Abstract

   We tested whether the drugs T5224, RSPO2, and AZD5363 exert therapeutic
   effects against functioning pituitary adenoma (FPA). We analysed the
   gene expression profiles of four FPA mRNA microarray datasets
   ([45]GSE2175, [46]GSE26966, [47]GSE36314, and [48]GSE37153) from the
   Gene Expression Omnibus database and identified genes differentially
   expressed in FPA vs control tissues. We then carried out Gene Ontology,
   Kyoto Encyclopedia of Genes and Genomes (KEGG), and protein-protein
   interaction network analyses. We also measured the difference in
   expression of hub genes between human normal pituitary cells and FPA
   cells using qRT-PCR. Our in vitro colony-formation and MTT assays
   showed that cell viability, number, and the size of clonogenicities
   were all lower in the presence of T5224, RSPO2, or AZD536 than in
   controls. Moreover, flow cytometry experiments showed that the
   incidence of apoptosis was higher in the presence of T5224, RSPO2, or
   AZD5363 than among controls, and was increased by increasing the doses
   of the drugs. This suggests these drugs could be used as therapeutic
   agents to treat FPA. Finally, we found that cFos, WNT5A, NCAM1, JUP,
   AKT3, and ADCY1 are abnormally expressed in FPA cells compared to
   controls, which highlights these genes as potential prognostic and/or
   therapeutic targets.

   Keywords: bioinformatics, brain science, drug treatment, functional
   pituitary adenomas, prognosis

INTRODUCTION

   Functioning Pituitary Adenoma (FPA) accounts for 70% of all pituitary
   adenomas (PA) [[49]1]. Epidemiological studies have suggested that the
   incidence and prevalence of pituitary neoplasm might be underestimated
   at 7.39/100,000/year and 97.76/100,000, respectively [[50]2]. FPA can
   cause hyper-secretion syndromes, such as hyperprolactinemia,
   acromegaly, and Cushing disease, or mass effects, such as headaches,
   hypopituitarism, vomiting, and visual field defects [[51]4]. Based on
   hormonal activity, FPA is clinically classified mainly as prolactinoma
   (PRL), growth hormone (GH) tumors, or adrenocorticotropic (ACTH)
   hormone tumors. Gonadotropin hormone tumors, multiple hormone adenomas,
   and thyroid-stimulating hormone (TSH) tumors occur only rarely in FPA
   [[52]3]. The fact that FPA can arise from a wide variety of cancer
   types makes it complicated to conduct research on FPA’s diagnosis,
   underlying molecular mechanisms, and treatment.

   Currently, early diagnosis and treatment have improved, owing to the
   widespread use of magnetic resonance imaging (MRI) [[53]2]. However,
   diagnostic methods, such as measuring hormone levels, MRI, pathological
   and immunohistochemical assays, etc., are still not accurate nor timely
   enough to prevent morbidity and consequent mortality due to FPA.
   Traditional treatments such as Dopamine agonists, surgery, and
   radiotherapy have limited effectiveness and cause deleterious side
   effects, including a reduced quality of life in the presence of
   persistent morbidity and slightly increased mortality [[54]4]. Many
   types of FPA, especially macroadenomas, have extremely low cure rates
   [[55]6]. For example, in ~20% of prolactinoma cases, treatment is
   partially or completely ineffective [[56]5].

   Previous studies have demonstrated overexpression of high mobility
   group A (HMGA) in FPA, possibly due to downregulation of HMGA-targeting
   microRNAs (miRNAs) [[57]7]. For example, HMGA2 is overexpressed in
   prolactinoma. In addition, the majority of adenomas show reduced EFEMP1
   expression, irrespective of subtype [[58]8]. Other factors like reduced
   expression of bone morphogenetic proteins (BMP) can cause some adenoma
   subtypes [[59]9]. In this study, c-Fos, Wnt5A, and Akt3 was identified
   as hub genes, which could be used to treat FPA. c-Fos is component of
   AP-1 transcription factors, and T5224 has been reported selectively
   inhibit AP-1. This drug already be used in phase II human clinical
   trials in Japan [[60]10]. RSPO2 can block binding of Wnt5A to Fzd7
   receptor to antagonize tumor cell migration [[61]49]. AZD5363 is one of
   Akt3 inhibitors and an apoptosis promoter in prostate cancer [[62]50].

   Here, we hypothesized that T5224, RSPO2, and AZD5363 should be
   effective treatments against FPA. To test this hypothesis, we looked
   for genes differentially expressed in FPA tissues compared to normal
   brain controls. We also performed bioinformatics analyses to
   investigate the molecular processes underlying FPA and used various
   biochemical and cell biology assays to test the effects of T5224,
   RSPO2, and AZD5363 treatments.

