Journal of Oncology
Volume 2020 (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, The Second Affiliated Hospital of Guangxi Medical University, Nanning 530007, China

Correspondence should be addressed to Shi-Hua Yin; shihuayin@126.com

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. This 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.

Abstract.

Neurofibromatosis Type 2- (NF2-) associated vestibular schwannomas (VSs) are histologically benign tumors. This 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. The 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

VSs, also known as acoustic neuromas, are histologically benign tumors originating from the eighth nerve. NF2 is a rare autosomal dominant inherited disorder tumor caused by deletion or loss-of-function mutations in the NF2 gene encoding merlin [1]. The main characteristic of NF2-associated VSs is the bilateral schwannomas of the vestibular nerve, which leads to sensorineural hearing loss, facial paralysis, vestibular dysfunction, brainstem compression, and even death [2]. Despite their benign nature, NF2-associated VSs have poor prognosis prone to recurrence, and there are no curative treatments. At present, the primary treatments are follow-up observation, microsurgery, and radiosurgery which are not always effective and sometimes cause neurological deficits [3]. Patients with hearing loss sometimes accept the otolaryngology surgery and require improving or saving hearing. With the targeted molecular therapies becoming increasingly common, drug therapy has gradually become possible. Therefore, it is urgently required to determine effective drug targets for NF2-associated VSs therapies. The present study aimed to determine disease-related genes, pathways, and potential targeted therapeutic drugs associated with NF2-associated VSs using the bioinformatics method.

2. Materials and Methods

2.1. Microarray Datasets

The gene expression profile GSE108524 of the NF2-associated VSs and normal nerve groups was obtained from the NCBI GEO database. These microarray data were based on GPL17586 Platform [HTA-2_0] Affymetrix Human Transcriptome Array 2.0 [transcript (gene) version], including 17 NF2-associated VSs tissues and 4 normal nerves.

2.2. Identification of DEGs

GEO2R (http://www.ncbi.nlm.nih.gov/geo/geo2r/), a web tool based on the analysis of variance or t-test, was used to identify DEGs between NF2-associated VSs tissues and normal nerves. The DEGs were identified as the genes with |log FC| ≥ 1.5 and adj. .

2.3. Functional and Pathway Enrichment Analysis of DEGs

The Database for Annotation, Visualization, and Integrated 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 (BP), cellular component (CC), and molecular function (MF). GO term with the criterion of and false discovery rate (FDR) < 0.05 and KEGG pathway analysis with the criterion of were considered statistically significant.

2.4. Protein-Protein Interaction (PPI) Network Analysis

We submitted DEGs in Search Tool for the Retrieval of Interacting Genes database (STRING, http://www.string-db.org/) to screen the PPI pairs with a combined score of ≥0.4 and visualized the interaction using Cytoscape software (Version 3.7.0.). Finally, CentiScaPe and Molecular Complex Detection (MCODE), a Cytoscape plugin, were utilized to screen PPI network key genes. The default parameters of MCODE were used: degree cutoff ≥2, node score cutoff ≥0.2, k-score  ≥ 2, and maximum depth = 100.

2.5. Drug-Gene Interaction Analysis

To better identify potential targeted therapeutic drugs for NF2-associated VSs, the hub genes were mapped onto the Drug-Gene Interaction database (DGIdb; http://www.dgidb.org) to obtain potential drug target genes and potential NF2-associated VSs treatment drugs. Visualization of the drug-gene interaction was generated using Cytoscape software (Version 3.7.0.). In addition, ClinicalTrials.gov (https://clinicaltrials.gov) was used to identify whether drugs have been previously investigated or are being currently tested in clinical trials.

2.6. Human NF2-Associated VSs Specimens

Human NF2-associated VSs tissues with the matched normal adjacent specimens were obtained from the Second Hospital of Guangxi Medical University. This study was approved by the Ethics Committee of the Second Hospital of Guangxi Medical University.

