Endokrynologia Polska 2/2016-Molecular classification of pituitary adenomas – in search of criteria useful for high-throughput studies

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Molecular classification of pituitary adenomas – in search of criteria useful for high-throughput studies

Klasyfikacja molekularna gruczolaków przysadki – w poszukiwaniu kryteriów przydatnych do badań wysokoprzepustowych

Jadwiga Żebracka-Gala1, Adam Rudnik2, Kornelia Hasse-Lazar1, Dawid Larysz3, Michał Jarząb4, Jolanta Krajewska1, Piotr Bażowski2, Barbara Jarząb1

1Department of Nuclear Medicine and Endocrine Oncology, MSC Cancer Centre and Institute of Oncology, Gliwice Branch, Poland

2Department of Neurosurgery, Silesian University School of Medicine, Poland

3Radiotherapy Department, MSC Cancer Centre and Institute of Oncology, Gliwice Branch, Poland

4III Department of Radiotherapy and Chemotherapy, MSC Cancer Center and Institute of Oncology, Gliwice Branch, Poland

Jadwiga Żebracka-Gala M.D., Department of Nuclear Medicine and Endocrine Oncology; MSC Cancer Centre and Institute of Oncology, Gliwice Branch, Poland, Wybrzeże AK 15, 44–100 Gliwice, phone: +48 32 278 94 52, e-mail: jzebracka@io.gliwice.pl

Abstract

Introduction: The mechanism of pathogenesis of pituitary adenomas is still unknown, and it shows differences in pituitary cells of different origin.

The aim of our study was to analyse the gene expression profile of pituitary hormones and their precursor genes: PRL, GH, POMC, TSHb, LHb, FSHb, and CGA by QPCR in particular types of pituitary adenomas, and to evaluate the results in the context of sample selection for microarray studies.

Material and methods: Analysis of the gene expression profile was performed in 84 samples of pituitary adenomas, by real-time quantitative PCR (QPCR).

Results: As expected, expression of GH gene was significantly higher in somatotropinomas than in prolactinomas (p<0.05). For POMC gene we noticed lower expression in all pituitary adenomas, except adrenocorticotropinomas (p<0.05). In the case of PRL gene, the highest expression was observed; PRL+ adenomas were in third place. LHb and FSHb genes showed the highest expression, respectively, in LH-producing and FSH-producing pituitary adenomas; however, our analysis did not show statistically significant differences between FH-producing and FSH-producing adenomas.

Conclusions: Our study showed that GH is a characteristic gene for somatotropinomas. We drew a similar conclusion for POMC gene and adrenocorticotropinomas.

However, the results that we obtained for PRL, TSHb, LHb, FSHb, and CGA genes indicate that evaluation of gene expression is not sufficient for classification of particular subtypes of pituitary adenomas.

(Endokrynol Pol 2016; 67 (2): 148–156)

 

Key words: pituitary adenoma; gene expression; QPCR

 

Streszczenie

Wstęp: Mechanizm odpowiedzialny za patogenezę gruczolaków przysadki nie został jeszcze w pełni wyjaśniony i wykazuje różnice w różnych typach komórek przysadki.

Celem badania była analiza profilu ekspresji genów kodujących hormony przysadkowe i ich prekursory: PRL, GH, POMC, TSHb, LHb, FSHb, CGA w poszczególnych typach gruczolaków przysadki oraz ocena uzyskanych wyników w kontekście wyboru próbek do badań mikromacierzowych.

Materiał i metody: Analizę ekspresji genów przeprowadzono za pomocą ilościowej reakcji PCR w czasie rzeczywistym (QPCR) na materiale 84 gruczolaków przysadki.

Wyniki: Ekspresja genu GHbyła znamiennie wyższa w gruczolakach somatotropinowych (GH+) w porównaniu z prolaktynowymi (PRF+). Zaobserwowano również wzrost ekspresji tego genu w guzach GH+ w stosunku do gruczolaków immunohistochemicznych ujemnych. Dla genu POMC wykazano niską ekspresję we wszystkich badanych grupach gruczolaków, z wyjątkiem gruczolaków kortykotropinowych (ACTH+). Najwyższą ekspresję genu PRL zaobserwowano w gruczolakach somatotropinowych; gruczolaki prolaktynowe były na trzecim miejscu. Dla genów LHbFSHb nie zaobserwowano statystycznie znamiennych różnic pomiędzy gruczolakami LH+ i FSH+.

