As a kind of benign adenomas in the pituitary gland, clinically non-functioning pituitary adenomas (NFPAs) are the most common type of pituitary macroadenomas in adults. The NFPAs account for about 34.0%  of all pituitary adenomas (PAs) that occur at a prevalence rate of 75-94 per 100,000 [1,2]. Patients with NFPAs generally suffer from headaches, hypopituitarism, hypogonadism and visual field defects. Late diagnosis due to inconspicuous signs and symptoms, extension to the cavernous sinus and sellar floor, resistance to pharmacological therapy and high recurrence rate, make their treatment disappointing and challenging . Approximately 80.0% of NFPAs originate from gonadotroph cells (gonadotroph pituitary adenoma, GnPA) , and other NFPAs are mainly associated with null cells (null cell pituitary adenoma, ncPA). The identification of novel therapeutic targets for human NFPAs depend on a good understanding of the molecular mechanism of NFPAs .
Progression in understanding the mechanism of PAs, especially NFPAs, has been achieved over the last several years. According to the reports, germline mutations in AIP or MEN1 genes are associated with young age-onset PAs [6,7]. The HGF and c-MET genes are frequently expressed in PAs, and their expressions are correlated with phos-phorylated Akt expression . Durán-Prado et al.  identified that sst5TMD4, a truncated variant of somatostatin receptor 5, appeared in 85.0% PAs rather than normal pituitary, and it may play an inhibitory role in PAs that possess poor response to somatostatin analogs. Raf/ MEK/ ERK and PI3K/Akt/mTOR signaling pathways are perturbed in NFPAs . As a target of the SF1 gene in gonadotroph cells, CYP11A1 is up-regulated in human GnPA, and Cyp11a1 promotes survival and proliferation of primary cells and cell lines of rat PAs . Rotondi et al.  suggested that the gonadotroph phenotype was strongly associated with AIP expression in NFPAs. The AIP level is higher in GnPA than that in ncPA, and both AIP and cyclinD1 levels are high in most NFPAs. The AIP level correlates with follicle-stimulating hormone β (FSHβ) and cyclinD1 levels in GnPA. However, AIP is not involved in the aggressiveness of NFPAs . Recently, CCNB1 was found to mediate the proliferation-inhibiting role of miR-410, a small non-coding RNA, in GnPA . Additionally, Chesnokova et al.  have identified that human pituitary tumors originated from gonadotroph cells express abundant FOXL2, and both FOXL2 and PTTG promote cluster- ing expression and secretion from gonadotroph cells, thus restraining the proliferation of pituitary cells.
Along with the development of microarray, transcriptome analysis has been widely utilized in understanding tumor mechanism. Based on the gene expression microarray dataset GSE26966, Michaelis et al.  identified that GADD45β, a downstream effector of p53, is a tumor suppressor in gonadotroph tumor. Its overexpression in mouse gonadotroph cells blocks cell proliferation and promotes apoptosis . Based on the same dataset, Cai et al.  identified the coexpressed and altered genes involved in gonadotroph tumors and suggest that ITGA4, MPP2, DLK1, CDKN2A and ASAP2 might be biomarkers. However, pathways or functions of the altered genes were not studied by Michaelis et al. , and the protein-protein interactions (PPIs) between genes were not investigated in the two aforementioned studies [14,15]. In particular, Zhao et al.  performed an integrated analysis of five available microarray datasets of various PAs, to detect 3994 differentially expressed genes (DEGs) (including 2043 up- and 1951 down-regulated genes), and conducted a PPI network analysis. However, PPIs of more DEGs are needed to be analyzed, and more potential novel PAs-related genes are still unknown. Moreover, molecular mechanisms underlying the pathogenesis of PAs, particular NFPAs, remain unclear, and it is still essential to comprehensively investigate and annotate the alterations in gene expression profiles. In the present study, NFPAs-related microarray data uploaded by Michaelis et al.  were analyzed to identify significant DEGs, study NFPAs-related functions and pathways, construct interaction network, and identify potential novel NFPAs-related genes.
