Open Access

Global gene expression in neuroendocrine tumors from patients with the MEN1 syndrome

  • William G Dilley1Email author,
  • Somasundaram Kalyanaraman1,
  • Sulekha Verma2,
  • J Perren Cobb1,
  • Jason M Laramie1 and
  • Terry C Lairmore1, 2
Molecular Cancer20054:9

DOI: 10.1186/1476-4598-4-9

Received: 11 November 2004

Accepted: 03 February 2005

Published: 03 February 2005

Abstract

Background

Multiple Endocrine Neoplasia type 1 (MEN1, OMIM 131100) is an autosomal dominant disorder characterized by endocrine tumors of the parathyroids, pancreatic islets and pituitary. The disease is caused by the functional loss of the tumor suppressor protein menin, coded by the MEN1 gene. The protein sequence has no significant homology to known consensus motifs. In vitro studies have shown menin binding to JunD, Pem, Smad3, NF-kappaB, nm23H1, and RPA2 proteins. However, none of these binding studies have led to a convincing theory of how loss-of-menin leads to neoplasia.

Results

Global gene expression studies on eight neuroendocrine tumors from MEN1 patients and 4 normal islet controls was performed utilizing Affymetrix U95Av2 chips. Overall hierarchical clustering placed all tumors in one group separate from the group of normal islets. Within the group of tumors, those of the same type were mostly clustered together. The clustering analysis also revealed 19 apoptosis-related genes that were under-expressed in the group of tumors. There were 193 genes that were increased/decreased by at least 2-fold in the tumors relative to the normal islets and that had a t-test significance value of p < = 0.005. Forty-five of these genes were increased and 148 were decreased in the tumors relative to the controls. One hundred and four of the genes could be classified as being involved in cell growth, cell death, or signal transduction. The results from 11 genes were selected for validation by quantitative RT-PCR. The average correlation coefficient was 0.655 (range 0.235–0.964).

Conclusion

This is the first analysis of global gene expression in MEN1-associated neuroendocrine tumors. Many genes were identified which were differentially expressed in neuroendocrine tumors arising in patients with the MEN1 syndrome, as compared with normal human islet cells. The expression of a group of apoptosis-related genes was significantly suppressed, suggesting that these genes may play crucial roles in tumorigenesis in this syndrome. We identified a number of genes which are attractive candidates for further investigation into the mechanisms by which menin loss causes tumors in pancreatic islets. Of particular interest are: FGF9 which may stimulate the growth of prostate cancer, brain cancer and endometrium; and IER3 (IEX-1), PHLDA2 (TSS3), IAPP (amylin), and SST, all of which may play roles in apoptosis.

Background

Multiple Endocrine Neoplasia type 1 (MEN1, OMIM 131100) is an autosomal dominant disorder characterized by endocrine tumors of parathyroid, pancreatic islets and pituitary [1]. The prevalence of MEN1 is estimated to be 2–10 per 100,000 [2]. Based on loss of heterozygosity in tumors and Knudson's "two-hit" hypothesis, the MEN1 gene was classified as a tumor suppressor [2, 3] and the gene was isolated in 1997 by positional cloning [4]. The MEN1 gene spans 9 kb of the genome, is comprised of 10 exons, and codes for a 610 amino acid protein termed menin [4]. More than 300 independent germline and somatic mutations have been identified [5]. Recently, five new germline mutations which affect splicing of pre-mRNA transcribed from MEN1 gene were identified in our laboratory [6]. The nature of all the disease-inducing mutations points to a loss of function of menin, which is characteristic of a tumor suppressor. Database analysis of menin protein sequence reveals no significant homology to known consensus protein motifs. Menin is widely expressed in both endocrine and non-endocrine tissues [4]. Menin is primarily localized in the nucleus and contains two nuclear localization signal sequences near the carboxyl terminus of the protein [7].

Studies on the function of menin have not yielded a clear picture as to the role of menin as a tumor suppressor; however, the results of these studies suggest some interesting possibilities. Two groups [8, 9], based on yeast two-hybrid screening of a human adult brain library, reported that menin interacts with JunD (a member of the AP-1 transcription factor family) and represses JunD mediated transcription. Recently, Agarawal et al[10] reported that when JunD loses its association with menin it becomes a growth promoter rather than a growth suppressor. Other reports suggest some relevance of the menin-JunD interaction. JunD null male mice exhibit impaired spermatogenesis [11]. In postnatal mouse, Men1 was found to be expressed in testis (spermatogonia) at high levels [12]. Lemmens et al [13] by screening a 12.5 dpc mouse embryo library with menin, identified a homeobox-containing mouse protein, Pem. Interestingly, both menin and Pem showed a very similar pattern of expression, especially in testis and Sertoli cells. These findings along with the fact that some MEN1 patients have idiopathic oligospermia and non-motility of spermatozoa [14] suggest that menin-JunD and menin-PEM interactions may play a vital role in spermatogenesis. Kaji et al [15] observed that menin interacts with Smad3 and inactivation of the former blocks transforming growth factor beta (TGF-β) signaling in pituitary tumor derived cell lines. Recently, two more menin interacting proteins, NF-kappa B [16] and a putative tumor metastasis suppressor nm23 [17] have been identified. Interactions among AP-1 family members, Smad proteins and NF-kappa B have been documented [1821] and such cross talk among signaling pathways is not uncommon.

Despite the above studies, a clear consensus of the molecular mechanisms leading to neoplasia, following the loss of menin, has not emerged. Very little is known about the gene expression changes in human neuroendocrine tumors following the loss of menin. Global gene expression analyses, using cDNA microarrays, have been used to classify other human tumors into clinically distinct categories [2226]. Wu [27] has discussed the mathematical and statistical considerations for the use of DNA microarrays to identify genes of specific interest, and Harkin [28] has used expression profiling to identify downstream transcriptional targets of the BRCA1 tumor suppressor gene. Our objective was to identify genes that might be directly or indirectly over or under-expressed as a consequence of loss of menin expression.

Results

Patients and Controls

Eight neuroendocrine tumors from six MEN1 patients were included in this study. The patient ages were 19, 22, 42, 51, 57, and 57 years at the time of surgery (Table 1). One was female, and five were male. Two of the patients had clinical and laboratory findings consistent with insulinoma. Three tumors were analyzed from one of these patients. One patient had findings consistent with VIP-oma (vasoactive intestinal polypeptide secreting tumor). Two patients, with no specific symptoms, had non-functioning or pancreatic polypeptide secreting tumors. One patient had symptoms of gastrinoma from a duodenal tumor (not used for this analysis). A pancreatic tumor from this patient, found incidentally, was used in this study. Pathological examination of tumors from the 6 patients resulted in the classification of 3 insulinomas, 3 neuroendocrine tumors, 1 VIP-oma and 1 glucagonoma. The ages of the individuals donating normal pancreatic islets were 42, 52(2), and 56 years. Two were female, and two were male.
Table 1

Characteristics of patients and normal subjects.