Wnioski: W niniejszym badaniu potwierdzono, że gen GH jest charakterystyczny dla gruczolaków somatotropinowych, podobnie jak gen POMC dla gruczolaków kortykotropinowych. Jednakże, wyniki uzyskane dla genów PRL, TSHb, LHb, FSHbCGA wskazują, że ocena ekspresji genów nie jest wystarczająca dla prawidłowej klasyfikacji poszczególnych podtypów gruczolaków przysadki.

(Endokrynol Pol 2016; 67 (2): 148–156)

 

Słowa kluczowe: gruczolak przysadki; ekspresja genu; QPCR

Supported by: Ministry of Science and Higher Education; No: 2946/P01/2006/31.

Introduction

Pituitary tumours are relatively frequent intracranial tumors and constitute a very interesting model: they share the properties of well-differentiated endocrine tumors, which are able to secrete hormones, characteristic for the cell of origin and the ability for invasive growth, characteristic for neoplastic cells [1], Among pituitary adenomas there is a wide variety of phenotypes: from hormone-producing tumours to non-functioning adenomas. Most functioning pituitary adenomas secrete prolactin (~40%) [2–4], about 20% secrete growth hormone [5], ~10% secrete corticotropin [1, 3], ~10–15% secrete glycoprotein hormones (LH, FSH) [1], and less than 1% secrete thyrotropin [6]. The other pituitary adenomas (~5%) are negative for hormone secretion, and these are referred to as immunohistochemically negative (null adenomas) [7], Pituitary tumours have been classified in different ways. The oldest classification was based on cellular characteristics using haematoxylin and eosin stains on resected tissues; however, this classification did not take into account clinical symptoms or hormone production by the adenoma. With the advent of immunohistochemical tests, tumours are now classified according to the characteristic hormone staining and electron microscopic changes. This classification is in general agreement with the reported clinical signs and symptoms. Currently, this is the main way to distinguish different types of pituitary tumours. However, it is expected that in the future, molecular and genetic techniques will also be applied. At the present time, microarray studies are a powerful method for global analysis of gene expression profile, opening up new horizons in molecular systems. In the case of pituitary adenomas there is still little specific knowledge about the gene expression profile differentiating particular subtypes of pituitary tumours.

In our study we focused on one of the crucial steps of microarray studies: sample selection. For this purpose, we used real-time quantitative PCR (QPCR), which is the most common method for fast, accurate, sensitive, and cost-effective gene expression analysis in many samples concurrently. It generates high-quality data without the requirement of additional validation, and it is applied to validate data obtained by higher throughput technologies such as microarray.

The main goal of our study was to analyse the gene expression profile of pituitary hormones and their precursor genes: PRL, GH, POMC, TSHb, LHb, FSHb and CGA by QPCR in particular types of pituitary adenomas, and to evaluate the results in the context of sample selection for microarray studies.

Material and methods

The study was approved by the Ethics Committee of Maria Sklodowska-Curie MSC Cancer Centre and Institute of Oncology in Gliwice.

Patients

The studied group of consisted of 84 patients operated on for pituitary adenoma with application of endoscopic transnasal transsphenoidal approach (ETTA) in the Department of Neurosurgery of the Medical University of Silesia, Poland. There were 41 women and 43 men. The mean age was 53.2 years (18–70 years) for the female subgroup and 52.7 years (20–77 years) for men. In 10 cases, tumours had increased features of invasiveness, i.e. the presence of tumour cells in samples of dura obtained during the surgery, in histopathological examination. Moreover, all aforementioned tumours infiltrated lateral walls of the sella, so they were classified as III or IV degree in Knosp’s Classification of Cavernous Sinus Invasion. In six cases surgical resection was performed as a reoperation. In all of the aforementioned six cases surgery was the first reoperation.