2.7. Quantitative PCR (qPCR)

Reverse transcription was carried out using SYBR premix EX Taq (Takara, Japan), and SYBR Premix Ex Taq II (Takara) was used for qPCR. We used several sequences: EGFR forward primer 5′-CTACAACCCCACCACGTACC-3′ and reverse primer 5′-CGCACTTCTTACACTTGCGG-3′; GAPDH forward primer 5′-CTTCGCTCTCTGCTCCTCCTGTTCG-3′ and reverse primer 5′-ACCAGGCGCCCAATACGACCAAAT-3. The results were calculated using the 2−ΔΔCt method.

2.8. Statistical Analysis

Statistical analysis was conducted by SPSS 20.0 software. The statistical significance between groups was determined using a two-tailed Student’s t-test. Values of were considered to indicate statistically significant differences.

3. Results

3.1. Identification of DEGs

A total of 542 DEGs, including 13 upregulated and 329 downregulated genes, were identified by comparing 17 NF2-associated VSs tissues and 4 normal nerves from GSE108524. The heat map and volcano plot showed these DEGs (Figure 1).

(a)

(b)

Figure 1: (a) DEGs were selected by volcano plot filtering (|fold change | ≥  1.5 and adj. ). (b) The 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.

3.2. Functional Annotation and Pathway Enrichment Analysis of DEGs

GO functional annotation revealed that the DEGs were significantly enriched in BP terms including cell adhesion, inflammatory response, immune response, signal transduction, positive regulation of protein kinase B signaling, positive regulation of ERK1 and ERK2 cascade, and positive regulation of GTPase activity. In addition, the CC terms mainly showed plasma membrane, extracellular exosome, extracellular region, extracellular matrix, and membrane raft. MF enrichment indicated heparin binding and integrin binding (Table 1). Furthermore, KEGG pathway enrichment analysis revealed focal adhesion, PI3K-Akt signaling pathway, ECM-receptor interaction, Toll-like receptor signaling pathway, Rap1 signaling pathway, and regulation of actin cytoskeleton (Table 2).

Table 1: GO analysis of differentially expressed genes.

Category
Term
Count
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

Table 2: KEGG pathway analysis of differentially expressed genes.

Term
Count
value
Genes

hsa05150: staphylococcus aureus infection
13
<0.001
C3AR1, C3, HLA-DRB3, FPR1, C1R, C1S, HLA-DQA1, FCGR1A, CFH, FCGR3A, CFD, SELPLG, FCGR3B

hsa04145: phagosome
19
<0.001
MRC1, NOS1, OLR1, C3, TUBB2A, HLA-DRB3, TLR2, HLA-C, C1R, TLR6, HLA-DQA1, CYBB, CD36, FCGR1A, COMP, CLEC7A, FCGR3A, FCGR3B, THBS4

hsa04514: cell adhesion molecules (CAMs)
16
<0.001
CLDN19, HLA-DRB3, HLA-C, L1CAM, NLGN3, CDH2, HLA-DQA1, CDH5, ALCAM, NCAM1, CD86, CD34, ITGA8, CLDN1, CD4, SELPLG

hsa04640: hematopoietic cell lineage
12
<0.001
CR1, CD37, CD36, CD34, HLA-DRB3, FCGR1A, MME, IL1B, CD4, ANPEP, CSF2RA, CSF1R

hsa05144: malaria
9
<0.001
CR1, CD36, COMP, TLR2, IL1B, HBA2, HBA1, HBB, THBS4

hsa04610: complement and coagulation cascades
10
<0.001
C3AR1, VWF, CR1, C3, F13A1, CFH, TFPI, C1R, C1S, CFD

hsa04510: focal adhesion
17
0.0017
PIK3CG, EGFR, CAV1, TNXB, TNC, FLNB, MYL9, VWF, CCND1, PAK3, CCND2, COMP, ITGA8, COL6A3, PDGFRA, SPP1, THBS4