Pt.#

T #

Age

Sex

Clinical

LN Mets

T Vol. (ml)

Menin Defect [6]

1

1

19

F

Insulinoma

0/1

8.28

Large Deletion, exon 1 & 2

2

2

42

M

Neuroendocrine Tumor

0/14

18.75

Nonesense Mutation, exon 7

6

6

60

M

VIP-oma

1/16

288

8 bp Deletion, exon 5

7

7

51

M

Neuroendocrine Tumor

2/30

3.75

2 bp Deletion, exon 2

8

8–10

22

M

Insulinoma

2/8

6.9

2 bp Deletion, exon 2

11

11

57

M

Gastrinoma

1/1

0.5

4 bp Deletion, exon 3

N1

N1

52

M

Normal

NA

NA

NA

N2

N2

56

F

Normal

NA

NA

NA

N3

N3

52

F

Normal

NA

NA

NA

N4

N4

42

M

Normal

NA

NA

NA

Quality of Hybridization

The RNA isolated from 8 tumor specimens (6 patients) and 4 normal islet preparations was of acceptable quality for hybridization, as determined by preliminary small hybridizations on test chips. The dChip computer program returned data concerning the percent of genes judged to be present, and the percent of single and array outlier events (Table 2). The expression data from one normal islet preparation had 5.94% array outliers, which prompted dChip to issue a warning (a warning indicates more than 5% array outliers detected). However, since we had only four normal specimens, we elected to include all four in our analysis. The average level of gene expression was computed for each gene (Figure 1). The average gene expression level for all genes followed an exponentially decreasing pattern; the greatest number of genes had expression values less than 100, and only a few genes had expression levels greater than 4000.
Table 2

Overall statistics on the quality of each the processed GeneChips. One chip was used for each tumor/normal specimen. The "Median Intensity" refers to the overall brightness of the fluorescence of the genes. The "Present Call" refers to whether the gene was "present" or "absent".

Chip Name

Median Intensity

Present Call (%)

Array outlier %

Single outlier %

Warning

T1

170

49.4

1.12

0.11

 

T2

107

46.2

1.54

0.15

 

T6

160

51.4

1.16

0.12

 

T7

132

47.7

0.50

0.08

 

T8

158

51.0

0.59

0.10

 

T9

114

48.9

0.66

0.10

 

T10

158

50.6

0.42

0.07

 

T11

121

46.1

3.34

0.30

 

N1

142

48.4

2.65

0.26

 

N2

179

49.7

2.72

0.24

 

N3

75

48.3

3.38

0.31

 

N4

73

33.2

9.50

0.63

*

Figure 1

Histogram showing the frequency of genes being expressed at levels between 50 and 7875 (arbitrary expression units).

Overall Consistency of Gene Expression

Average expression and standard deviation was computed for each gene in both the group of 4 normal islets, and the group of 8 islet tumors and expressed as the coefficient of variation (CV). Genes with average expression levels less than 50 were excluded from this analysis. Figure 2 shows that the average (11,416 genes and expressed sequences) CV in the group of 8 tumors was 30%. There was a linear regression of CV values as the average minimum expression level of the genes increased. Genes with an average minimum expression level of 7000 or more had an average CV level of 12.7%. The analysis of genes expressed in the normal islets gave similar results. However, when the tumors were combined with the normals, the CV was higher than either group alone. This was caused by the true differences in gene expression levels between the tumors and the normals.
Figure 2

Coefficient of variation (CV) of genes being expressed at levels between 50 and 6000. For each gene expressed at an average level of 50 or above, the CV was computed for the group of 8 tumors, for the group of 4 normals, and for the group of all 12 tumors and normals. As the lower limit of expression was increased, the number of genes represented in the CV decreased: there were 12,000 genes with expression levels of 50 or more, but only a few genes with expression levels of 6,500 or more.

Clustering

The experimental groups were clustered (figure 3) using a hierarchical clustering procedure [29, 30]. This cluster was based on the inclusion of all genes which had 33% to 67% of "present" calls made by the GeneChip software. The assignment of tumor type was made on the basis of principal hormone messenger RNA levels that were consistent with the clinical and biochemical findings (Table 3). The principal bifurcation in the clustering occurred between the group that included the normal specimens and the three tumors with a predominance of insulin expression, on one hand, and the other tumor types on the other. The four normal islet preparations clustered together, separate from the tumors. Among the normal islets, the females clustered separately from the males. Among the tumors, all 3 insulinomas clustered together, separate from the VIP-oma, the glucagonoma and the PP-omas (pancreatic polypeptide producing tumors). It is also interesting that all the specimens clustered in a pattern of increasing malignancy going from normal at the bottom of the cluster to most malignant at the top.
Table 3

Gene expression levels of islet hormone mRNAs in tumors and normals. VIP: Vasoactive intestinal polypeptide; PP: Pancreatic polypeptide.

 

T1

T2

T6

T7

T8

T9

T10

T11

N1

N2

N3

N4

pre-Gastrin

864

530

678

392

600

383

209

395

1036

775

28

1192

Insulin

9990

13

179

401

10195

240

8971

1831

10010

9752

9580

8158

Glucagon

10

6482

2783

1198

10

8370

10

10

9037

8425

9043

7800

VIP

351

278

10243

374

334

276

362

202

806

436

334

389

PP

246

7257

577

5845

70

1805

211

8895

1897

7605

3598

1177

Figure 3

Clustering of tumors and normals according to overall gene expression patterns. The predominant type of hormone expression (Table 3) is noted for each tumor/normal specimen.

The genes were also clustered by the dChip software. A group of apoptosis-related genes was identified whose expression was significantly correlated with the Tumor/Normal assignment of the data. Twenty-four apoptosis-related genes represented by 26 different Affymetrix probes were identified in the overall hierarchical clustering. Nineteen of these genes were more highly expressed in the normal islets than in the islet tumors (Figure 4). Eighteen of the nineteen under expressed genes in the set of tumors had t-test p values (tumor vs. normal) <= 0.037. All five of the apoptosis-related genes, that were more highly expressed in the tumors, had t-test p values >0.05
Figure 4

Clustering of apoptosis-related genes in tumors (T) and normals (N). Pink indicates strong, white indicates moderate, and blue indicates weak expression.

Evaluation of Student's t-test

Since the Student's t-test was designed to compare only one parameter in two populations, the simultaneous measurement of multiple genes might lead to an excessive number of false positives. In order to empirically determine the potential false positive rate, we started with 923 genes which had a p value <=.05 and repeatedly scrambled the individual tests into groups 4 and 8 and then performed new t-tests. The average number of genes having a p value = < .05 in 20 such scrambles was 51 (5.5% of 923 genes). This was only slightly more than the 46 genes expected (0.05 × 923). We therefore concluded that there was little chance of excess false positives in repeatedly using the Student's t-test.