Tumours

The analysis of gene expression was performed in 84 samples of pituitary adenomas, collected with the cooperation of the Department of Neurosurgery of the Silesian Medical University in Katowice. Fragments of pituitary adenomas were taken intraoperatively and stored in RNA at 4°C. All adenomas were histopathologically verified. As judged by postoperative immunohistochemistry (IHC), there were 13 GH+, 26 PRL+, 8 ACTH+, 5TSH+, 11 LH+, 5 FSH+ and 16 null adenomas (with negative immunohistochemistry toward the previous hormones). The percentage of TSH-positive adenomas in the study group was relatively high compared to published data; however, we collected so many tumours intentionally because we wanted to analyse the gene expression level also in rare functional pituitary adenomas. Moreover, using a smaller group of TSH+ pituitary adenomas in statistical analysis might have led to biased results in this analysis.

The GH+, PRL+, ACTH+ and TSH+ adenomas were grouped as functional adenomas (FA) due to the clinical symptoms caused, while LH+, FSH+, and null adenomas were described clinically as non-functioning tumours (NFA).

Isolation of RNA

Total RNA was extracted from homogenised frozen tissue using Mini Kits (Qiagen GmbH, Hilden, Germany). RNA quantity was measured by NanoDrop ND-1000 (Thermo Scientific, Wilmington, DE, USA) minispectrophotometer, and the quality was estimated by Agilent 2100 using RNA 6000 Nano Assay (Agilent Technologies, Santa Clara, CA, USA). RNA integrity, assessed by RIN index, was within the 4.5–9.1 range.

cDNA synthesis

cDNA was synthesised from 500 ng of total RNA by Omniscript Kit (Qiagen GmbH, Hilden, Germany), with a mixture of oligo-dT and random nonamer primers (Sigma-Aldrich, ST. Louise, USA) and 10U RNAse inhibitor (Fermentas Thermo Fisher Scientific, Walthman, USA). The reaction was carried out at 37ºC for one hour.

Quantitative real-time reverse transcription-PCR (QPCR)

Analysis of gene expression was performed by real-time quantitative PCR with the use of fluorescent probes from the Universal Probe Library (Roche, Basel, Switzerland). Amplicons were designed using a web-based application (www.roche-applied-sdence.com/sis/rtpcr/upl) (Table I). QPCR was carried out in a 96-well optical reaction plate using an ABI Prism 7700 Sequence Detection System (Applied Biosystems). Five microlitres of template cDNA (equivalent to 500 ng of total RNA) were added to 15 μl of PCR reaction mix containing 10 μl TaqMan Universal PCR Master Mix (Life Technologies, Carlsbad, CA, USA), 1 μl forward and reverse primers (200 nM), 1 μl probe (100 nM), and water. Thermal cycling condition were as follows: 50°C for two minutes (incubation and activation AmpErase UNG), 95°C for 10 minutes (activation AmpliTag Polymerase DNA), 95°C for 15 seconds (denaturation), and 60°C for one minute (annealing and extension). Every sample was examined in duplicate. The standard curve, used in experiments, was prepared from serial dilutions of human reference RNA (Stratagene, La Jolla, CA, USA). Expression of the examined genes was normalised to tire reference index, obtained by calculation of tire geometric mean of tire reference genes expression: ACTB, EIF3S10, ATP6V1E, UBE2D2, B2M, and GUS B.