RESULTS

Identification of differentially expressed genes

   We analyzed the gene expression profiles of four FPA mRNA microarray
   datasets ([63]GSE2175, [64]GSE26966, [65]GSE36314, and [66]GSE37153)
   from the Gene Expression Omnibus database. There were 19,943
   differently expressed genes (DEGs) picked up from [67]GSE2175, of which
   12,268 were upregulated while 7675 were downregulated. Altogether, 4635
   DEGs were found out from [68]GSE26966, among which 2159 were
   upregulated and 2476 were downregulated. Among 6472 DEGs were
   identified from [69]GSE36314 with 2520 upregulated genes and 3952
   downregulated genes. Lastly, 2020 DEGs were discovered from
   [70]GSE37153, in which 2020 genes were upregulated and 1017 were
   downregulated. There were 178 mutual DEGs among the four datasets
   ([71]Figure 1A, [72]Supplementary Table 1).

Figure 1.

   [73]Figure 1
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   (A) Venn diagrams for DEGs. (B) Results of q-PCR analysis. (C)
   Functional and pathway enrichment analysis of up- regulated genes. (B)
   Expression heat map of hub genes. (D) Functional and pathway enrichment
   analysis of down-regulated genes. (E–G) Expression heat map of hub
   genes.

Functional and pathway enrichment analysis

   The mutual DEGs were uploaded to DAVID for GO and KEGG pathway analyses
   ([75]Table 1 and [76]Figure 1C, [77]1D). The GO analysis results
   revealed that the mutually upregulated DEGs were mainly associated with
   several biological processes (BPs), such as mitotic nuclear division,
   cell division, and chromosome segregation; cellular components (CCs;
   spindle, microtubule, kinetochore); and molecular functions (MFs;
   protein binding, ATP binding, microtubule motor activity). For the
   mutually downregulated DEGs, the GO analyses revealed that they were
   primarily involved in BPs such as neurotransmitter secretion,
   neurotransmitter transport, and ion transport; CCs covering cell
   junctions and plasma membrane; and MFs including calcium ion binding
   and calcium-dependent protein binding. In addition, KEGG analyses
   indicated that the mutual DEGs were mainly involved in cell cycle,
   oocyte meiosis, and p53 signaling pathways, nicotine addiction,
   GABAergic synapse, and morphine addiction.

Table 1. Functional and pathway enrichment analysis of up-regulated and
down-regulated genes among four datasets.

   Expression Category Term Count % P Value
   up-regulated GOTERM_BP_DIRECT GO:0007067~mitotic nuclear division 18
   38.29787234 1.28E-20
   GOTERM_BP_DIRECT GO:0051301~cell division 17 36.17021277 1.39E-16
   GOTERM_BP_DIRECT GO:0007062~sister chromatid cohesion 9 19.14893617
   1.85E-10
   GOTERM_BP_DIRECT GO:0000086~G2/M transition of mitotic cell cycle 9
   19.14893617 1.82E-09
   GOTERM_BP_DIRECT GO:0007059~chromosome segregation 7 14.89361702
   1.91E-08
   GOTERM_CC_DIRECT GO:0005819~spindle 11 23.40425532 2.99E-13
   GOTERM_CC_DIRECT GO:0030496~midbody 10 21.27659574 2.40E-11
   GOTERM_CC_DIRECT GO:0005874~microtubule 11 23.40425532 3.41E-09
   GOTERM_CC_DIRECT GO:0000775~chromosome, centromeric region 7
   14.89361702 5.29E-09
   GOTERM_CC_DIRECT GO:0000776~kinetochore 7 14.89361702 4.53E-08
   GOTERM_MF_DIRECT GO:0005515~protein binding 41 87.23404255 9.05E-10
   GOTERM_MF_DIRECT GO:0005524~ATP binding 18 38.29787234 3.45E-08
   GOTERM_MF_DIRECT GO:0003777~microtubule motor activity 7 14.89361702
   4.29E-08
   GOTERM_MF_DIRECT GO:0008017~microtubule binding 8 17.0212766 7.29E-07
   GOTERM_MF_DIRECT GO:0004674~protein serine/threonine kinase activity 8
   17.0212766 3.54E-05
   KEGG_PATHWAY hsa04110:Cell cycle 6 12.76595745 1.97E-06
   KEGG_PATHWAY hsa04114:Oocyte meiosis 4 8.510638298 9.73E-04
   KEGG_PATHWAY hsa04115:p53 signaling pathway 3 6.382978723 0.00674313
   KEGG_PATHWAY hsa04914:Progesterone-mediated oocyte maturation 3
   6.382978723 0.01117001
   KEGG_PATHWAY hsa05203:Viral carcinogenesis 3 6.382978723 0.05510903
   down-regulated GOTERM_BP_DIRECT GO:0007268~chemical synaptic
   transmission 16 14.15929204 1.58E-11
   GOTERM_BP_DIRECT GO:0007269~neurotransmitter secretion 6 5.309734513
   1.41E-05
   GOTERM_BP_DIRECT GO:0006836~neurotransmitter transport 5 4.424778761
   1.73E-05
   GOTERM_BP_DIRECT GO:1902476~chloride transmembrane transport 7
   6.194690265 2.15E-05
   GOTERM_BP_DIRECT GO:0006811~ion transport 7 6.194690265 1.24E-04
   GOTERM_CC_DIRECT GO:0030054~cell junction 25 22.12389381 1.97E-16
   GOTERM_CC_DIRECT GO:0030672~synaptic vesicle membrane 10 8.849557522
   3.25E-11
   GOTERM_CC_DIRECT GO:0005886~plasma membrane 52 46.01769912 1.47E-08
   GOTERM_CC_DIRECT GO:0048786~presynaptic active zone 6 5.309734513
   7.06E-07
   GOTERM_CC_DIRECT GO:0008021~synaptic vesicle 8 7.079646018 1.22E-06
   GOTERM_MF_DIRECT GO:0004890~GABA-A receptor activity 4 3.539823009
   1.88E-04
   GOTERM_MF_DIRECT GO:0005509~calcium ion binding 14 12.38938053 3.42E-04
   GOTERM_MF_DIRECT GO:0005230~extracellular ligand-gated ion channel
   activity 4 3.539823009 9.96E-04
   GOTERM_MF_DIRECT GO:0044325~ion channel binding 5 4.424778761
   0.00472253
   GOTERM_MF_DIRECT GO:0048306~calcium-dependent protein binding 4
   3.539823009 0.00505404
   KEGG_PATHWAY hsa05033:Nicotine addiction 7 6.194690265 2.30E-07
   KEGG_PATHWAY hsa04723:Retrograde endocannabinoid signaling 9 7.96460177
   3.09E-07
   KEGG_PATHWAY hsa04727:GABAergic synapse 8 7.079646018 1.40E-06
   KEGG_PATHWAY hsa05032:Morphine addiction 7 6.194690265 3.07E-05
   KEGG_PATHWAY hsa04721:Synaptic vesicle cycle 6 5.309734513 6.13E-05
   [78]Open in a new tab