Materials and Methods
Microarray dataset of gene expression, GSE26966 , was downloaded from the Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo/ query/acc.cgi?acc=GSE26966). In this dataset, nine normal human pituitary samples were collected from individuals without an endocrine dysfunction at autopsy 2-18 hours post death, and 14 NFPAs samples were obtained from patients at the time of transsphenoidal surgery after obtaining the patient’s or their families’ permission . Moreover, the 14 NFPA samples contained 10 human GnPA samples [histological analysis: >5.0% staining for α-subunit (ASU), follicle-stimulating hormone (FSH) or lutein-izing hormone (LH)] and four ncPA samples (histological analysis: <5.0% staining for ASU, FSH or LH) . Clinical characteristics of tumor samples were: male/female = 8/6, mean age (years) = 61.4, invasive/noninvasive = 7/7, and recurrent/non-recurrent = 5/9. Clinical characteristics of normal controls were: male/female = 4/5 and mean age (years) = 55.9 years that had no significant difference in comparison with tumor samples (p value = 0.39) . Raw microarray data were collected using Affymetrix Human Genome U133 Plus 2.0 Array (http:// www.ncbi.nlm.nih. gov/geo/query/acc.cgi?acc=GPL570) in the previous study .
Pre-Treatment and Differential Analyses
Robust multi-array average algorithm in the affy package (from http://www.bioconductor/org/package/release/bioc/ html/affy.html)  in R was chosen for background correction, data normalization, and calculation of expression values. T-test in package simpleaffy  was performed, and fold change (FC) values were determined. Then, p values were corrected using the Bonferroni method, and corrected p value <0.05 and [log2 FC] >2 were set as the cut-off to identify DEGs. Thereafter, package Pheatmap (https://cran.r-project/org/web/packages/pheatmap/index. html)  in R was utilized to cluster genes and samples based on the expression values of DEGs.
Functional and Pathway Enrichment Analyses
Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were conducted using package GOstats (http://www.biocon ductor.org/packages/release/bioc/GOstats.html) . The p value <0.05 was set as the threshold. User data mapping module in the KEGG database () was utilized to visualize the significantly enriched pathways.
Construction of Protein-Protein Interaction Network
For all of the identified DEGs, a PPI network was constructed with information from a well-known online server, Search Tool for the Retrieval of Interacting Genes/ Proteins version 10 (STRING v10) (http://string-db.org) . Only the PPIs with a confidence score of >0.4 were defined as significant PPIs, which were then utilized to construct the PPI network. The network was visualized using software Cytoscape version 2.8 (http://www.cytoscape.org) , and node degrees were determined.
Potential Novel Non-Functioning Pituitary Adenoma-Related Genes and Sub-Network
In order to find potential novel disease genes, known genes implicated in pituitary tumorigenesis were obtained from the Comparative Toxicogenomics Database (CTD) (the most recently released version was up-dated on February 9 2016, http://ctdbase.org/) . Afterwards, the appearance of these known genes were checked in the PPI network to see whether the known genes were DEGs. Common genes, namely, the overlapped genes, were marked in the PPI network. Other DEGs were defined as potential novel NFPAs-related genes, as they were significantly altered in NFPA specimens and interacted with known disease genes. Furthermore, the top 10 significant DEGs, and DEGs directly interacting with the top DEGs, were extracted to construct a sub-network.
Differentially Expressed Genes and Clusters
A total of 604 DEGs were acquired between NFPAs and controls, involving 177 up- and 427 down-regulated genes. The top 10 up-regulated genes and top 10 down-regulated genes are shown in Table 1. The 604 DEGs and 23 samples were clustered, and DEGs could well differentiate the disease samples from the healthy controls (Figure 1).
Functions and Pathways
The GO enrichment analysis and KEGG pathway analysis were performed to reveal the key biological functions altered in NFPAs. As shown in Table 2, 12 pathways were significantly enriched, which were mainly associated with signaling pathway and receptor interaction. In GO enrichment analysis, DEGs were significantly enriched in 1037 biological process terms mainly about cell communication and signaling, 65 cellular component terms mainly related with an extracellular matrix (ECM), plasma membrane, and collagen, as well as 186 molecular function terms mainly associated with transcription factor activity and receptor binding (Table 2). In order to better understand the positions of DEGs in pathways and their roles in the development of NFPAs, we visualized four significant pathways that had been reported to participate in the pathogenesis of NFPAs or PAs, including MAPK signaling pathway  (Figure 2), p53 signaling pathway  (Figure 3), transforming growth factor β (TGFβ), signaling pathway  (Figure 4), and Jak-STAT signaling pathway  (Figure 5).