Table I. Amplicons used for quantitative real-time PCR measurement of analysed genes
Tabela I. Sekwencje starterów i sondy do ilościowej reakcji PCR dla genów badanych oraz genów kontrolnych

Gene Name Gene ID Primer F sequence
Investigated genes
PRL Prolactin NM_000948.2 AAAGGATCGCCATGGAAAG
GH Growth hormone NM_000515.3 CCAACAGGGAGGAAACACAA
POMC Proopiomelanocortin NM_000939.2 CAGGAGAGCTCGGCAAGTAT
TSHb Thyroid-stimulating hormone, beta subunit S70587.1 CAGCACAATGGATACGCATAA
FSHb Follicle-stimulating hormone, beta polypeptide NM_000510.2 TGGTGTGCTGGCTACTGCT
LHb Luteinizing hormone, beta polypeptide NM_000894.2 GCTACTGCCCCACCATGA
CGA Glycoprotein hormones, alpha polypeptide NM_000735.2 TCTCCATTCCGCTCCTGAT
Reference genes
ACTB β-actin NM_001101.2 ATTGGCAATGAGCGGTTC
ATP6V1E1 ATPase, H+ transporting, lysosomal 31 kDa, V1 subunit E1 NM_001696.2 AAGCCGGCTGGATCTCAT
B2M β–2-microglobulin NM_004048.2 TTCTGGCCTGGAGGCTATC
EIF3S10 Eukaryotic translation initiation factor 3, subunit 10 theta NM_003750.1 AGTAGAGCGCCTGTACCATGA
GUSB β-glucuronidase NM_000181.1 CGCCCTGCCTATCTGTATTC
UBE2D2 Ubiquitin-conjugating enzyme E2D 2 NM_003339.2 AATGGCAGCATTTGTCTTGA

Table I cd. Amplicons used for quantitative real-time PCR measurement of analysed genes cont.
Tabela I cd. Sekwencje starterów i sondy do ilościowej reakcji PCR dla genów badanych oraz genów kontrolnych

Gene Name Primer R sequence Probe Probe sequence
Investigated genes
PRL Prolactin GCACAGGAGCAGGTTTGAC 18 TCCTGCTG
GH Growth hormone GACACTCCTGAGGAACTGCAC 19 GGCTGGAG
POMC Proopiomelanocortin GGCTCTTCTTCCCCTCCTT 82 CTCCTCTG
TSHb Thyroid-stimulating hormone, beta subunit CAGCACAATGGATACGCATAA 33 TCCCAGCTC
FSHb Follicle-stimulating hormone, beta polypeptide CCTTGAAGGTACATGTTTTCTGG 20 CTGGCTGG
LHb Luteinizing hormone, beta polypeptide GCTACTGCCCCACCATGA 71 CTGGCTGC
CGA Glycoprotein hormones, alpha polypeptide GGGAGAAGAATGGGTTTTCC 61 TTGCCCAG
Reference genes
ACTB β-actin GGATGCCACAGGACTCCAT 11 CTTCCAGC
ATP6V1E1 ATPase, H+ transporting, lysosomal 31 kDa, V1 subunit E1 GCATTTGCACCAAACAAGG 3 CCCAGCAG
B2M β–2-microglobulin TCAGGAAATTTGACTTTCCATTC 42 CATCCAGC
EIF3S10 Eukaryotic translation initiation factor 3, subunit 10 theta GCGTGTATTGGAGGCAGAAT 61 TTGCCCAG
GUSB β-glucuronidase TCCCCACAGGGAGTGTGTAG 57 CTGGGGCC
UBE2D2 Ubiquitin-conjugating enzyme E2D 2 CACAACAGAGAACAGATGGACAA 67 CTCCAGCA

Statistical analysis

geNorm application software for Microsoft Excel was used to identify the most stable reference gene under the described conditions, and to determine the optimal number of reference genes required for reliable normalisation of QPCR data.

To determine between-group differences, we used Mann-Whitney nonparametric test. Differences were considered significant at p < 0.05. The fold change was calculated by dividing medians of expression.

Results

The results of the analysis for each gene: PRL, GH, POMC, TSHb, LHb, FSHb and CGA were presented in the form of a report showing the raw data: Ct value and the relative of the template, which was read from the standard curve. The expression of the investigated genes was normalised with relation to a reference index, obtained by calculation of the geometric mean of expression of reference genes: ACTB, EIF3S10, ATP6V1E, UBE2D2, B2M, and GUS B. The comparison was performed in particular types of pituitary adenomas: FA/null, PRL+/GH+, PRL+/null, GH+/null, GH+/ACTH+, ACTH+/null, LH+/FSH+, LH+/null and FSH+/null. We observed significant differences in gene expression in particular types of pituitary adenomas (Table II).