Hormone Expression Profiles

In order to obtain a better picture of the nature of the tumors and normal islets in this study, the expression levels of the principal hormone RNA of pancreatic islets was examined (Table 3). Tumors 1, 8, and 10 had high levels of insulin expression and came from patients with the clinical diagnosis of insulinoma. Tumor 6 had high levels of VIP and came from a patient with the clinical syndrome of VIP-oma. Tumors 2 and 7 had high levels of pancreatic polypeptide, and came from patients with only a diagnosis of neuroendocrine tumor. Tumor 9, which came from a patient with a clinical diagnosis of insulinoma had a high level of glucagon expression; the clinical diagnosis was apparently due to the other tumor (#8) which did have a high level of insulin expression. One other apparent discrepancy between the clinical diagnosis and hormone expression profile occurred with tumor 11, which had high a level of glucagon expression. This patient had an additional duodenal tumor that was responsible for the gastrin secretion and the clinical diagnosis. All the normal islet preparations had high levels of insulin and glucagon expression, as expected.

Comparison of tumor and normal gene expression

The reporting of differentially expressed genes was restricted to those in which the absolute ratio of Tumor to Normal was greater than or equal to 2, and which had a Student's t-test p value of less than or equal to .005. There were 193 genes that met the criteria. Expressed sequences with no known protein product were not included. There were 45 genes that were increased in the tumors relative to the normals, and 148 genes that were decreased. The fold-change in expression values ranged from +179 to -449. Genes were assigned to functional categories based on the Gene Ontology Consortium assignments http://www.geneontology.org/. There were 16 genes related to cell growth, 13 genes related to signal transduction, and 16 genes related to other functions which were increased in the group of tumors relative to the group of normal islets (Table 4). There were 44 genes related to cell growth, 10 related to cell death, 10 related to embryogenesis, 5 related to nucleic acid binding, 21 related to cell signaling, and 58 related to other functions in the group of genes which were decreased in the islet tumors relative to the controls (Tables 5, 6, 7, 8).
Table 4

Genes significantly increased in tumors.

GeneBank Accession

Gene

Symbol

Normal Mean

Tumor Mean

Fold Change

P value

 

Cell Growth/Cycle

     

X16323

hepatocyte growth factor

HGF

11

116

10.77

0.003305

AB017642

oxidative-stress responsive 1

OSR1

58

428

7.41

0.000819

AL078641

phorbolin-like protein

APOBEC3G

15

92

6.21

0.000158

L17128

gamma-glutamyl carboxylase

GGCX

64

346

5.37

0.000018

D21089

xeroderma pigmentosum, complementation group C

XPC

292

1278

4.38

0.000284

AL050223

vesicle-associated membrane protein 2

VAMP2

360

1533

4.26

0.002196

D38145

prostaglandin I2 synthase

PTGIS

29

121

4.09

0.000448

AF092563

structural maintenance of chromosomes 2-like 1

SMC2L1

58

185

3.21

0.002352

AF006087

actin related protein 2/3 complex, subunit 4

ARPC4

292

865

2.96

0.000565

AC004537

inhibitor of growth family, member 3

ING3

46

114

2.47

0.003976

AF013168

tuberous sclerosis 1

TSC1

35

86

2.45

0.001232

AJ236876

ADP-ribosyltransferase polymerase)-like 2

ADPRTL2

32

76

2.34

0.003874

 

Cell Death/Apoptosis

     

D38435

postmeiotic segregation increased 2-like

PMS2L1

74

193

2.6

0.002976

M61906

phosphoinositide-3-kinase, regulatory subunit

PIK3R1

43

104

2.4

0.004387

 

Signal Transduction

     

U26710

Cas-Br-M ectropic retroviral transforming sequence b

CBLB

21

177

8.4

0.000082

AB010414

guanine nucleotide binding protein, gamma 7

GNG7

59

334

5.68

0.003835

U59913

mothers against decapentaplegic homolog 5

MADH5

14

73

5.22

0.004731

AB004922

Homo sapiens gene for Smad 3

MADH3

93

443

4.76

0.001024

L11672

zinc finger protein 91

ZNF91

428

2007

4.69

0.000376

D14838

fibroblast growth factor 9

FGF9

27

108

3.97

0.000752

W27899

member RAS oncogene family

RAB6B

68

232

3.43

0.00501

U48251

protein kinase C binding protein 1

PRKCBP1

40

127

3.18

0.001999

U90268

cerebral cavernous malformations 1

CCM1

53

151

2.87

0.004392

AL050275

cysteine rich with EGF-like domains

CRELD1

195

543

2.79

0.000828

AB014600

SIN3 homolog B, transcriptional regulator

SIN3B

177

425

2.39

0.001924

M27691

cAMP responsive element binding protein 1

CREB1

107

229

2.15

0.003559

U85245

phosphatidylinositol-4-phosphate 5-kinase, type II, beta

PIP5K2B

244

518

2.12

0.000441

W25793

ring finger protein 3

RNF3

163

326

2

0.004947

 

Nucleic Acid Binding

     

D50912

RNA binding motif protein 10

RBM10

96

443

4.6

0.001925

U41315

makorin, ring finger protein, 4

MKRN4

404

808

2

0.000262

 

Ligand Binding

     

X67155

kinesin-like 5

KIF23

64

368

5.76

0.001584

AB028985

ATP-binding cassette, sub-family A, member 2

ABC1

65

262

4.04

0.001234

Z48482

matrix metalloproteinase 15

MMP15

139

495

3.56

0.003946

 

Enzyme

     

X13794

lactate dehydrogenase B

LDHB

396

1606

4.05

0.000845

X15334

creatine kinase, brain

CKB

939

2083

2.22

0.002008

X60708

dipeptidylpeptidase IV

DPP4

133

291

2.19

0.000697

AC004381

SA homolog

SAH

283

599

2.11

0.000168

AF000416

exostoses-like 2

EXTL2

134

271

2.02

0.001314

 

Embryogenesis

     

U48437

amyloid beta precursor-like protein 1

APLP1

851

2433

2.86

0.001043

U66406

ephrin-B3

EFNB3

168

438

2.6

0.00309

D50840

UDP-glucose ceramide glucosyltransferase

UGCG

85

211

2.5

0.002554

 

Other/Unknown

     

L48215

hemoglobin, beta

HBB

12

2099

178.78

0.001299

J00153

hemoglobin, alpha 1

HBA1

15

1249

82.25

0.001889

U30521

P311 protein

C5orf13

157

453

2.88

0.001431

AB011169

similar to S. cerevisiae SSM4

TEB4

140

300

2.15

0.00154

AL031432

GCIP-interacting protein

P29

99

198

2

0.002036

Table 5

Genes significantly decreased in tumors.