Protein-Protein Interaction Network of Differentially Expressed Genes
Potential Novel Non-Functioning Pituitary Adenoma-Related Genes and Sub-Network
Known disease genes were obtained from the CTD database (http://ctd base.org/) and compared with the DEGs in the PPI network. Consequently, 99 up- and 288 down-regulated DEGs were known disease genes, e.g. EGFR (epidermal growth factor receptor, degree = 63) [10,26-28] and ESR1 (estrogen receptor 1, degree = 48)  (Figure 6). In contrast, 16 up- and 17 down-regulated DEGs were potential novel NFPA-related genes, e.g. COL4A5 (collagen type IV α5, degree = 17), LHX3 (LIM homeobox protein 3, degree = 11), MSN (moesin, degree = 11) and GHSR (growth hormone secretagogue receptor, degree = 10) (Figure 6). Moreover, COL4A5 interacted with known NFPA-related genes such as EGFR, LHX3 interacted with known NFPAs-related genes like PRL (Prolactin), and MSN interacted with known NFPA-related genes such as EGFR. Among the top 10 up-regulated genes and top 10 down-regulated genes, only 12 DEGs interacted with other DEGs [e.g. CD-KN2A (cyclin-dependent kinase inhibitor 2A)-IDH1 (isocitrate dehydrogenase 1)], and all 12 DEGs were known disease genes [e.g. DLK1 (δ-like 1 homologue)] (Figure 7). In addition, potential NFPA-related gene GHSR interacted with the top DEG GH1 (growth hormone 1).
Non-functioning pituitary adenomas comprise about 34.0% of pituitary tumors, while their molecular mechanism is still incompletely understood . In the current study, we comprehensively analyzed the gene expression profile of NFPAs and healthy pituitary glands. As a result, 604 DEGs were identified between NFPAs and controls, including 177 up- and 427 down-regulated genes, which were much less than those identified by Michaelis et al. . However, in the current study, we analyzed the same microarray data using different software, algorithms, and analysis criteria (corrected p value <0.05 and [log2 FC] >2) in order to focus on the DEGs that were more significant.
In the current study, mean FC of the up-regulated genes was 6.6, and mean FC of the down-regulated genes was –19.2, which were different from those in the previous study by Michaelis et al.  (4.5 and –32.2, respectively). The differences of mean FC values might be caused by the different DEG sets in the two studies . The major DEGs found by Michaelis et al.  had similar expression change patterns in the current study, e.g. for the PLAGL1, CDKN1A, RPRM, PMAIP1, MDM2, GADD45A, GAD-D45B and GADD45G genes.
Of the top DEGs, DLK1, GH1, CDKN2A and MEG3 were significantly down-regulated in NFPAs in comparison with normal pituitary glands in this study. According to the report, the MEG3 and DLK1-MEG3 locus are silenced in human NFPAs of gonadotroph origin, and DLK1-MEG3 locus plays a tumor suppressor role in NFPAs . Based on proteome data and microarray data or reverse transcription quantitative real-time polymerase chain reaction analysis, Moreno et al.  found that DLK1, GH1 and PRL are down-regulated in NFPAs when compared with normal pituitary glands, whereas IDH1 is significantly up-regulated. The CDKN2A and DLK1 are considered as biomarkers of gonadotroph tumors by Cai et al. , and gene silencing mediated by hypermethylation of the CpG island within exon 1 in CDKN2A is associated with NFPAs . As clearly shown in Figure 7, the expression change patterns of known disease genes DLK1, GH1, PRL, CDKN2A and IDH1, were consistent with the aforementioned studies [30-32], demonstrating the high accuracy of our results.