Table II. The expression of investigated genes in different types of pituitary adenomas
Tabela II. Wartości względne odzwierciedlające ekspresję genów badanych w poszczególnych typach gruczolaków przysadki

Type of pituitary adenomas Number Gene expression
Median Quartile 1 Quartile 3
PRL
GH 13 0.427 0.005 6.012
PRL 26 0.273 0.015 55.374
LH 11 0.393 0.024 0.899
FSH 5 0.088 0.001 1.802
FA* 52 0.125 0.012 6.252
NFA* 31 0.087 0.005 0.611
null 15 0.037 0.005 0.611
GH
GH 13 165.332 0.226 216.951
PRL 26 0.045 0.012 0.376
LH 11 0.018 0.005 0.381
FSH 5 0.749 0.001 0.749
FA* 52 0.176 0.012 41.389
NFA* 32 0.021 0.002 0.475
null 16 0.007 0.001 0.475
POMC
ACTH 8 0.097 0.062 10.658
GH 13 0.001 0.0004 0.018
PRL 26 0.002 0.001 0.013
LH 11 0.002 0.001 0.014
FSH 5 0.003 0.003 0.009
FA* 52 0.009 0.001 0.034
NFA* 32 0.003 0.001 0.01
null 16 0.003 0 0.01
TSH B
TSH 5 1.584 0.798 5.456
GH 13 0.786 0.103 1.522
PRL 14 0.599 0.135 1.447
LH 11 1.716 0.475 3.201
FSH 5 1.482 1.006 1.776
FA* 50 0.759 0.128 1.569
NFA* 32 0.786 0.411 1.66
null 16 0.528 0.245 1.66
LH B
GH 12 0.007 0.002 1.371
PRL 22 0.264 0.01 0.915
LH 11 0.422 0.007 2.213
FSH 5 0.03 0.03 3.815
FA* 46 0.038 0.004 0.64
NFA* 32 0.095 0.005 1.77
null 16 0.055 0.004 1.77
FHS B
GH 12 0.006 0.0002 0.426
PRL 20 0.31 0.031 2.915
LH 11 1.11 0.111 3.235
FSH 5 2.525 2.621 7.131
FA* 42 0.072 0.001 0.99
NFA* 31 0.305 0.073 3.553
null 15 0.089 0.013 3.553
CGA
GH 16 0.078 0.012 0.646
PRL 26 0.227 0.052 1.138
TSH 5 0.98 0.008 1.806
LH 11 3.577 0.929 7.219
FSH 5 0.88 0.36 0.948
FA* 52 0.166 0.029 1.377
NFA* 31 0.31 0.18 1.727
null 15 0.271 0.117 1.727

FA – functional adenomas, null – immunohistochemically-negative adenomas, GH – somatotropinomas, PRL – prolactinomas, ACTH – corticotropinomas, TSH – thyrotropic adenomas; LH, FSH – gonadotropic adenomas, NFA – common group of immunohistochemistry negative adenomas and gonadotropic adenomas, FA* – GH+, PRL+, ACTH+, TSH+ adenomas, NFA* – null, LH+, FSH+ adenomas

Table II cd. The expression of investigated genes in different types of pituitary adenomas
Tabela II cd. Wartości względne odzwierciedlające ekspresję genów badanych w poszczególnych typach gruczolaków przysadki