hsa04060: cytokine-cytokine receptor interaction
18
0.0022
EGFR, CCL3, TGFBR1, LIFR, EDA2R, CCL4L1, CCL4, CXCL12, IL17RA, LEP, PPBP, CXCL14, CCL3L3, CX3CR1, PDGFRA, IL1B, CSF2RA, CSF1R

hsa05140: leishmaniasis
9
0.0027
CR1, C3, HLA-DRB3, FCGR1A, TLR2, IL1B, FCGR3A, FCGR3B, HLA-DQA1

hsa04151: PI3K-akt signaling pathway
23
0.0033
EGFR, PIK3CG, FGF7, TNXB, TNC, TLR2, FGF10, IRS1, DDIT4, VWF, CCND1, 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 pathway
10
0.0100
PIK3CG, CD86, CCL3, CCL3L3, TLR2, CCL4L1, IL1B, TLR6, CCL4, SPP1

hsa05323: rheumatoid arthritis
9
0.0101
CD86, CCL3, HLA-DRB3, CCL3L3, TLR2, IL1B, ANGPT1, CXCL12, HLA-DQA1

hsa05218: melanoma
8
0.0103
PIK3CG, EGFR, CCND1, FGF7, PDGFRA, MDM2, FGF10, FGF1

hsa04015: Rap1 signaling pathway
15
0.0126
FYB, PIK3CG, EGFR, FGF7, FPR1, FGF10, APBB1IP, DOCK4, PLCB4, LPAR5, 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

hsa05206: microRNAs in cancer
18
0.0176
EGFR, TNXB, CYP1B1, TNC, MIRLET7F1, MIR99A, ZEB1, MIR222, MIR221, IRS1, DDIT4, NOTCH3, CCND1, CCND2, PDGFRA, MDM2, MARCKS, MIR181B2

hsa05152: tuberculosis
13
0.0178
MRC1, CR1, ITGAX, C3, FCGR1A, HLA-DRB3, TLR2, IL1B, CLEC7A, FCGR3A, TLR6, FCGR3B, HLA-DQA1

hsa05205: proteoglycans in cancer
14
0.0191
PIK3CG, EGFR, CAV1, LUM, FZD1, TLR2, DCN, FLNB, CCND1, CBLB, GPC3, 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 trypanosomiasis)
9
0.0254
PIK3CG, CCL3, PLCB4, C3, TGFBR1, CCL3L3, TLR2, IL1B, TLR6

hsa05200: pathways in cancer
22
0.0267
EGFR, PIK3CG, FGF7, TGFBR1, FZD1, RUNX1T1, FGF10, CXCL12, CBLB, CCND1, PLCB4, LPAR5, RASGRP3, SLC2A1, PDGFRA, MDM2, PTCH1, PTCH2, HHIP, FGF1, CSF2RA, CSF1R

hsa04810: regulation of actin cytoskeleton
14
0.0282
PIK3CG, EGFR, FGF7, FGF10, NCKAP1L, MYL9, ARPC1B, ITGAX, CHRM3, PAK3, ITGA8, RRAS2, PDGFRA, FGF1

hsa04380: osteoclast differentiation
10
0.0349
PIK3CG, CYBB, FCGR1A, TGFBR1, IL1B, FCGR3A, TREM2, FCGR3B, CSF1R, BLNK

3.3. PPI Network Analysis

In total, we made the PPI network of 369 nodes and 1,322 edges, based on the STRING database (Figure 2(a)). We identified 28 hub genes with connectivity degree ≥20 (Figure 2(b), Table 3). Then, using MCODE, three modules with scores >4.5 and a number of nodes >18 were selected. Module 1 with a 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 score of 4.455 comprised 23 nodes and 49 edges (Figure 2(e)).

(a)

(b)

(c)

(d)

(e)

Figure 2: (a) The PPI network of DEGs. (b) The 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.

Table 3: 28 hub genes with connectivity degree ≥20.