Module screening from the PPI network

   We also conducted PPI network analyses of the previous 178 mutual DEGs.
   Genes with degrees ≥ 6 were screened as hub genes based on the STRING
   database. Altogether, 21 genes were identified as hub genes ([79]Figure
   1E–[80]1G), including c-Fos, MYC, BCL2, WNT5A, POMC, NCAM1, JUP, AKT3,
   ADCY1, FGFR2, GH1, CCND2, TSHB, GHRHR, PPP2R5A, BCR, CAMK2G, ATP2A2,
   APC, and MAD2L1 (listed in [81]Table 2). MYC had the highest degree of
   nodes, which was 20. Moreover, after MCODE analysis, 157 nodes and 797
   edges were obtained, as well as the top three modules ([82]Figure 2),
   whose functional annotation and enrichment are shown in [83]Table 3.
   Enriched function analysis revealed that genes in module 1 were
   primarily related to cell proliferation, protein complex formation, and
   negative regulation of apoptosis. In module 2, the genes were mainly
   enriched in activation of adenylate cyclase activity, adenylate
   cyclase-activating G-protein coupled receptor signaling pathways, and
   regulation of lipolysis in adipocytes. Finally, for module 3, the genes
   were involved in glycoprotein binding, anchored component of membrane,
   and myelin sheath formation.

Table 2. Detailed information of the hub genes among four datasets.

   Gene symbol Degree Betweenness centrality Gene symbol Degree
   Betweenness centrality
   cFos          22         0.3840231            GH1       6    0.01536337
   MYC           20         0.3150368           CCND2      6    0.02628263
   WNT5A         18         0.1803458           TSHB       6    0.01756622
   BCL2          17         0.37660255          GHRHR      6    0.00112801
   NCAM1         13         0.24487191         PPP2R5A     6    0.11767315
   JUP           12         0.0401026            BCR       6    0.03518683
   POMC          11         0.05597786         CAMK2G      6    0.03972707
   AKT3          10         0.03310717         ATP2A2      6    0.12136994
   ADCY1         9          0.00765123           APC       5    0.02201292
   FGFR2         7          0.0745872          MAD2L1      5    0.07012635
   [84]Open in a new tab

Figure 2.

   [85]Figure 2
   [86]Open in a new tab

   Top 3 modules from the protein-protein interaction network.

Table 3. Functional and pathway enrichment analysis of the modules genes.