Expressions of EGFR in NFPAs varied in different studies [10,26-28]. In the current study, EGFR showed low expression in NFPAs (Figure 7), and it interacted with known disease gene CDKN2A, indicating that low expression of EGFR might be associated with NFPAs. We also found that CDKN2A was a top DEG, and it interacted with 22 DEGs in the whole PPI network and most DEGs in the PPI sub-network, suggesting that CDKN2A might play a crucial role in the progression of NFPAs.
Furthermore, potential novel genes were identified (Figure 6), especially COL4A5, LHX3, MSN and GHSR. The role of these genes in NFPAs has not been investigated by previous studies. According to the report, mRNA level of GHSR in NFPAs is lower than that in growth hormone-producing PAs . In the present study, COL4A5, LHX3, MSN and GHSR were significantly down-regulated in NF-PAs in comparison with normal controls, and they interacted with known NFPA-related genes such as EGFR, PRL, and GH1. These results indicated that COL4A5, LHX3, MSN and GHSR might participate in the initiation and progression of NFPAs via interaction with EGFR, PRL and GH1, respectively.
We found DEGs were significantly enriched in the p53 (Figure 3) and Jak-STAT signaling pathways (Figure 5), which had been reported to take part in PAs pathogenesis [8,24]. The p53 signaling pathway is involved in biological processes such as cell cycle arrest, apoptosis, senescence, DNA repair and changes in metabolism. Expression level of p53 correlates with the proliferative state of PAs . The Jak-STAT pathway is an important downstream pathway for growth factor receptors and cytokine receptors, and it is involved in the regulation of cell proliferation and survival [34,35]. As all of the DEGs mapped on these pathways were remarkably down-regulated in NFPAs, p53 and Jak-STAT signaling pathways might play roles in the progression of NFPAs.
In addition, DEGs were significantly enriched in GO terms mainly about cell communication, signaling, ECM, plasma membrane, collagen, transcription factor activity and receptor binding (Table 2). The ECM, plasma membrane, and receptor binding are the basis of cell communication and signaling between pituitary cells, which play crucial roles in the development and invasion of PAs [36, 37]. As DEGs mapped on these GO terms were remarkably dysregulated in NFPAs, cell communication and signaling might contribute to the progression of NFPAs.
In conclusion, a number of genes (e.g. COL4A5, LHX3, MSN and GHSR) identified in this study, might be potential novel NFPA-related genes. Furthermore, cell communication and signaling pathways (e.g. p53 and JakSTAT) might be implicated in the pathogenesis of NFPAs. Currently, no effective medical therapies are available for NFPAs, due to their unclear mechanism. Although further validation is required, our findings might provide information to guide future researchers and even benefit the development of medical therapy for NFPAs.
Declaration of Interest
This study was supported by Natural Science Fund (grant number: 20150101193JC). The authors report no conflicts of interest. The authors alone are responsible for the content and writing of this article.
Gruppetta M, Mercieca C, Vassallo J. Prevalence and incidence of pituitary adenomas: A population based study in Malta. Pituitary. 2013; 16(4): 545-553. Google Scholar
Karavitaki N. Prevalence and incidence of pituitary adenomas. Ann Endocrinol (Paris). 2012; 73(2): 79-80. Google Scholar
Pereira AM, Biermasz NR. Treatment of non-functioning pituitary adenomas: What were the contributions of the last 10 years? A critical view. Ann Endocrinol (Paris). 2012; 73(2): 111-116. Google Scholar