Number
Gene
Degree of connectivity
Regulation

1
EGFR
59
Down

2
IL1B
53
Up

3
PIK3CG
49
Up

4
CSF1R
40
Up

5
CXCL12
39
Down

6
CD34
36
Down

7
EDN1
36
Down

8
ITGAX
34
Up

9
ACACB
34
Down

10
LYN
32
Up

11
FCGR3A
32
Up

12
DCN
30
Down

13
CD36
30
Down

14
VWF
30
Down

15
CD86
29
Up

16
TLR2
29
Up

17
ACTA2
29
Down

18
LEP
29
Down

19
FCGR3B
26
Up

20
NCAM1
25
Up

21
CAV1
24
Down

22
HBA1
23
Up

23
ACTG2
22
Down

24
SPP1
21
Up

25
C3
21
Up

26
PDGFRA
20
Down

27
CCND1
20
Up

28
RRAS2
20
Up

3.4. Drug-Gene Interaction Analysis

Based on the DGIdb, we use the 28 hub genes to screen for drug-gene interactions, which revealed that 31 drugs associated with 12 key genes may be potential NF2 treatment drugs (Figure 3). Based on ClinicalTrials.gov, we found that nilotinib was previously investigated for Phase 2 of growing VSs treatment and everolimus is being used in Phase 2 of the NF2 treatment study.

Figure 3: Drug-gene interactions of hub genes.

3.5. mRNA Expression Levels of EGFR

qPCR analysis verified EGFR mRNA underexpression levels in the NF2-associated VSs tissues (Figure 4).

Figure 4: The mRNA expression levels of EGFR ().

4. Discussion

In this study, we found that the 28 hub genes had been insufficiently studied or not studied at all in VSs, 12 of which may be target genes for potential NF2 treatment drugs. Among these genes, IL1B, PIK3CG, CSF1R, LYN, FCGR3A, FCGR3B, SPP1, and CCND1 were upregulated in NF2-associated VSs, while EGFR, DCN, VWF, and PDGFRA were downregulated. Then, LYN, FCGR3A, and FCGR3B are involved in “module 1” of the subnetwork, in which GO functional annotation is enriched in inflammatory response and immune response, and KEGG pathway enrichment analysis is enriched in staphylococcus aureus infection, phagosome, and osteoclast differentiation. EGFR and VWF are involved in “module 2,” which is enriched in focal adhesion and PI3K-Akt signaling pathway. PIK3CG and SPP1 are involved in “module 3,” which is also enriched in focal adhesion and PI3K-Akt signaling pathway.

We found that upregulated genes PIK3CG, CSF1R, SPP1, and CCND1 and downregulated genes EGFR and VWF were significantly enriched in PI3K-Akt signaling pathway involved in VSs development, which can increase schwannoma cell proliferation, survival, and cell-matrix adhesion acting [4–6]. That may be the cause of poor prognosis in NF2-associated VSs. The drugs that inhibit the PI3K-Akt signaling pathway may be a potential therapeutic strategy for NF2 by antitumor activity against NF2-related tumor cells.

Merlin, a tumor suppressor, is constantly inactivated in NF2-associated VSs. SPP1, also known as osteopontin (OPN), is a secreted, integrin-binding phosphoprotein. OPN had been insufficiently studied in VSs, while elevated OPN is a utility of some tumors progression and metastasis, suggesting a poor prognosis, such as breast cancer [7]. Morrow et al. study [7] revealed that OPN-initiated signaling induced Akt-mediated phosphorylation and degradation of merlin in breast cancer cells; it was reported for the first time that OPN 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 biomarker of NF2-associated VSs, whose interaction with merlin has not been reported in NF2-associated VSs. Furthermore, we found that drugs associated with SPP1, including tacrolimus and tretinoin, may be potential therapeutic agents for NF2-associated VSs, which require a one-step study. Tacrolimus, a powerful immunosuppressant, significantly increased OPN mRNA and protein expression 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 active rheumatoid arthritis [10] and lupus nephritis [11]. Based on functional annotation and pathway enrichment analysis of DEGs, inflammatory response, immune response, melanoma, and rheumatoid arthritis may be connected with NF2-associated VSs development. Therefore, tacrolimus may be used for NF2-associated VSs treatment.