   Module Term Count P Value FDR Genes
   module 1 GO:0008283~cell proliferation (BP) 3 2.76E-03 3.48761792 BCL2,
   ALK, MYC
   GO:0043234~protein complex (CC) 3 2.97E-03 2.382996199 BCR, ALK, MYC
   GO:0043066~negative regulation of apoptotic process (BP) 3 4.24E-03
   5.307949002 CCND2, BCL2, MYC
   module 2 GO:0007190~activation of adenylate cyclase activity(BP) 3
   3.31E-05 3.62E-02 ADCY1, GNAS, GHRHR
   GO:0007189~adenylate cyclase-activating G-protein coupled receptor
   signaling pathway(BP) 3 5.19E-05 5.67E-02 ADCY1, GNAS, GHRHR
   hsa04923:Regulation of lipolysis in adipocytes(KEGG) 3 3.83E-04
   3.72E-01 ADCY1, TSHB, GNAS
   module 3 GO:0001948~glycoprotein binding(MF) 2 7.69E-03 3.89981344
   CNTN2, CNTN1
   GO:0031225~anchored component of membrane(CC) 2 1.24E-02 8.45522442
   CNTN2, CNTN1
   GO:0043209~myelin sheath(CC) 2 1.66E-02 11.21564142 CNTN2, CNTN1
   [87]Open in a new tab

Measuring expression of hub genes by qRT-PCR

   We performed qRT-PCR in order to conform the expression of cFos, WNT5A,
   NCAM1, JUP, AKT3, and ADCY1 in normal pituitary cells and FPA cells
   (GT1-1, GH3). NCAM1, cFos, AKT3, and ADCY1 were consistently
   upregulated in FPA cells compared to normal pituitary cells (P < 0.05)
   while WNT5A and JUP were downregulated (P < 0.05)., with levels being
   slightly different across the tested cell lines (showed in [88]Figure
   1B).

T5224, RSPO2, and AZD5363 reduce proliferation of FPA cells

   We used MTT assay to measure cell survival after T5224, RSPO2, and
   AZD5363 treatment. As is shown in [89]Figures 3 and [90]4, with
   increasing drug concentrations, cellular viability (ratio to controls)
   in cell lines GT1-1 and GH3 dropped, decreasing more rapidly for T5224
   than for STO609, Genipin (P < 0.05), RSPO2, and AZD5363. Besides,
   compared to STO609, cellular viability also declined faster for Genipin
   in cell line GT1-1. However, in GH3 cells, cellular viability was
   similar for STO609 and Genipin.

Figure 3.

   [91]Figure 3
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   Cellular viability of glioblastoma cells treated with T5224, Genipin
   and STO-609.

Figure 4.

   [93]Figure 4
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   Cellular viability of glioblastoma cells treated with RSPO2 and
   AZD5363.

   Colony-formation assays revealed different percentages of clone
   formation for each drug treatment group. Compared to controls, there
   were fewer and smaller colonies in all drug groups (0.5μmol/L,
   1μmol/L). On the other hand, clonogenicities were approximately the
   same for the STO609 and Genipin groups, both of which were higher than
   those for the T5224, RSPO2, and AZD5363 groups (P < 0.05) ([95]Figure
   5). Higher drug concentrations correlated with fewer clonogenicities,
   implying dose-dependent effects for T5224, RSPO2, and AZD5363.

Figure 5.

   [96]Figure 5
   [97]Open in a new tab

   Clonogenicities in Petri dishes with different dose of T5224, RSPO2,
   AZD5363, Geinpin, and STO-609.

T5224, RSPO2, and AZD5363 induce apoptosis of FPA cells

   Flow cytometry of FPA cells and controls treated with different doses
   of drugs for 48 h allowed us to measure the percentages of normal,
   necrotic, late apoptotic, and early apoptotic cells. For controls, the
   respective percentages were 57.35%, 16.04%, 21.33%, and 5.28%. On the
   other hand, for T-5224-treated cells they were 5.69%, 1.08%, 59.74%,
   and 33.49% in low-dose group (10 μmol/L); 0.36%, 0.55%, 70.23%, and
   28.86% in the intermediate-dose group (20 μmol/L); and 0.16%, 0.91%,
   83.13%, and 15.8% in the high-dose group (40 μmol/L) ([98]Figure 6A).
   For STO-609-treated cells, the numbers were 13.37%, 1.73%, 34.72%, and
   50.17% in the low-dose group (35 μmol/L); 12.29%, 2.93%, 60.1%, and
   24.68% in the middle-dose group (75 μmol/L); and 4.04%, 3.01%, 79.71%,
   and 13.24% in the high-dose group (150 μmol/L) ([99]Figure 6B). The
   relevant numbers for Genipin were 0.23%, 1.08%, 97.28%, 1.41% in low
   dose group (75 μmol/L) and 0.22%, 0.51%, 90.53%, 8.74% in high dose
   group (150 μmol/L) ([100]Figure 7A). For RSPO2-treated cells, the
   percentages were 7.19%, 4.15%, 48.04%, and 40.62% in the low-dose group
   (75 μmol/L), and 1.72%, 6.59%, 90.72%, and 0.97% in the high-dose group
   (150 μmol/L) ([101]Figure 7B). For AZD5363-treated cells, the
   percentages were 6.91%, 3.75%, 52.22%, and 37.12% in the low-dose group
   (75 μmol/L), and 0.24%, 0.71%, 97.32%, and 1.73% in the high-dose group
   (150 μmol/L) ([102]Figure 7C). We noticed that normal cells were
   predominant in the control group while apoptotic cells were predominant
   in the presence of T5224, RSPO2, or AZD5363 treatment. Compared to
   STO-609-treated cells, there was a greater percentage of apoptotic
   cells in the T5224, Genipin, RSPO2, and AZD5363-treated groups, even
   when using the same dose for all drugs.