Type of pituitary adenomas Number Descriptive statistic Comparison of each class to null (p level is given if significant)
Skewness Kurtosis
PRL
GH 13 3.479 12.317 ns
PRL 26 1.883 3.158 ns
LH 11 1.262 0.933 ns
FSH 5 0.548 -2.886 ns
FA* 52 2.837 8.421 ns
NFA* 31 4.436 21.963  
null 15 3.823 14.72  
GH
GH 13 1.667 2.778 0.002
PRL 26 3.145 9.623 0.015
LH 11 2.82 8.264 ns
FSH 5 -0.073 -2.826 ns
FA* 52 2.81 8.078 0.001
NFA* 32 3.822 16.585  
null 16 3.256 11.255  
POMC
ACTH 8 1.145 -0.631 0.01
GH 13 2.113 4.924 ns
PRL 26 5.068 25.776 ns
LH 11 3.262 10.724 ns
FSH 5 0.607 -2.697 ns
FA* 52 4.351 18.675 ns
NFA* 32 2.943 8.471  
null 16 2.222 4.234  
TSH B
TSH 5 0.616 -2.595 0.04
GH 13 0.474 -1.336 ns
PRL 14 2.581 6.441 ns
LH 11 1.793 2.212 0.038
FSH 5 -0.635 0.535 ns
FA* 50 2.719 7.693 ns
NFA* 32 3.432 11.964  
null 16 1.18 2.188  
LH B
GH 12 2.456 6.170 ns
PRL 22 2.569 7.109 ns
LH 11 2.074 4.856 ns
FSH 5 0.278 -1.749 ns
FA* 46 2.962 8.922 ns
NFA* 32 1.881 2.979  
null 16 3.127 10.536  
FHS B
GH 12 2.055 3.151 ns
PRL 20 4.472 20 ns
LH 11 0.524 -1.394 ns
FSH 5 -0.319 -2.959 0,006
FA* 42 6.481 42 ns
NFA* 31 0.931 -0.176  
null 15 1.227 -0.176  
CGA
GH 16 1.517 0.865 ns
PRL 26 2.776 9.119 ns
TSH 5 0.608 -1.072 ns
LH 11 1.004 -0.139 0,0008
FSH 5 1.938 3.989 ns
FA* 52 2.662 7.961 ns
NFA* 31 2.414 5.44  
null 15 1.366 2.912  

FA – functional adenomas, null – immunohistochemically-negative adenomas, GH – somatotropinomas, PRL – prolactinomas, ACTH – corticotropinomas, TSH – thyrotropic adenomas; LH, FSH – gonadotropic adenomas, NFA – common group of immunohistochemistry negative adenomas and gonadotropic adenomas, FA* – GH+, PRL+, ACTH+, TSH+ adenomas, NFA* – null, LH+, FSH+ adenomas

PRL gene

The highest expression of PRL gene was observed in pituitary adenomas, which were GF1+ in the IIIC study; PRL+ adenomas were in the third place. We observed also that expression of PRL gene was the lowest in FSH + adenomas. When we compared all functional adenomas (FA) with inununohistochemically-negative adenomas (null) PRL gene showed 3.5x higher expression in FA; however, the difference was not significant.

GH gene

In a comparison of GF1-I- and oilier groups of functional adenomas we observed significantly higher expression of GH gene in the GF1+/PRL+ comparison (3674×↑; p = 0.005) (Fig. 1A). Expression of GH gene was also significantly higher when we compared immunohistochemically-negative adenomas (null) with GH+ adenomas (23618×↑; p = 0.002) (Fig. IB) and PRL+ adenomas (6×↑; p = 0.015) (Fig. 1C).

Figure 1. Comparison of GH expression in: A. GH+ to PRL+ pituitary adenomas, (U Mann Whitney test, p = 0.005); B. GH+ to IHC negative (null) pituitary adenomas, (U Mann Whitney test, p = 0.002); C. PRL+ to IHC negative (null) pituitary adenomas, (U Mann Whitney test, p = 0.015)
Rycina 1. Porównanie ekspresji genu GH w: A. Gruczolakach GH+ w porównaniu z gruczolakami PRL+, (test U Manna Whitneya, p = 0.005); B. Gruczolakach GH+ w porównaniu z gruczolakami IHC ujemnymi (null) (test U Manna Whitneya, p = 0.002); C. Gruczolakach PRL + w porawnaniu z gruczolakami IHC ujemnymi (null) (test U Manna Whitneya, p = 0.015)

POMC gene

The expression of POMC gene was similarly low in all pituitary groups except adenomas, which were ACTH+ in the IHC study. Significant differences were observed in the comparison of ACTH+ and null adenomas (32×↑; p = 0.01) (Fig. 2).