Chaidarun SS, Klibanski A. Gonadotropinomas. Semin Reprod Med. 2002; 20(4): 339-348. Google Scholar
Lee M, Marinoni I, Irmler M, Psaras T, Honegger JB, Beschorner R, et al. Transcriptome analysis of MENX-associated rat pituitary adenomas identifies novel molecular mechanisms involved in the pathogenesis of human pituitary gonadotroph adenomas. Acta Neuropathol. 2013; 126(1): 137-150. Google Scholar
Beckers A, Aaltonen LA, Daly AF, Karhu A. Familial isolated pituitary adenomas (FIPA) and the pituitary adenoma predisposition due to mutations in the aryl hydrocarbon receptor interacting protein (AIP) gene. Endocr Rev. 2013; 34(2): 239-277. Google Scholar
Stratakis CA, Tichomirowa MA, Boikos S, Azevedo MF, Lodish M, Martari M, et al. The role of germline AIP, MEN1, PRKAR1A, CDKN1B and CDKN2C mutations in causing pituitary adenomas in a large cohort of children, adolescents, and patients with genetic syndromes. Clin Genet. 2010; 78(5): 457-463. Google Scholar
Trovato M, Torre ML, Ragonese M, Simone A, Scarf R, Barresi V, et al. HGF/c-met system targeting PI3K/ AKT and STAT3/phosphorylated-STAT3 pathways in pituitary adenomas: An immunohistochemical characterization in view of targeted therapies. Endocrine. 2013; 44(3): 735-743. Google Scholar
Duran-Prado M, Saveanu A, Luque RM, Gahete MD, Gracia-Navarro F, Jaquet P, et al. A potential inhibitory role for the new truncated variant of somatostatin receptor 5, sst5TMD4, in pituitary adenomas poorly responsive to somatostatin analogs. J Clin Endocrinol Metab. 2010; 95(5): 2497-2502. Google Scholar
Rubinfeld H, Shimon I. PI3K/Akt/mTOR and Raf/ MEK/ERK signaling pathways perturbations in non-functioning pituitary adenomas. Endocrine. 2012; 42(2): 285-291. Google Scholar
Rotondi S, Oliva MA, Esposito V, Ventura L, Giangaspero F, Alesse E, et al. AIP expression in non-functioning pituitary adenomas is strongly associated with the gonadotroph phenotype but not with tumour aggressiveness. Endocrine Abstracts. 2014; 35: P835. (hppt:// www.endocrine-abstracts.org/ea/0035/ea0035P835/ htm).
Mussnich P, Raverot G, Jaffrain-Rea ML, Fraggetta F, Wierinckx A, Trouillas J, et al. Downregulation of miR-410 targeting the cyclin B1 gene plays a role in pituitary gonadotroph tumors. Cell Cycle. 2015; 14(16): 2590-2597. Google Scholar
Chesnokova V, Zonis S, Wawrowsky K, Tani Y, Ben-Shlomo A, Ljubimov V, et al. Clusterin and FOXL2 act concordantly to regulate pituitary gonadotroph adenoma growth. Mol Endocrinol. 2012; 26(12): 2092-2103. Google Scholar
Michaelis KA, Knox AJ, Xu M, Kiseljak-Vassiliades K, Edwards MG, Geraci M, et al. Identification of growth arrest and DNA-damage-inducible gene beta (GADD45beta) as a novel tumor suppressor in pituitary gonadotrope tumors. Endocrinology. 2011; 152(10): 3603-3613. Google Scholar
Cai T, Xiao J, Wang ZF, Liu Q, Wu H, Qiu YZ. Identification of differentially coexpressed genes in gona-dotrope tumors and normal pituitary using bioinformatics methods. Pathol Oncol Res. 2014; 20(2): 375-380. Google Scholar
Zhao P, Hu W, Wang H, Yu S, Li C, Bai J, et al. Identification of differentially expressed genes in pituitary adenomas by integrating analysis of microarray data. Int J Endocrinol. 2015; 2015: 164087. doi: 10.1155/2015/ 164087. Google Scholar
Gautier L, Cope L, Bolstad BM, Irizarry RA. affy— analysis of Affymetrix GeneChip data at the probe level. Bioinformatics. 2004; 20(3): 307-315. Google Scholar
Wilson CL, Miller CJ. Simpleaffy: A BioConductor package for Affymetrix Quality Control and data analysis. Bioinformatics. 2005; 21(18): 3683-3685. Google Scholar
Kolde R. Pheatmap: Pretty Heatmaps. R Package Version 0.7. 7. CRAN Repository, 2012. Google Scholar
Falcon S, Gentleman R. Using GOstats to test gene lists for GO term association. Bioinformatics. 2007; 23(2): 257-258. Google Scholar