Figure 6.

   [103]Figure 6
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   The distribution of cells in apoptosis with different doses of (A)
   STO-609 and (B) T5224.

Figure 7.

   [105]Figure 7
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   The distribution of cells in apoptosis with different dose of (A)
   Genipin, (B) RSPO2, and (C) AZD5363.

DISCUSSION

   Many FPA patients present reduced quality of life, persistent
   morbidity, and slightly increased mortality in spite of receiving
   current therapies [[107]4]. In the present study, we analyzed the gene
   expression profiles of 21 FPA samples and 14 normal samples from mRNA
   microarray datasets [108]GSE2175, [109]GSE26966, [110]GSE36314, and
   [111]GSE37153 in the GEO database. And a total of 19,943, 4635, 6472,
   and 2020 DEGs were identified respectively from those four datasets.
   There were 178 “mutual DEGs” identified by performing Venn plot among
   those four datasets.

   GO analysis of abnormally expressed genes showed that upregulated genes
   were mainly associated with biological processes relevant to mitotic
   proliferation such as cell division, spindle formation, microtubule
   polymerization, protein binding, and ATP binding, which may explain the
   fast multiplication of cancer cells. Downregulated genes were primarily
   involved in biological processes underlying cell communication and
   signaling, including ion transport, cell junction and plasma membrane
   formation, calcium-dependent protein binding and calcium ion binding.
   Our results agree with a previous study showing that
   over-representation of genes can modify the course of the cell cycle,
   cell development, and cell differentiation/proliferation in pathologic
   adenoma cells [[112]11]. Several studies have also shown that cell
   membrane ion channels, especially potassium channels, participate in
   cell signal transduction, proliferation, apoptosis, and regulation of
   gene expression in tumors [[113]12]. Furthermore, the KEGG analyses we
   conducted here revealed that the mutual DEGs were mainly involved in
   the cell cycle, the p53 signaling pathway, oocyte meiosis, nicotine
   addiction, GABAergic synapse, and morphine addiction.

   The levels of some cell-cycle regulators (p16, pRB protein, and cyclin
   D1) can predict the occurrence and proliferation of FPA [[114]13]. The
   P53 signaling pathway is involved in cell cycle arrest, apoptosis,
   senescence, DNA repair, and changes in metabolism [[115]14]. Thus, it
   is not surprising that P53 mutations are the most common in malignant
   tumors. The inhibition of p53 caused by c-Jun upregulation promotes FPA
   invasion [[116]15]. Also, morphine promotes tumor growth by inhibiting
   apoptosis and promoting angiogenesis and migration of tumor cells
   [[117]16]. Similarly, tobacco compounds have long been known to promote
   cell proliferation [[118]17], suggesting that smoking may increase the
   probability of developing FPA.

   c-Fos, one of the components of the activator protein-1 (AP-1)
   transcription factors, is hyper-activated in tumorigenesis and promotes
   cancer cell invasion and proliferation [[119]18–[120]19] for various
   types of cancer ([121]Table 4) [[122]18–[123]20]. Injection treatment
   with FGFR1 inhibitor AZD4547 decreases the number and surface area of
   metastatic lung nodules and parenchyma in mice [[124]21], highlighting
   AZD4547 as a potential treatment for other types of cancer. Similarly,
   T5224 inhibits AP-1, c-Fos, and therefore FGFR1, which suggests that it
   might exert anti-FPA effects.

Table 4. Hub genes and related cancers.