GeneBank Accession

Gene Description

Symbol

Normal Mean

Tumor Mean

Fold Change

P value

 

Cell Growth/Division

     

D17291

regenerating protein I beta

REG1B

6286

13

-499.46

0.000095

X67318

carboxypeptidase A1

CPA1

3928

121

-32.57

0.003205

AI763065

regenerating islet-derived 1 alpha

REG1A

5641

334

-16.88

0.000001

D29990

solute carrier family 7, member 2

SLC7A2

2988

445

-6.72

0.002204

AB017430

kinesin-like 4

KIFF22

1223

316

-3.87

0.000177

Z25884

chloride channel 1

CLCN1

2511

655

-3.84

0.00013

X81438

amphiphysin

AMPH

2686

752

-3.57

0.000002

L03785

myosin, light polypeptide 5

MYL5

207

59

-3.51

0.000233

W28062

guanine nucleotide-exch. Prot. 2

ARFGEF2

66

19

-3.46

0.003602

X52486

uracil-DNA glycosylase 2

UNG2

2555

756

-3.38

0.000514

M81933

cell division cycle 25A

CDC25A

312

96

-3.25

0.000005

M69136

chymase 1

CMA1

360

115

-3.13

0.004413

U90543

butyrophilin

BTN2A1

685

226

-3.04

0.000023

X69086

utrophin

UTRN

1325

457

-2.90

0.000011

AF039241

histone deacetylase 5

HDAC5

1124

393

-2.86

0.000319

U49392

allograft inflammatory factor 1

AIF1

165

58

-2.82

0.000105

U81992

pleiomorphic adenoma gene-like 1

PLAGL1

330

118

-2.80

0.004717

L26336

heat shock 70kD protein 2

HSPA2

90

32

-2.79

0.000689

F27891

cytochrome c oxidase subunit VIa

COX6A2

872

313

-2.79

0.000342

D87673

heat shock transcription factor 4

HSF4

1964

721

-2.73

0.000453

X97795

RAD54-like

RAD54L

392

144

-2.72

0.001345

X92689

UDP-N-acetyl-alpha-D-galactosamine

GALNT3

80

32

-2.50

0.000243

Y08683

carnitine palmitoyltransferase I

CPT1B

1038

420

-2.47

0.000573

U40622

X-ray repair complementing defective repair 4

XRCC4

177

72

-2.45

0.000678

U64315

excision repair, complementation group 4

ERCC4

2122

868

-2.44

0.000045

AB020337

beta 1,3-galactosyltransferase

B3GALT5

1489

635

-2.34

0.002613

U40152

origin recognition complex

ORC1L

3671

1702

-2.16

0.001425

M10943

metallothionein 1F

MT1F

5691

2653

-2.14

0.001707

X79882

major vault protein

MVP

758

376

-2.02

0.001719

AF035960

transglutaminase 5

TGM5

3097

1542

-2.01

0.002951

 

Cell Death/Apoptosis

     

S81914

immediate early response 3

IER3

2209

480

-4.60

0.000307

D80007

programmed cell death 11

PDCD11

457

129

-3.55

0.002358

AF013956

chromobox homolog 4

CBX4

1599

492

-3.25

0.00034

U33284

protein tyrosine kinase 2 beta

PTK2B

693

237

-2.93

0.000763

U90919

likely partner of ARF1

APA1

2687

1021

-2.63

0.000015

X57110

Cas-Br-M retroviral transforming

CBL

1889

784

-2.41

0.000033

AL050161

pro-oncosis receptor

PORIMIN

1178

497

-2.37

0.00031

U40380

presenilin 1

PSEN1

1301

569

-2.29

0.00012

D83699

harakiri, BCL2 interacting protein

HRK

768

338

-2.27

0.001321

U07563

v-abl viral oncogene homolog 1

ABL1

1415

631

-2.24

0.000248

M95712

v-raf oncogene homolog B1

BRAF

338

157

-2.16

0.004207

M16441

lymphotoxin alpha

LTA

2106

985

-2.14

0.000239

AF035444

pleckstrin homology-like domain, family A, member 2

PHLDA2

334

166

-2.01

0.001759

Table 6

Genes significantly decreased in tumors (continued).

GeneBank Accession

Gene Description

Symbol

Normal Mean

Tumor Mean

Fold Change

P value

 

Signal Transduction

     

J00306

somatostatin

SST

7701

284

-27.09

0

AI636761

somatostatin

SST

7224

598

-12.09

0.000001

AB011143

GRB2-associated binding protein 2

GAB2

2237

402

-5.57

0.001816

M93056

serine (or cysteine) proteinase inhibitor

SERPINB1

505

105

-4.80

0.004637

X68830

islet amyloid polypeptide

IAPP

2231

477

-4.68

0.001221

AB029014

RAB6 interacting protein 1

RAB6IP1

824

181

-4.56

0.000155

AI198311

neuropeptide Y

NPY

610

154

-3.96

0.004817

M28210

member RAS oncogene family

RAB3A

2566

672

-3.82

0.000048

J04040

glucagon

GCG

8620

2351

-3.67

0.000396

AF030335

purinergic receptor P2Y

P2RY11

2314

680

-3.40

0.000058

M29335

major histocompatibility complex

HLA-DOA

906

268

-3.39

0.00159

L38517

Indian hedgehog homolog

IHH

3013

897

-3.36

0.000055

U95367

gamma-aminobutyric acid A receptor, pi

GABRP

668

202

-3.30

0.000837

W28558

pleiotropic regulator 1

PLRG1

704

216

-3.26

0.000068

L08485

gamma-aminobutyric acid A receptor, alpha 5

GABRA5

342

107

-3.20

0.000336

AF004231

leukocyte immunoglobulin-like receptor

LILRB2

93

30

-3.08

0.001105

AF055033

insulin-like growth factor binding protein 5

IGFBP5

126

43

-2.96

0.000257

AJ010119

ribosomal protein S6 kinase

RPS6KA4

1532

522

-2.94

0.000201

U46194

Human renal cell carcinoma antigen

RAGE

2057

754

-2.73

0.000324

L13858

son of sevenless homolog 2

SOS2

964

354

-2.72

0.000268

Z29572

tumor necrosis factor receptor superfamily

TNFRSF17

184

68

-2.69

0.000178

U01134

fms-related tyrosine kinase 1

FLT1

910

379

-2.40

0.003257

D78156

RAS p21 protein activator 2

RASA2

327

144

-2.26

0.002332

U77783

glutamate receptor

GRIN2D

518

240

-2.15

0.001379

D49394

5-hydroxytryptamine receptor 3A

HTR3A

197

98

-2.02

0.002493

 

Nucleic Acid Binding

     