In our study, CCND1 involved in apoptosis and cell cycle control, a key cell cycle regulatory protein, was upregulated in NF2-associated VSs, which is consistent with previous studies [12, 13]. Elevated CCND1 is known to suggest poor prognosis in many cancers, such as colorectal cancer [14], breast cancer [15], and multiple myeloma [16, 17]. We found drugs associated with CCND1, including palbociclib and mycophenolic acid, which had not been studied in VSs. Palbociclib, a cyclin-dependent kinase 4 and 6 (CDK4/6) inhibitor, prolongs progression-free survival among patients with advanced estrogen receptor-positive and HER2-negative breast cancer [18, 19]. Mycophenolic acid, an immunosuppressant, can inhibit proliferation and induce apoptosis in cancer cells, which may be caused by inhibition of upregulation of CCND1 and the PI3K/AKT/mTOR pathway [20]. Very interestingly, CCND1 was also upregulated in NF2-associated VSs and was significantly enriched in the PI3K-Akt signaling pathway in this study. Thus, palbociclib and mycophenolic acid may inhibit the growth of NF2-associated VSs.

In contrast to SPP1 and CCND1, EGFR was downregulated in NF2-associated VSs, in agreement with the results of Torres-Martin et al. [8], but contrary to those of Yi et al. [21]. At present, the efficacy of EGFR inhibitors in acoustic neuroma treatment is not ideal yet, which may be related to EGFR downregulated in some patients.

In conclusion, with the present analysis, we identified 28 drugs not yet tested in NF2-associated VSs. Tacrolimus, palbociclib, and mycophenolic acid may be candidate drugs. SPP1 and CCND1 may be potential targeted genes in NF2-associated VSs. PI3K-Akt signaling pathway may be involved in VSs development.

Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest

All authors declare that they have no conflicts of interest.

Acknowledgments

The authors greatly appreciate the financial support from the General Program of Natural Science Foundation of Guangxi Province of China (no. 2016GXNSFAA380150) and NSFC Cultivation Project of the Second Affiliated Hospital of Guangxi Medical University (GJPY2019001).

References

J. O. Blakeley, X. Ye, D. G. Duda et al., “Efficacy and biomarker study of bevacizumab for hearing loss resulting from neurofibromatosis type 2-associated vestibular schwannomas,” Journal of Clinical Oncology, vol. 34, no. 14, pp. 1669–1675, 2016.

A. Meola and S. D. Chang, “Bilateral vestibular schwannomas in neurofibromatosis type 2,” New England Journal of Medicine, vol. 379, no. 15, p. 1463, 2018.

F. Zhao, B. Wang, Z. Yang et al., “Surgical treatment of large vestibular schwannomas in patients with neurofibromatosis type 2: outcomes on facial nerve function and hearing preservation,” Journal of Neuro-Oncology, vol. 138, no. 2, pp. 417–424, 2018.

K. Tanaka, A. Eskin, F. Chareyre et al., “Therapeutic potential of HSP90 inhibition for neurofibromatosis type 2,” Clinical Cancer Research, vol. 19, no. 14, pp. 3856–3870, 2013.

S. Agnihotri, I. Gugel, M. Remke et al., “Gene-expression profiling elucidates molecular signaling networks that can be therapeutically targeted in vestibular schwannoma,” Journal of Neurosurgery, vol. 121, no. 6, pp. 1434–1445, 2014.

L. Provenzano, Y. Ryan, D. A. Hilton et al., “Cellular prion protein (PrPC) in the development of merlin-deficient tumours,” Oncogene, vol. 36, no. 44, pp. 6132–6142, 2017.