   Hub genes Related cancers
   c-Fos breast cancer, osteosarcoma, endometrial carcinoma, bladder
   cancer, prostate cancer, hepatoma cancer, et al.
   WNT5A prostate cancer, melanoma, gastric carcinoma, breast cancer,
   prostate cancer, lung metastasis of sarcoma cells, et al.
   NCAM1 ovarian carcinoma, gastric cancer, melanoma, Wilms tumor (WT), et
   al.
   JUP colorectal cancer, oral intraepithelial neoplasms, breast cancer,
   serous ovarian cancer, testicular cancer, et al.
   AKT3 glioma, breast cancer, leukemia, colon cancer and prostate cancer,
   et al.
   ADCY1 esophageal carcinoma, pancreatic cancer, rectal adenocarcinoma,
   et al.
   [125]Open in a new tab

   WNT5A promotes malignant progression in tumor cells [[126]22–[127]23]
   and is overexpressed in many types of cancer [[128]22–[129]24]
   ([130]Table 4). RSPO2 inhibits tumor cell migration [[131]49], implying
   it might also have therapeutic effects in FPA.

   NCAM1 is a neural cell adhesion molecule that promotes cell-cell and
   cell-matrix interactions during development and cellular
   differentiation [[132]25]. NCAM1 promotes other normal cellular
   processes [[133]26] and genes in the underlying pathways are more
   likely to be deregulated in tumors that have migrated to lymph nodes,
   especially basal-like tumors associated with poor prognosis [[134]25].
   NCAM1 is also deregulated in other types of cancer ([135]Table 4)
   [[136]27], which suggests it might serve as a prognostic biomarker and
   therapeutic target for FPA [[137]28–[138]29].

   The junction plakoglobin gene (JUP) is a desmosomal anchor protein
   gene, whose normal functioning is necessary for having healthy
   inter-cellular junctions and microtubules [[139]30–[140]31]. JUP is
   abnormally expressed in various diseases, including cancer ([141]Table
   4) [[142]30–[143]33], and its overexpression promotes metastasis and
   primary site recurrence in squamous cell carcinoma [[144]34–[145]35];
   thus, JUP levels might also inform on the recurrence of FPA.

   Protein kinase AKT3 promotes progression, metastasis, and drug
   resistance in various types of cancer ([146]Table 4) [[147]36–[148]39].
   AZD5363, a Akt3 inhibitor, inhibits proliferation in prostate cancer
   [[149]50], which suggests it could also have therapeutic effects in in
   FPA.

   ADCY1, adenylate cyclase 1, catalyzes the synthesis of cAMP
   [[150]40–[151]41] and was found to be dysregulated in rectal
   adenocarcinoma (RAC) and other cancers ([152]Table 4) [[153]40,
   [154]42]. In addition, ADCY1 overexpression promotes multi
   drug-resistant esophageal carcinoma-1 [[155]43]. Fortunately, ADCY1
   target drugs improve prognosis for esophageal carcinoma patients, which
   suggests that these drugs may also help FPA patients [[156]43–[157]44].

   In this study, cFos, NCAM1, JUP, AKT3 ADCY1, CCND2, PPP2R5A, CAMK2G,
   ATP2A2, and MAD2L1 as well as hub genes were shown to be dysregulated
   in FPA and may serve as therapeutic targets or prognostic and
   diagnostic makers.

   We used qRT-PCR to measure cFos, WNT5A, NCAM1, JUP, AKT3, and ADCY1
   levels in normal pituitary cells (R1200) and FPA cell lines (GT1-1,
   GH3). We found that the expression of cFos, NCAM1, AKT3, and ADCY1 was
   lower in normal pituitary cells than in FPA cells (P < 0.05) while
   WNT5A and JUP levels were higher. This means that high expression of
   cFos, NCAM1, AKT3, and ADCY1 promotes tumorigenesis while high levels
   of AKT3 and ADCY1 inhibits it.

   We used MTT and colony-formation assays to evaluate the effects of
   T5224, RSPO2, and AZD5363 on FPA. For cell lines GT-1 and GH3, cell
   viability correlated negatively with T5224, RSPO2, AZD5363, STO-609,
   and Genipin treatments in a dose-dependent manner. Furthermore, the
   ratio of T5224 groups dropped more significantly than the rest of
   groups in GH1-1 cell lines, including RSPO2 and AZD5363 groups. That
   indicates that T5224, RSPO2, and AZD5363 have therapeutic effects on
   FPA cells and protective effects on normal pituitary cells. Similarly,
   colony-forming assays showed that the number and size of
   clonogenicities in the drug groups were remarkably smaller than in
   controls and correlating negatively with treatment in a dose-dependent
   manner, with T5224, RSPO2, and AZD5363 yielding the smallest
   clonogenicities, in agreement with the results of our MTT assays.

   Our flow cytometry experiments on FPA cells treated with different
   doses of T5224, RSPO2, AZD5363, STO-609, or Genipin for 48 h, showed
   that apoptosis correlated positively with treatment dose in all five
   drug groups compared to controls, suggesting beneficial effects from
   such drugs in the treatment of FPA.