Z30425

nuclear receptor subfamily 1, group I, member 3

NR1I3

1008

356

-2.83

0.000329

U18760

nuclear factor I/X

NFIX

5796

2216

-2.62

0.000711

AI223140

purine-rich element binding protein A

PURA

1137

506

-2.25

0.002448

AF015950

telomerase reverse transcriptase

TERT

561

255

-2.20

0.002839

U40462

zinc finger protein, subfamily 1A, 1

ZNFN1A1

662

308

-2.15

0.001171

Z93930

X-box binding protein 1

XBP1

2223

1061

-2.09

0.000277

AB019410

PET112-like

PET112A

1422

707

-2.01

0.001309

 

Ligand Binding

     

X00129

retinol binding protein 4, plasma

RBP4

1517

68

-22.27

0.004809

AJ223317

sarcosine dehydrogenase

SARDH

3844

1069

-3.60

0.000085

AB017494

LCAT-like lysophospholipase

LYPLA3

906

326

-2.78

0.001131

U78735

ATP-binding cassette, sub-family A, member 3

ABCA3

1914

706

-2.71

0.000288

AF026488

spectrin, beta, non-erythrocytic 2

SPTBN2

1604

671

-2.39

0.00005

U83659

ATP-binding cassette, sub-family C, member 3

ABCC3

1287

551

-2.34

0.00244

R93527

metallothionein 1H

MT1H

5093

2196

-2.32

0.002937

AA586894

S100 calcium binding protein A7

S100A7

507

221

-2.29

0.000537

U91329

kinesin family member 1C

KIF1C

2981

1484

-2.01

0.000518

Table 7

Genes significantly decreased in tumors (continued).

GeneChip Accession

Gene Description

Symbol

Normal Mean

Tumor Mean

Fold Change

P value

 

Enzyme

     

M81057

carboxypeptidase B1

CPB1

4534

79

-57.09

0.001106

X71345

protease, serine, 4

PRSS3

3859

76

-51.11

0.004102

X01683

serine (or cysteine) proteinase inhibitor, clade A

SERPINA1

2550

74

-34.64

0.004833

M24400

chymotrypsinogen B1

CTRB1

5158

207

-24.95

0.001744

M18700

elastase 3A, pancreatic

ELA3A

7058

384

-18.37

0.000009

U66061

protease, serine, 1

PRSS1

7291

645

-11.31

0.000047

L22524

matrix metalloproteinase 7

MMP7

595

54

-11.03

0.002591

AI655458

5-oxoprolinase (ATP-hydrolysing)

OPLAH

446

99

-4.52

0.004072

H94881

FXYD domain-containing ion transport regulator 2

FXYD2

3116

708

-4.40

0.000539

AL021026

flavin containing monooxygenase 2

FMO2

905

215

-4.21

0.000804

AC005525

plasminogen activator, urokinase receptor

PLAUR

1779

566

-3.14

0.000031

U40370

phosphodiesterase 1A, calmodulin-dependent

PDE1A

268

89

-3.03

0.004023

R90942

sialyltransferase 7D

SIAT7D

3148

1052

-2.99

0.002319

M84472

hydroxysteroid (17-beta) dehydrogenase 1

HSD17B1

1196

440

-2.72

0.000991

X55988

ribonuclease, RNase A family, 2

RNASE2

480

203

-2.36

0.001314

AB003151

carbonyl reductase 1

CBR1

4538

1945

-2.33

0.000511

X08020

glutathione S-transferase M1

GSTM1

2766

1376

-2.01

0.000519

 

Embryogenesis

     

U15979

delta-like homolog

SIGLEC5

3384

402

-8.41

0.002927

M60094

H1 histone family, member T

HIST1H1T

976

230

-4.23

0.001639

U50330

bone morphogenetic protein 1

BMP1

3298

973

-3.39

0.001637

M74297

homeo box A4

HOXA4

501

176

-2.85

0.000477

AJ011785

sine oculis homeobox homolog 6

SIX6

530

190

-2.79

0.000286

U66198

fibroblast growth factor 13

FGF13

191

73

-2.61

0.001068

D31897

double C2-like domains, alpha

DOC2A

1151

451

-2.55

0.000068

U12472

glutathione S-transferase pi

GSTP1

3122

1524

-2.05

0.000237

 

Transcription

     

AL049228

pleckstrin homology domain interacting protein

PHIP

257

33

-7.69

0.000782

M27878

zinc finger protein 84

ZNF84

54

15

-3.64

0.001108

U77629

achaete-scute complex-like 2

ASCL2

438

184

-2.38

0.000058

D50495

transcription elongation factor A, 2

TCEA2

1330

595

-2.23

0.000019

U49857

transcriptional activator of the c-fos promoter

CROC4

542

259

-2.09

0.003894

Table 8

Genes significantly decreased in tumors (continued).

GeneBank Accession

Gene Description

Symbol

Normal Mean

Tumor Mean

Fold Change

P value

 

Other/Undefined

     

