGFBR1 NEDD4L

LEP

MMP14 PIK3CG

FGF10 PDGFRA LUM OMD

ASPN PDE7B MDM2 RRAS2

ECM2

LATS2

PAK3

ACTG2

(e)

Figure 2: (a) ,e PPI network of DEGs. (b) ,e hub genes with connectivity degree ≥20. (c) Module 1. (d) Module 2. (e) Module 3. Green represents a downregulated expression, and red indicates an upregulated level.

3.4. Drug-Gene Interaction Analysis. Based on the DGIdb, Table 3: 28 hub genes with connectivity degree ≥20. we use the 28 hub genes to screen for drug-gene interactions, Number Gene Degree of connectivity Regulation which revealed that 31 drugs associated with 12 key genes 1 EGFR 59 Down may be potential NF2 treatment drugs (Figure 3). Based on 2 IL1B 53 Up ClinicalTrials.gov, we found that nilotinib was previously 3 PIK3CG 49 Up investigated for Phase 2 of growing VSs treatment and 4 CSF1R 40 Up everolimus is being used in Phase 2 of the NF2 treatment 5 CXCL12 39 Down study. 6 CD34 36 Down 7 EDN1 36 Down 8 ITGAX 34 Up 3.5. mRNA Expression Levels of EGFR. qPCR analysis veri- 9 ACACB 34 Down fied EGFR mRNA underexpression levels in the NF2-as- 10 LYN 32 Up sociated VSs tissues (Figure 4). 11 FCGR3A 32 Up 12 DCN 30 Down 4. Discussion 13 CD36 30 Down 14 VWF 30 Down In this study, we found that the 28 hub genes had been 15 CD86 29 Up insufficiently studied or not studied at all in VSs, 12 of which 16 TLR2 29 Up 17 ACTA2 29 Down may be target genes for potential NF2 treatment drugs. 18 LEP 29 Down Among these genes, IL1B, PIK3CG, CSF1R, LYN, FCGR3A, 19 FCGR3B 26 Up FCGR3B, SPP1, and CCND1 were upregulated in NF2-as- 20 NCAM1 25 Up sociated VSs, while EGFR, DCN, VWF, and PDGFRA were 21 CAV1 24 Down downregulated. ,en, LYN, FCGR3A, and FCGR3B are 22 HBA1 23 Up involved in “module 1” of the subnetwork, in which GO 23 ACTG2 22 Down functional annotation is enriched in inflammatory response 24 SPP1 21 Up and immune response, and KEGG pathway enrichment 25 C3 21 Up analysis is enriched in staphylococcus aureus infection, 26 PDGFRA 20 Down phagosome, and osteoclast differentiation. EGFR and VWF 27 CCND1 20 Up are involved in “module 2,” which is enriched in focal ad- 28 RRAS2 20 Up hesion and PI3K-Akt signaling pathway. PIK3CG and SPP1 are involved in “module 3,” which is also enriched in focal VWF were significantly enriched in PI3K-Akt signaling adhesion and PI3K-Akt signaling pathway. pathway involved in VSs development, which can increase We found that upregulated genes PIK3CG, CSF1R, schwannoma cell proliferation, survival, and cell-matrix SPP1, and CCND1 and downregulated genes EGFR and adhesion acting [4–6]. ,at may be the cause of poor Journal of Oncology 7

CYCLOPHOSPHAMIDE RITUXIMAB

PREDNISONE IBRITUMOMAB TIUXETAN TOSITUMOMAB FCGR3A

STREPTOZOTOCIN ALEMTUZUMAB THALIDOMIDE FCGR3B GALLIUM NITRATE VWF

CYTARABINE VINCRISTINE MITOMYCIN IL1B PALBOCICLIB

TACROLIMUS IDELALISIB CANAKINUMAB PIK3CG CCND1

SPP1 MYCOPHENOLIC ACID

IRINOTECAN TEMSIROLIMUS PACLITAXEL

TRETINOIN SORAFENIB EVEROLIMUS NILOTINIB EGFR SUNITINIB PDGFRA SIROLIMUS CARBOPLATIN PAZOPANIB CSF1R TEMOZOLOMIDE

DCN IBRUTINIB DASATINIB

LYN

Figure 3: Drug-gene interactions of hub genes.