Szklarczyk D, Franceschini A, Wyder S, Forslund K, Heller D, Huerta-Cepas J, et al. STRING v10: Protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 2015; 43(Database issue): D447-D452. Google Scholar
Kohl M, Wiese S, Warscheid B. Cytoscape: Software for visualization and analysis of biological networks. Methods Mol Biol. 2011; 696: 291-303. Google Scholar
Davis AP, Grondin CJ, Lennon-Hopkins K, Saraceni-Richards C, Sciaky D, King BL, et al. The Comparative Toxicogenomics Database’s 10th year anniversary: Update 2015. Nucleic Acids Res. 2015; 43(Database issue): D914-D920. Google Scholar
Suliman M, Royds J, Cullen D, Timperley W, Powell T, Battersby R, et al. Mdm2 and the p53 pathway in human pituitary adenomas. Clin Endocrinol (Oxf). 2001; 54(3): 317-325. Google Scholar
Butz H, Likó I, Czirják S, Igaz P, Korbonits M, Rácz K, et al. MicroRNA profile indicates downregulation of the TGFβ pathway in sporadic non-functioning pituitary adenomas. Pituitary. 2011; 14(2): 112-124. Google Scholar
Rishi A, Sharma MC, Sarkar C, Jain D, Singh M, Ma-hapatra AK, et al. A clinicopathological and immu-no-histochemical study of clinically non-functioning pituitary adenomas: A single institutional experience. Neurol India. 2010; 58(3): 418-423. Google Scholar
Chaidarun SS, Eggo MC, Sheppard MC, Stewart PM. Expression of epidermal growth factor (EGF), its receptor, and related oncoprotein (erbB-2) in human pituitary tumors and response to EGF in vitro. Endocrinology. 1994; 135(5): 2012-2021.Google Scholar
Onguru O, Scheithauer BW, Kovacs K, Vidal S, Jin L, Zhang S, et al. Analysis of epidermal growth factor receptor and activated epidermal growth factor receptor expression in pituitary adenomas and carcinomas. Mod Pathol. 2004; 17(7): 772-780. Google Scholar
Chaidarun SS, Klibanski A, Alexander JM. Tumor-specific expression of alternatively spliced estrogen receptor messenger ribonucleic acid variants in human pituitary adenomas. J Clin Endocrinol Metab. 1997; 82(4): 1058-1065. Google Scholar
Cheunsuchon P, Zhou Y, Zhang X, Lee H, Chen W, Nakayama Y, et al. Silencing of the imprinted DLK1-MEG3 locus in human clinically non-functioning pituitary adenomas. Am J Pathol. 2011; 179(4): 2120-2130. Google Scholar
Moreno CS, Evans CO, Zhan X, Okor M, Desiderio DM, Oyesiku NM. Novel molecular signaling and classification of human clinically nonfunctional pituitary adenomas identified by gene expression profling and proteomic analyses. Cancer Res. 2005; 65(22): 10214-10222. Google Scholar
Simpson DJ, Bicknell JE, McNicol AM, Clayton RN, Farrell WE. Hypermethylation of the p16/CDKN2A/ MTSI gene and loss of protein expression is associated with nonfunctional pituitary adenomas but not somatotrophinomas. Genes Chromosomes Cancer. 1999; 24(4): 328-336. Google Scholar
Kim K, Arai K, Sanno N, Osamura RY, Teramoto A, Shibasaki T. Ghrelin and growth hormone (GH) secre-tagogue receptor (GHSR) mRNA expression in human pituitary adenomas. Clin Endocrinol (Oxf). 2001; 54(6): 759-768. Google Scholar
Heim MH. The Jak-STAT pathway: Cytokine signalling from the receptor to the nucleus. J Recept Sig Transd. 1999; 19(1-4): 75-120. Google Scholar
Schindler CW. Series introduction: JAK-STAT signaling in human disease. J Clin Invest. 2002; 109(9): 1133-1137. Google Scholar
Gong J, Zhao Y, Abdel-Fattah R, Amos S, Xiao A, Lopes MBS, et al. Matrix metalloproteinase-9, a potential biological marker in invasive pituitary adenomas. Pituitary. 2008; 11(1): 37-48. Google Scholar
Paez-Pereda M, Kuchenbauer F, Arzt E, Stalla G. Regulation of pituitary hormones and cell proliferation by components of the extracellular matrix. Braz J Med Biol Res. 2005; 38(10): 1487-1494. Google Scholar