X72475

immunoglobulin kappa constant

IGKC

1409

276

-5.11

0.000111

D17570

zona pellucida binding protein

ZPBP

355

71

-5.02

0.001107

M90657

transmembrane 4 superfamily member 1

TM4SF1

592

141

-4.20

0.004537

AF063308

mitotic spindle coiled-coil related protein

SPAG5

2015

502

-4.01

0.000588

U66059

T cell receptor beta locus

TRB@

3022

779

-3.88

0.000266

AL022165

carbohydrate sulfotransferase 7

CHST7

359

94

-3.82

0.001738

U10694

melanoma antigen, family A, 9

MAGEA9

1039

272

-3.82

0.000067

M73255

vascular cell adhesion molecule 1

VCAM1

80

22

-3.66

0.004179

U47926

leprecan-like 2 protein

LEPREL2

1003

319

-3.15

0.00013

L05424

CD44 antigen

CD44

1439

471

-3.05

0.001361

AI445461

transmembrane 4 superfamily member 1

TM4SF1

463

161

-2.88

0.002911

AF010310

proline oxidase homolog

PRODH

1194

421

-2.84

0.000005

AF000991

testis-specific transcript, Y-linked 2

TTTY2

700

254

-2.76

0.000542

X57522

transporter 1, ATP-binding cassette, sub-family B

TAP1

781

287

-2.72

0.000971

AA314825

trefoil factor 1

TFF1

1657

616

-2.69

0.000011

AB020880

squamous cell carcinoma antigen

SART3

3228

1224

-2.64

0.000135

AF040707

homologous to yeast nitrogen permease

NPR2L

1131

437

-2.59

0.001537

U47292

trefoil factor 2

TFF2

359

141

-2.54

0.000684

X69398

CD47 antigen

CD47

350

144

-2.42

0.000853

U27331

fucosyltransferase 6

FUT6

1105

473

-2.34

0.000872

AI827730

cyclin M2

CNNM2

5863

2535

-2.31

0.000484

U05255

glycophorin B

GYPB

1606

717

-2.24

0.00013

M34428

pvt-1 oncogene homolog, MYC activator

PVT1

1231

550

-2.24

0.004423

U86759

netrin 2-like

NTN2L

2039

937

-2.18

0.000204

D90278

CEA-related cell adhesion molecule 3

CEACAM3

4388

2024

-2.17

0.000902

L40400

ZAP3 protein

ZAP3

1549

719

-2.15

0.000776

U48224

beaded filament structural protein 2, phakinin

BFSP2

568

271

-2.10

0.000166

AI138834

deltex homolog 2

DTX2

311

148

-2.10

0.000687

M13755

interferon-stimulated protein, 15 kDa

G1P2

1507

741

-2.03

0.001157

X52228

mucin 1, transmembrane

MUC1

1523

756

-2.02

0.001707

Validation of GeneChip Data with Quantitative RT-PCR

In order to evaluate how accurately the GeneChip data was representing actual gene expression levels, eleven genes were tested with quantitative RT-PCR (Q-PCR). The results are shown in Table 9. The correlation coefficients ranged from 0.964 to 0.235 with an average of 0.655. The lower correlation coefficients were associated with genes with larger numbers of exons. There was some association of low correlation with low average numerical expression values. The lowest correlations were associated with very faint image intensity of the involved genes in the dChip visual representation. The correlation coefficients of 4 genes, identified as apoptosis-related, was examined in detail (Figure 5). IER3, IAPP, SST, and PHLDA2 all had good correlation between GeneChip and Q-PCR results. FGF9, a potential growth stimulating gene was also examined (Figure 6). Again, there was overall good correlation between the individual GeneChip and Q-PCR results.
Table 9

Correlation of GeneChip expression with quantitative RT-PCR.

Gene Symbol

Correlation

Probe Set

Exons

Gene Size (bp)

Fold Change (T/N)

P value GeneChip T vs. N

IER3

0.964

1237_at

1

1236

-4.6

0.0000

SST

0.925

37782_at

2

351

-12

0.0000

PHLDA2

0.909

40237_at

2

913

-2.01

0.0003

REG1B

0.875

35981_at

6

773

-499

0.0000

IAPP

0.823

37871_at

3

1462

-4.68

0.0033

REG1A

0.814

38646_s_at

6

808

-16.9

0.0000

FGF9

0.74

1616_at

3

1420

3.97

0.0031

CBLB

0.327

514_at

21

3923

3.01

0.0009

XPC

0.318

1873_at

16

3658

4.38

0.0018

HRK

0.273

34011_at

2

716

-2.27

0.0011

PTK2B

0.235

2009_at

38

4715

-2.94

0.0019

Average

0.655

     
Figure 5

The expression levels of 4 apoptosis-related genes are shown by GeneChip and quantitative RT-PCR: a) IER3; b) IAPP; c) SST; d) PHLDA2. Normals (N) and tumors (T) are shown. Solid bars represent GeneChip and open bars represent Q-PCR results.

Figure 6

FGF9 expression levels in tumors (T) and normals (N) by GeneChip and quantitative RT-PCR. Solid bars represent GeneChip and open bars represent Q-PCR results.

Discussion

Whether there were degradative processes acting on the tissues prior to or during or after the extraction of the RNA can be guessed by the quality of the RNA. Each RNA specimen in this study was tested on an Affymetrix test chip, and each was found to be acceptable. Additional quality assessment was made by the dChip software. Only one specimen, a normal control, had Array Outliers greater than 5%, suggesting that it was subnormal (Table 2). However, since the percent outliers was only 5.94, the chip was included in the analysis.

Although, only solid tumor was utilized, there were undoubtedly a small percentage of blood, blood vessel, and connective tissue elements intermixed with the tumor tissue. Rarely, there might be a small amount of exocrine tissue. In the case of the normal islets used as controls, microscopic examination showed that greater than 90% of the tissue was islet. Any contaminants would probably have the effect of reducing the discriminant power to differentiate tumor from normal. Thus, t-test p values and fold changes would tend to under-represented and some, otherwise significant, genes might be missed. The actual data, represented by the hierarchical specimen clustering (Figure 3), showed strong differential gene expression relating to group identity as would be expected if the overall gene expression levels were accurate. All the normals clustered together, separate from all the tumors. Within the normals, the two male specimens clustered in one group, and the two female in another. All the normal islet preparations, which are composed predominantly of beta cells, clustered closer to the insulinoma tumors than to the other neuroendocrine tumor types. The gene clustering results revealed 19 apoptosis-related genes whose expression was suppressed in the islet tumors relative to the normals. This suggests that apoptosis may play a significant role in the development of these tumors.

One might have expected more variation in the gene expression levels in the tumors than in the normal islets, since tumors are often heterogonous. However the data on the average CV of the genes in the normal and tumor groups suggested that there was no more variation in the tumors (average CV of 30%) than in the normals (average CV of 31%). The low CV in the tumors may relate to the single mode of tumor formation (induction by the loss of the menin tumor suppressor). However, there was increased variation noted when the tumors and normals were combined (Figure 2). This was probably the result of the differences in expression between the tumors and the normals.

Of particular interest was the high proportion (3/8) of tumors expressing principally PP hormonal RNA. This was entirely consistent with pathological studies showing the preponderance of PP containing tumors in the pancreas of MEN1 patients [31]. The fact that the clinical classification of two patients (9 and 11) was different than indicated by the hormone expression profile of the tumor analyzed was a consequence of the facts that those patients had multiple tumors secreting multiple hormones but only insulin and gastrin and sometime PP over secretion are likely to result in a clinical diagnosis.

The use of the Students t-test for comparison of multiple genes might be questioned because the test was designed for comparison of only two groups. In this study, we confirmed that comparison of 923 genes would not generate an excess number of false positive results. Nevertheless, in the group of 193 genes finally selected at a p < = .005, we can expect that 1 of those genes is a false positive.

This study suggests that the overall effect of loss of function of menin is the suppression of gene expression. Nevertheless, there were 86 genes that were over-expressed in the tumors relative to the normals. Although we associate tumorigenesis with increased rates of growth, only two of eleven Cell Cycle and Cell Proliferation genes were increased in the tumors. Since tumor growth may also be significantly affected by rates of cell death, it is perhaps significant that there were no Cell Death genes significantly increased in the tumors relative to the controls.

The correlation of GeneChip results with quantitative real-time PCR (Q-PCR, Table 9) was relatively good. However, there were some genes that correlated poorly (correlation coefficient less than 0.6). Interestingly, most of the genes with poor correlation coefficients had a large number of exons, whereas those with high correlation coefficients had a low number of exons. Since exhaustive testing of alternative primer pairs for Q-PCR was not made, it is possible that correlation coefficients of some genes could be improved by the use of other primers.