   Considering previous studies, the results of our in vitro study here
   indicate that T5224 exerts anti- FPA effects specifically by inhibiting
   cFos pathways and that RSPO2 does so by inhibiting Wnt5A while AZD5363
   inhibits Akt3. Further studies in vivo should be conducted to test the
   therapeutic effects we uncovered here in more clinically-relevant
   systems.

MATERIALS AND METHODS

Microarray data

   The gene expression profiles of [158]GSE2175, [159]GSE26966,
   [160]GSE36314, and [161]GSE37153 were obtained from the GEO database
   ([162]https://www.ncbi.nlm.nih.gov/geo). The corresponding profiles
   were provided on platform [163]GPL96 ([164]GSE2175), [165]GPL570
   ([166]GSE26966), [167]GPL8300 ([168]GSE36314), and [169]GPL6480
   ([170]GSE37153) [[171]45–[172]48]. The [173]GSE2175 contained one FPA
   samples and three normal samples, 14 FPA tissues and nine normal
   samples in the [174]GSE26966. The [175]GSE36314 provided four FPA
   samples and three normal pituitary tissues, and [176]GSE37153 consisted
   of two FPA samples and one normal sample.

Identification of DEGs

   Analyses of the raw data were carried out using GeneSpring software
   (version 11.5, Agilent, USA) for four groups of DEGs to fit four
   respective gene expression profiles. The category of each data set was
   derived from hierarchical clustering. Group FPA and normal tissues were
   identified. The probe quality control in GeneSpring was limited by
   virtue of principal component analysis (PCA), and probes with intensity
   values below the 20^th percentile were filtered out using the “filter
   probesets by expression” option. Then, the DEGs were identified using
   classical t test with P value cutoff of < 0.05 and a change ≥ two fold.
   We also computed Venn diagrams for each DEG
   ([177]http://bioinformatics.psb.ugent.be/webtools/Venn/).

Gene ontology and pathway enrichment analysis of DEGs

   The DAVID database (Database for Annotation, Visualization and
   Integrated Discovery, [178]https://david.ncifcrf.gov/) provides a
   comprehensive annotation tools to understand the biological meaning
   underlying plenty of genes. GO (Gene Ontology) is a useful method for
   exposing biological process, molecular function, and cell component of
   genes. KEGG (Kyoto encyclopedia of Genes and Genomes) is a base for
   gene function analysis and genomic information linking. We performed GO
   and KEGG pathway enrichment analyses using DAVID for functional
   analyses of DEGs.

PPI network construction and modules selection

   We used the online database STRING (Search Tool for Retrieval of
   Interacting Genes, [179]https://string.embl.de/) for PPI
   (Protein-Protein interaction) analysis. Then, we used Cytoscape
   software to screen hub genes and modules with MCODE (Molecular Complex
   Detection). Finally, we performed function and pathway enrichment
   analyses of DEGs in modules.

Cell lines

   Normal pituitary cells (R1200) and FPA cells (GT1-1 and GH3) were
   received from the ATCC (American Type Culture Collection). Those cell
   lines were cultured in Dulbecco’s modified Eagle’s medium (DMEM,
   Hyclone, USA) supplemented with 10% fetal bovine serum (FBS, Gibco,
   USA). The cell cultures were maintained at 5% CO[2] and 95% air at 37
   °C.

Real-time quantitative reverse transcription PCR

   To verify the expression of cFos, WNT5A, NCAM1, JUP, AKT3, and ADCY1 in
   FPA cell lines and normal human pituitary cells, we used FastStart
   Universal SYBR Green Master (ROX) (Roche Diagnostics) to perform
   qRT-PCR in a CFX96 Real-Time System (Bio-Red) according to the
   manufacturer’s instructions. Expression levels were normalized to
   glyceraldehyde-3-phosphate dehydrogenase (GAPDH). The 2^-ΔΔCt method
   was used for qRT-PCR data analysis. The primers of genes were
   enumerated as followed: cFos sense, 5′-CCTCTCCATGCAGGAGTTAAGA-3′; cFos
   anti-sense, 5′-GGTCTCGGGTCCTTGATTTTCT-3′; WNT 5A sense,
   5′-TACTGCGGTGGAGCAAGAAG-3′; WNT5A anti-sense, 5′-CATCTGCGCTTGACGGAGA
   G-3′; NCAM1 sense, 5′-AGCCCATCAATAAGGGAG GG-3′; NCAM1 anti-sense,
   5′-ACCTGACACCCGTTT TAGCTG-3′; JUP sense, 5′-TACTGCGGTGGAGCAA GAAG-3′;
   JUP anti-sense, 5′-CATCTGCGCTTGACG GAGAG-3′; ADCY1 sense,
   5′-TACTGCGGTGGAGC AAGAAG-3′; ADCY1 anti-sense, 5′-CATCTGCGCTT
   GACGGAGAG-3′.