∗ 5 a utility of some tumors progression and metastasis, sug- gesting a poor prognosis, such as breast cancer [7]. Morrow et al. study [7] revealed that OPN-initiated signaling induced 4 Akt-mediated phosphorylation and degradation of merlin in breast cancer cells; it was reported for the first time that OPN 3 is involved in merlin protein degradation. We showed that SPP1 is upregulated in NF2-associated VSs, consistent with the result of Torres-Martin et al. [8]. SPP1 may be a bio- 2 marker of NF2-associated VSs, whose interaction with merlin has not been reported in NF2-associated VSs. Fur- 1 thermore, we found that drugs associated with SPP1, including tacrolimus and tretinoin, may be potential therapeutic agents for NF2-associated VSs, which require a 0 one-step study. Tacrolimus, a powerful immunosuppressant, Normal tissues VSs tissues significantly increased OPN mRNA and protein expression Figure 4: ,e mRNA expression levels of EGFR (∗P < 0.05). from kidney tissue and renal cells, which may contribute to nephrotoxicity inducing [9]. However, tacrolimus used to treat autoimmunity blocks IL2 production and is used for prognosis in NF2-associated VSs. ,e drugs that inhibit the active rheumatoid arthritis [10] and lupus nephritis [11]. PI3K-Akt signaling pathway may be a potential therapeutic Based on functional annotation and pathway enrichment strategy for NF2 by antitumor activity against NF2-related analysis of DEGs, inflammatory response, immune re- tumor cells. sponse, melanoma, and rheumatoid arthritis may be con- Merlin, a tumor suppressor, is constantly inactivated in nected with NF2-associated VSs development. ,erefore, NF2-associated VSs. SPP1, also known as osteopontin tacrolimus may be used for NF2-associated VSs treatment. (OPN), is a secreted, integrin-binding phosphoprotein. OPN In our study, CCND1 involved in apoptosis and cell cycle had been insufficiently studied in VSs, while elevated OPN is control, a key cell cycle regulatory protein, was upregulated 8 Journal of Oncology in NF2-associated VSs, which is consistent with previous preservation,” Journal of Neuro-Oncology, vol. 138, no. 2, studies [12, 13]. Elevated CCND1 is known to suggest poor pp. 417–424, 2018. prognosis in many cancers, such as colorectal cancer [14], [4] K. Tanaka, A. Eskin, F. Chareyre et al., “,erapeutic potential breast cancer [15], and multiple myeloma [16, 17]. We found of HSP90 inhibition for neurofibromatosis type 2,” Clinical drugs associated with CCND1, including palbociclib and Cancer Research, vol. 19, no. 14, pp. 3856–3870, 2013. mycophenolic acid, which had not been studied in VSs. [5] S. Agnihotri, I. Gugel, M. Remke et al., “Gene-expression Palbociclib, a cyclin-dependent kinase 4 and 6 (CDK4/6) profiling elucidates molecular signaling networks that can be therapeutically targeted in vestibular schwannoma,” Journal inhibitor, prolongs progression-free survival among patients of Neurosurgery, vol. 121, no. 6, pp. 1434–1445, 2014. with advanced estrogen receptor-positive and HER2-nega- [6] L. Provenzano, Y. Ryan, D. A. Hilton et al., “Cellular prion tive breast cancer [18, 19]. Mycophenolic acid, an immu- protein (PrPC) in the development of merlin-deficient tu- nosuppressant, can inhibit proliferation and induce mours,” Oncogene, vol. 36, no. 44, pp. 6132–6142, 2017. apoptosis in cancer cells, which may be caused by inhibition [7] K. A. Morrow, S. Das, B. J. Metge et al., “Loss of tumor of upregulation of CCND1 and the PI3K/AKT/mTOR suppressor merlin in advanced breast cancer is due to post- pathway [20]. 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