Four studies of global gene expression in pancreatic islets have been published recently [3235]. Cardozo et al [32] have used microarrays to look for NF-kB dependent genes in primary cultures of rat pancreatic islets. Shalev et al [33] have measured global gene expression in purified human islets in tissue culture under high and low glucose concentrations. They noted that the TGFβ superfamily member PDF was down regulated 10-fold in the presence of glucose, whereas other TGFβ superfamily members were up regulated. In the current study, none of the TGFβ superfamily members were significantly different between tumor and normal. Scearce et al [34] have used a pancreas-specific micro-chip, the PanChip to analyze gene expression patterns in E14 to adult mice. Only a few specific genes were noted in the paper, and none of them had human homologs of significance to the current study. Maitra et al [35] conducted a study which in many ways was similar to the current one. They compared gene expression, using the Affymetrix U133A chip, in a series of sporadic pancreatic endocrine tumors with isolated normal islets. There was no overlap in the genes they identified (having a three-fold or greater difference in expression) with the genes we identified (having a two-fold or greater difference in expression). This is quite surprising, but perhaps suggests that sporadically arising tumors may have a quite different pattern of gene expression than tumors arising as a result of menin loss or dysfunction. Another possible cause of the differences may be the different Affymetrix GeneChips used in the two studies.

The question of which (if any) of the genes delineated in this study are a direct and necessary affect of loss-of-menin tumorigenesis cannot be determined by this study alone. Firstly, the activity of many genes are regulated both by their levels of expression and by post-translation modifications, such as phosphorylation. Secondly, the microchips used in this study represent only about 1/3 of the total number of human genes. Thirdly, it is not certain that the initiating gene changes caused by loss-of-menin are persistent in the tumors that develop. However, there were some genes, which because of their association with growth or apoptosis are of special interest. The general suppression of apoptosis related genes noted in this study (Figure 4) has been highlighted by the recent study of Schnepp et al, [36] who showed a loss of menin suppression of apoptosis in murine embryonic fibroblasts through a caspase-8 mechanism. Specific apoptosis-related genes which were suppressed in the tumors in the current study, and which were confirmed by Q-PCR include IER3, SST, PHLDA2, and IAPP. IER3 (IEX-1) is regulated by several transcription factors and may have positive or negative effects upon cell growth and apoptosis depending upon the cell-specific context [37]. Several studies have shown that it can be a promoter of apoptosis [3840]. Somatostatin has shown a wide range of growth inhibitory activity in vitro and in vivo [4157].PHLDA2 (TSSC3) is an imprinted gene homologous to the murineTDAG51 apoptosis-related gene [58], and may be involved in human brain tumors [59]. IAPP (amylin) is a gene which has contrasting activities and has been associated with experimental diabetes in rodents [60]. Amylin deposits were increased in islets of patients with gastrectomy-induced islet atrophy [61]. On the other hand, exposure of rat embryonic islets to amylin results in beta cell proliferation [62]. In contrast, amylin has been shown to induce apoptosis in rat and human insulinoma cells in vitro [63, 64]. In contrast to the suppression of apoptosis-related genes, FGF9 (Figure 6), a growth promoting gene, was significantly increased in the neuroendocrine tumors. This protein has been reported to play roles in glial cell growth [65], chondrocyte growth [66], prostate growth [67], endometrial growth [68], and has been suggested to have a role in human oncogenesis [69].

A recent report by Busygina et al [70] suggested that loss of menin can lead to hypermutability in a Drosophila model for MEN1. The spectrum of mutation sensitivity suggested that there was a defect in nucleotide excision repair. Whether the defect was a direct or indirect effect of menin loss was not stated. In the current study, there was a 2.44-fold decrease, in the tumors, in the expression of ERCC4 (Table 5), a gene involved in nucleotide excision repair. In addition, XRCC4, a gene involved in double-strand break repair, was also decreased in the tumors in the current study.

Conclusion

This first study of global gene expression in neuroendocrine tumors arising in the pancreas of patients with the MEN1 syndrome has identified many genes that are differentially expressed, as compared with normal human islet cells. A number of these genes are strongly over/under expressed and are attractive candidates for further investigation into the mechanisms by which menin loss causes tumors in pancreatic islets. Of particular interest was a group of 24 apoptosis-related genes that were significantly differentially expressed (mostly underexpressed) in the group of neuroendocrine tumors. The underexpression of these apoptosis-related genes may be related to neoplastic development or progression in these MEN1-related neuroendocrine tumors.

Methods

Human Tissue Specimens

Tumor specimens were obtained from patients with the MEN1 syndrome who had undergone surgery for islet-cell tumors of the pancreas. The specific germline mutations in the menin tumor suppressor gene were identified and previously reported [6] for each of the patients. Six of the patients had frame-shift mutations and one had a nonsense mutation. Informed consent was obtained in advance, and tumor tissues not needed for pathological analysis were snap frozen in liquid nitrogen, and kept frozen at -70° prior to RNA extraction. Normal pancreatic islets (which were originally intended for human transplatation studies, but were not used) were isolated from brain-dead donors by a collagenase procedure, as previously described [71], and were then frozen until used for extraction of RNA. Human Studies Committee approval from Washington University School of Medicine was obtained for this study.

Isolation of RNA from Tissue Specimens

Approximately 50 mg of tissue was removed from each frozen tumor specimen and homogenized with a mortar and pestle (Qiagen, Qiashredder Kit), and RNA was extracted using the Rneasy Mini Kit (Qiagen, Inc.), and quantified by UV absorbance. RNA was similarly isolated from the normal human islet preparations.

GeneChip Hybridization and Analysis

The RNA was submitted to the GeneChip facility of the Siteman Cancer Center at Washington University School of Medicine. There, biotin labeled cRNA was prepared and hybridized to U95Av2 microarray chips (Affymetrix). The fluorescence of individual spots was then measured and the data returned on compact discs. We analyzed the gene expression data and made comparisons between groups using the dChip computer program [30]. Following normalization (to equalize the overall intensity of each chip), the expression of each gene was determined by statistical modeling procedure in the dChip software. Each gene was represented by an array of 10 perfect match oligonucleotide spots and 10 mismatch oligonucleotide spots on the U95Av2 chip. The dChip program examines all the spots on all the chips involved in the study, and by a statistical procedure determines single and array outliers. These outliers can be considered as "bad" readings, and removed from further consideration.

Quantitative RT-PCR

The same preparations of total RNA that were used to probe the GeneChips were also used to prepare c-DNA for quantitative RT-PCR analysis of gene expression. C-DNA was first prepared using Superscript II reverse transcriptase (Invitrogen, Inc.). Primers for each gene were designed to produce products of 100 to 150 bp that spanned exon boundaries (when possible). The primer pairs are shown in table 10.