K. A. Morrow, S. Das, B. J. Metge et al., “Loss of tumor suppressor merlin in advanced breast cancer is due to post-translational regulation,” Journal of Biological Chemistry, vol. 286, no. 46, pp. 40376–40385, 2011.

M. Torres-Martin, L. Lassaletta, J. San-Roman-Montero et al., “Microarray analysis of gene expression in vestibular schwannomas reveals SPP1/MET signaling pathway and androgen receptor deregulation,” International Journal of Oncology, vol. 42, no. 3, pp. 848–862, 2013.

A. Khanna, “Tacrolimus and cyclosporinein vitro and in vivo induce osteopontin mRNA and protein expression in renal tissues,” Nephron Experimental Nephrology, vol. 101, no. 4, pp. e119–e126, 2005.

M. P. Curran and C. M. Perry, “Tacrolimus,” Drugs, vol. 65, no. 7, pp. 993–1001, 2005, discussion 1002-1003.

C. C. Mok, K. Y. Ying, C. W. Yim et al., “Tacrolimus versus mycophenolate mofetil for induction therapy of lupus nephritis: a randomised controlled trial and long-term follow-up,” Annals of the Rheumatic Diseases, vol. 75, no. 1, pp. 30–36, 2016.

P. Rajaraman, S. S. Wang, N. Rothman et al., “Polymorphisms in apoptosis and cell cycle control genes and risk of brain tumors in adults,” Cancer Epidemiology Biomarkers &amp; Prevention, vol. 16, no. 8, pp. 1655–1661, 2007.

J. Jabbour, P. Earls, N. Biggs, G. Gracie, P. Fagan, and R. Bova, “role of cyclins D1 and D3 in vestibular schwannoma,” The Journal of Laryngology & Otology, vol. 130, no. S1, pp. S2–S10, 2016.

M. Xie, F. Zhao, X. Zou, S. Jin, and S. Xiong, “The association between CCND1 G870A polymorphism and colorectal cancer risk: a meta-analysis,” Medicine (Baltimore), vol. 96, no. 42, p. e8269, 2017.

M. A. Bewick, M. S. C. Conlon, and R. M. Lafrenie, “Polymorphisms in XRCC1, XRCC3, and CCND1 and survival after treatment for metastatic breast cancer,” Journal of Clinical Oncology, vol. 24, no. 36, pp. 5645–5651, 2006.

S. Manier, D. Huynh, Y. J. Shen et al., “Inhibiting the oncogenic translation program is an effective therapeutic strategy in multiple myeloma,” Science Translational Medicine, vol. 9, no. 389, 2017.

V. Shah, A. L. Sherborne, B A. Walker et al., “Prediction of outcome in newly diagnosed myeloma: a meta-analysis of the molecular profiles of 1905 trial patients,” Leukemia, vol. 32, no. 1, pp. 102–110, 2018.

N. C. Turner, D. J. Slamon, J. Ro et al., “Overall survival with palbociclib and fulvestrant in advanced breast cancer,” New England Journal of Medicine, vol. 379, no. 20, pp. 1926–1936, 2018.

L. Malorni, G. Curigliano, A. M. Minisini et al., “Palbociclib as single agent or in combination with the endocrine therapy received before disease progression for estrogen receptor-positive, HER2-negative metastatic breast cancer: TREnd trial,” Annals of Oncology, vol. 29, no. 8, pp. 1748–1754, 2018.

S. Domhan, C. Schwager, Q. Wei et al., “Deciphering the systems biology of mTOR inhibition by integrative transcriptome analysis,” Current Pharmaceutical Design, vol. 20, no. 1, pp. 88–100, 2014.

D. Yi, S. Z. Kuo, H. Zheng et al., “Activation of PDGFR and EGFR promotes the acquisition of a stem cell-like phenotype in schwannomas,” Otology & Neurotology, vol. 33, no. 9, pp. 1640–1647, 2012.