Figure 2. Comparison of POMC expression in ACTH+ to IHC negative (null) pituitary adenomas, (U Mann Whitney test, p = 0.01)
Rycina 2. Porownanie ekspresji genu POMC w gruczolakach ACTH+ w porównaniu z gruczolakami IHC ujemnymi (null), (test U Manna Whitneya, p = 0.01)

TSHb gene

For TSHb gene we observed higher expression in TSH+ adenomas than PRL+, GH+, and FSH+. What is interesting is that we observed also an increase in TSHb gene expression in LH+ adenomas, and the difference between TSH+ and LH+ was not significant. Analysis of particular groups of adenomas showed significant differences only for comparison of TSH+/null (3×↑; p = 0.04) (Fig. 3A) and LH+/null (3.25×↑; p = 0.038) (Fig. 3B).

Figure 3. Comparison of TSHb expression in: A. TSH+ to IHC negative (null) pituitary adenomas, (U Mann Whitney test, p = 0.04); B. LH+ to IHC negative (null) pituitary adenomas, (U Mann Whitney test, p = 0.038)
Rycina 3. Porównanie ekspresji genu TSHb w: A. Gruczolakach TSH+ w porównaniu z gruczolakami IHC ujemnymi (null), (test U Manna Whitneya, p = 0.04); B. Gruczolakach LH+ w porównaniu z gruczolakami 1HC ujemnymi (null), (test U Manna Whitneya, p = 0.038)

LHb gene

The highest expression of LHb gene was observed in LH+ adenomas, and in second place were PRL+ adenomas. However, our analysis did not show significant differences between LH+ and FSH+ adenomas.

FSHb gene

In the comparison of all groups of pituitary adenomas the highest expression of FSHb gene was noticed in FSH+ adenomas. The difference of expression level was significant only for comparison of FSH+/null (28×↑; p = 0.006) (Fig. 4).

Figure 4. Comparison of FSHb expression in FSH+ to IHC negative (null) pituitary adenomas, (U Mann Whitney test, p = 0.006)
Rycina 4. Porównanie ekspresji genu FSHb w gruczolakach FSH+ w porównaniu z gruczolakami IHC ujemnymi (null), (test U Manna Whitneya, p = 0.006)

CGA gene

In the case of CGA gene the highest expression was observed in LH+ adenomas, while the lowest was in GH+ adenomas. Expression of CGA gene was on a similar level in TSH+ and FSH+ adenomas. We did not observe significant differences in comparisons of: FSH+ and LH+ adenomas, FSH+ and TSH+ adenomas, and TSH+ and LH+ adenomas. Gene CGA was significantly overexpressed in LH+ adenomas in comparison to null adenomas (13×↑; p = 0.0008) (Fig. 5).

Figure 5. Comparison of CGA expression in LH+ to IHC negative (null) pituitary adenomas, (U Mann Whitney test, p = 0.0008)
Rycina 5. Porównanie ekspresji genu CGA w gruczolakach LH+ w porównaniu z gruczolakami IHC ujemnymi (null), (test U Manna Whitneya, p = 0.0008)

Discussion

One of the first results of a microarray study in pituitary adenomas was published by Evans et al. in 2001 [8]. In recent years researchers have used this high-throughput technique to compare gene expression between normal tissues and pituitary adenomas, and identified many genes associated with particular tumour types [9–15],

Our study was intended to evaluate gene expression of pituitary hormone and their precursor genes: PRL, GH, POMC, TSHb, FSHb, LHb, and CGA in particular types of functional and non-functional pituitary adenomas using QPCR technique.