MTT assay

   The FPA cells (GT1-1, GH3) were plated into 96-well culture plate with
   a density of 500 cells/well, and were treated with different doses of
   T-5224, RSPO2, and AZD5363, respectively, as well as STO-609 (CaMKK
   inhibitor) and Genipin (aglycone derived from the iridoid glycoside),
   both of which protect against several types of tumors, including brain
   tumors. We used MTT (Sigma, St. Louis, Missouri, USA) dissolved in PBS
   (5 mg/ml) to measure the viability of cells. On the day of measurement,
   the medium was replaced on fresh DMEM supplemented with 10% FBS and
   diluted MTT (1:10, 10% MTT), and incubated for 3.5 h at 37 °C. Then,
   the incubation medium was removed and formazan crystals were dissolved
   in 200 μl solution of DMSO. We used an ELx800 absorbance microplate
   reader (BioTek Instruments, VT, USA) to quantify the MTT reduction by
   measuring light absorbance at 570 nm. Each test was repeated four
   times.

Colony-forming assay

   FPA cells (GT1-1, GH3) were seeded in Petri dishes with a density of 50
   cells/cm^2. After 24 h in culture, the cells were treated with
   different doses of STO-609, Genipin, T-5224, RSPO2, and AZD5363,
   respectively. After 10 days of growth in vitro, colonies were counted
   and described according to Franken et al. Then, colonies were rinsed
   with PBS, fixed in 4% paraformaldehyde, stained with 5% crystal violet
   for 0.5 h, and rinsed twice with water.

Flow cytometry

   The FPA cells (GT1-1) in the log growth phase were seeded into 6-well
   plates with a density of 2 × 10^5 cells/well and treated with different
   doses of STO-609, Genipin, T-5224, RSPO2 and AZD5363. After 48 h of
   culturing, the cells were harvested using accutase detachment solution
   (Sigma Aldrich, USA). Annexin-V-FITC/PI labeling was conducted
   according to the manufactures’ instruction. a flow cytometer was used
   to count stained cells with the FACSDiva Version 6.2.

Statistics

   All statistical data analyses were carried out using SPSS 18.0 (SPSS
   Inc., Chicago, Illinois, USA), namely t tests for independent samples
   with P values < 0.05.

Supplementary Material

   Supplementary Table
   [180]aging-11-102372-s001.xls^ (780.5KB, xls)

Abbreviations

   ATCC
          American Type Culture Collection

   ACTH
          Adrenocorticotropic

   AP-1
          Activator protein-1

   ADCY1
          Adenylate cyclase 1

   BP
          Biological processes

   BMP
          Bone morphogenetic proteins

   CC
          Cell component

   DEG
          Differential expressed genes

   DAVID
          Database for Annotation, Visualization and Integrated Discovery

   DMEM
          Dulbecco’s modified Eagle’s medium

   Eca-1
          Esophageal carcinoma-1

   FBS
          Fetal bovine serum

   FPA
          Functioning pituitary adenomas

   GO
          Gene ontology

   GH
          Growth hormone

   GEO
          Gene Expression Omnibus

   miRNAs
          HMGA-targeting microRNAs

   JUP
          Junction plakoglobin,

   KEGG
          Kyoto encyclopedia of Genes and Genomes

   LN1
          Lymph node-positive

   MF
          Molecular function

   nAChRs
          Nicotinic acetylcholine receptors

   NCAM1
          Neural Cell Adhesion Molecule

   PRL
          Prolactinoma

   PA
          Pituitary adenomas

   PKB
          Protein kinase B

   PCA PCA
          Principal component analysis

   PPI
          Protein-protein interaction

   RAC
          Rectal adenocarcinoma

   STRING
          Search Tool for Retrieval of Interacting Genes

   TSH
          Thyroid-stimulating hormone

   WHO
          World Hearth Organization

   WNT5A
          Wnt member 5a

   WT
          Wilms’ tumor

   β-ARs
          β-Adrenergic receptors

Footnotes

   AUTHOR CONTRIBUTIONS: Sheng Zhong and Bo Wu designed experiments;
   Jiahui Li and Xinhui Wang wrote the manuscript. Shanshan Jiang, Fangfei
   Hu, Gaojing Dou carried out experiments; Yuan Zhang Chunjia Sheng
   analyzed experimental results. Yong Chen analyzed sequencing data.

   CONFLICTS OF INTEREST: The authors declare no conflicts of interest.

   FUNDING: This study was supported by grants from the National Natural
   Science Foundation of China (Nos. 81672505 and 81772684), the S&T
   Development Planning Program of Jilin Province (Nos. 20160101086JC,
   20160312017ZG, and 20180101152JC), the Jilin Provincial Education
   Department “13^th Five-Year” Science and Technology Project
   (JJKH20180191KJ) and the Interdisciplinary Innovation Project of The
   First Hospital of Jilin University (JDYYJC001).

REFERENCES