In most cases we obtained results that were consistent with the secretory status of the adenoma subtype. However, comparison of our results with other published studies is rather difficult because of the methods we used. Generally, pituitary adenomas are classified in immunohistochemical tests [16, 17], and molecular techniques are not used. We found confirmation of our results in a studies by Evans at al. [8] and Morris et al. [9], in which gene expression profile was analysed by microarray in different types of pituitary adenomas (normal pituitary, PRL+ , GH+, and ACTH+). For PRL gene these analyses showed, in comparison to normal pituitary, overexpression in PRL+ adenomas (fold change 2.9×; 3.7× respectively), underexpression in non-functional adenomas (fold change 31.3×; > 10× respectively), ACTH+ adenomas (fold change 10.7×; 2.4× respectively), and in GH+ adenomas (fold change 2.2×; 4.5× respectively). In our study PRL gene showed the highest expression in pituitary adenomas that secreted GH, and prolactinomas were in the third place. Our comparison of all functional and immunohistochemically-negative adenomas showed 3.5× higher expression in FA; however, the difference was not significant. The results indicate that PRL gene is not a characteristic gene for prolactinomas.

In the case of GH gene we observed higher expression in GH+ adenomas in comparison to other groups of pituitary adenomas. According to our expectations, this gene was significantly overexpressed in GH+ adenomas when we compared it with PRL + (3674×) and also with immunohistochemically-negative adenomas (23618×). Our results are consistent with the results of Evans and Morris, who showed overexpression of GH gene in somatotropinomas in comparison to normal pituitary (2× and 9.6×, respectively) and underexpression in prolactinomas (2.2× and 10×, respectively), corticotropinomas (2.8× and > 10×, respectively), and nonfunctional adenomas (31.3× and > 10×, respectively). Our obtained results confirm that GH is a characteristic gene for GH+ adenomas. We drew a similar conclusion for POMC gene, expression of which was low in all pituitary adenomas except adrenocorticotropinomas. We observed the same trend in the study of Morris. POMC was the most highly expressed gene in ACTH+ adenomas, whereas its expression was lower in GH+ (> 10×), PRL+ (> 10×), and non-functional adenomas (> 10×). Based on tire results, we confirm that POMC is a characteristic gene for adrenocorticotropinomas.

Another gene which we analysed was TSHb encoding β subunit of TSH. We showed that its expression was higher in TSH+ adenomas than in PRL+, GH+, and FSH+ adenomas, but we did not observe statistically significant differences between groups of functional adenomas. When we compared particular groups of pituitary adenomas with null adenomas we observed significantly higher expression of TSHb gene in TSH+ (3×) and LH+ (3.25×) adenomas. We noticed a similar direction in the above-mentioned studies [8, 9]. TSHb gene was underexpressed in PRL+ (17.2× and > 10×, respectively), GH+ (16.2× and > 10×, respectively), and non-functional adenomas (2.4× and >10×, respectively), in comparison to normal pituitary. The next two genes are expressed in pituitary adenomas that are clinically non-functional, but they can produce glycoprotein hormones: LH or FSH. According to our expectations, expression of LHb and FSHb genes was the highest, respectively, in LH+ and FSH+ adenomas; we noticed lower expression of these genes in PRL+, GF1+, and immunohistochemically-negative adenomas. We found similar results in tire studies by Evans et al. and Morris et al. Expression of LHb gene was lower in PRL+ (4× and 2×, respectively), GF1+ (4.9× and 8×, respectively), and in non-functional adenomas (6.2× and 7.7×, respectively). FSHb gene was underexpressed in PRL+ (10.2× and 2×, respectively), GH+ (23× and 3.6×, respectively), and in non-functional adenomas (2.4× [8]). Although tire differences in LHb and FSHb gene expression were noticeable between particular types of pituitary adenomas our analysis did not show statistically significant differences between LH+ and FSH+ adenomas.

The last CGA gene codes alpha subunit of glycoprotein hormones which is common for FSH, LF1, and TSF1. In our study we did not observe significant differences in CGA expression in particular types of pituitary adenomas that produce glycoprotein hormones.

Conclusions

Our study showed that GH is a characteristic gene for somatotropinomas. We drew a similar conclusion for POMC gene and adrenocorticotropinomas.

However, the results which we obtained for PRL, TSHb, LHb, FSHb and CGA genes indicate that evaluation of gene expression is not sufficient for classification of particular subtypes of pituitary adenomas.

References

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