Table 10

Gene

Forward Primer

Reverse Primer

CBLB

cacgtctaaatctatagcagccagaac

tgcactcccaagcctcttctc

FGF9

cggcaccagaaattcacaca

aaattgtctttgtcaactttggcttag

HRK

agctggttcccgttttcca

cagtcccattctgtgtttctacgat

IAPP

ctgctttgtatccatgagggttt

gaggtttgctgaaagccacttaa

ER3

ccagcatctcaactccgtctgt

caccctaaaggcgacttcaaga

SST

cccagactccgtcagtttctg

tacttggccagttcctgcttc

PHLDA2

tgcccattgcaaataaatcact

ctgcccgcccattcct

PTK2B

gtgaggagtgcaagaggcagat

gccagattggccagaacct

REG1A

cctcaagcacaggattccaga

acatgtattttccagctgcctcta

REG1B

gggtccctggtctcctacaagt

catttcttgaatcctgagcatgaa

XPC

gcccgcaagctggacat

atcagtcacgggatgggagta

The Sybr Green technique on an Applied Biosystems model GeneAmp 5700 instrument was utilized. Relative quantitation using a standard c-DNA preparation from an in vitro islet tumor cell line was utilized.

Declarations

Acknowledgements

We would like to thank the Alvin J. Siteman Cancer Center at Washington University School of Medicine and Barnes-Jewish Hospital in St. Louis, Missouri, for the use of the Multiplexed Gene Analysis Core, which provided the GeneChip processing service. The Siteman Cancer Center is supported in part by an NCI Cancer Center Support Grant #P30 CA91842.

Portions of this work were supported by grant RPG-99-183-01-CCE (TCL) from the American Cancer Society and a Siteman Cancer Center Research Development Award.

Authors’ Affiliations

(1)
Department of Surgery, Washington University School of Medicine
(2)
John Cochran Veterans Administration Medical Center

References

  1. Wermer P: Genetic aspects of adenomatosis of the endocrine glands. Am J Med. 1954, 16: 363-371.View ArticlePubMedGoogle Scholar
  2. Marx SJ, Agarwal SK, Kester MB, Heppner C, Kim YS, Skarulis MC, James LA, Goldsmith PK, Saggar SK, Park SY, Spiegel AM, Burns AL, Debelenko LV, Zhuang Z, Lubensky IA, Liotta LA, Emmert-Buck MR, Guru SC, Manickam P, Crabtree J, Erdos MR, Collins FS, Chandrasekharappa SC: Multiple endocrine neoplasia type 1: clinical and genetic features of the hereditary endocrine neoplasias. Recent Prog Horm Res. 1999, 54: 397-438; discussion 438-9.PubMedGoogle Scholar
  3. Knudsen AGJ: Mutation and Cancer. Proc Natl Acad Sci USA. 1971, 68: 820-823.View ArticleGoogle Scholar
  4. Chandrasekharappa SC, Guru SC, Manickam P, Olufemi SE, Collins FS, Emmert-Buck MR, Debelenko LV, Zhuang Z, Lubensky IA, Liotta LA, Crabtree JS, Wang Y, Roe BA, Weisemann J, Boguski MS, Agarwal SK, Kester MB, Kim YS, Heppner C, Dong Q, Spiegel AM, Burns AL, Marx SJ: Positional cloning of the gene for multiple endocrine neoplasia-type 1. Science. 1997, 276: 404-407.View ArticlePubMedGoogle Scholar
  5. Schussheim DH, Skarulis MC, Agarwal SK, Simonds WF, Burns AL, Spiegel AM, Marx SJ: Multiple endocrine neoplasia type 1: new clinical and basic findings. Trends Endocrinol Metab. 2001, 12: 173-178.View ArticlePubMedGoogle Scholar
  6. Mutch MG, Dilley WG, Sanjurjo F, DeBenedetti MK, Doherty GM, Wells SAJ, Goodfellow PJ, Lairmore TC: Germline mutations in the multiple endocrine neoplasia type 1 gene: evidence for frequent splicing defects. Hum Mutat. 1999, 13: 175-185.View ArticlePubMedGoogle Scholar
  7. Guru SC, Goldsmith PK, Burns AL, Marx SJ, Spiegel AM, Collins FS, Chandrasekharappa SC: Menin, the product of the MEN1 gene, is a nuclear protein. Proc Natl Acad Sci USA. 1998, 95: 1630-1634.PubMed CentralView ArticlePubMedGoogle Scholar
  8. Agarwal SK, Guru SC, Heppner C, Erdos MR, Collins RM, Park SY, Saggar S, Chandrasekharappa SC, Collins FS, Spiegel AM, Marx SJ, Burns AL: Menin interacts with the AP1 transcription factor JunD and represses JunD-activated transcription. Cell. 1999, 96: 143-152.View ArticlePubMedGoogle Scholar
  9. Gobl AE, Berg M, Lopez-Egido JR, Oberg K, Skogseid B, Westin G: Menin represses JunD-activated transcription by a histone deacetylase-dependent mechanism. Biochim Biophy Acta. 1999, 1447: 51-56.View ArticleGoogle Scholar
  10. Agarwal SK, Novotny EA, Crabtree JS, Weitzman JB, Yaniv M, Burns AL, Chandrasekharappa SC, Collins FS, Spiegel AM, Marx SJ: Transcription factor JunD, deprived of menin, switches from growth suppressor to growth promoter. Proc Natl Acad Sci U S A. 2003, 100: 10770-10775.PubMed CentralView ArticlePubMedGoogle Scholar
  11. Thepot D, Weitzman JB, Barra J, Segretain D, Stinnakre MG, Babinet C, Yaniv M: Targeted disruption of the murine junD gene results in multiple defects in male reproductive function. Development. 2000, 127: 143-153.PubMedGoogle Scholar
  12. Stewart C, Parente F, Piehl F, Farnebo F, Quincey D, Silins G, Bergman L, Carle GF, Lemmens I, Grimmond S, Xian CZ, Khodei S, Teh BT, Lagercrantz J, Siggers P, Calender A, Van de Vem V, Kas K, Weber G, Hayward N, Gaudray P, Larsson C: Characterization of the mouse Men1 gene and its expression during development. Oncogene. 1998, 17: 2485-2493.View ArticlePubMedGoogle Scholar
  13. Lemmens IH, Forsberg L, Pannett AA, Meyen E, Piehl F, Turner JJ, Van de Ven WJ, Thakker RV, Larsson C, Kas K: Menin interacts directly with the homeobox-containing protein Pem. Biochem Biophys Res Commun. 2001, 286: 426-431.View ArticlePubMedGoogle Scholar
  14. Brandi ML, Weber G, Svensson A, Falchetti A, Tonelli F, Castello R, Furlani L, Scappaticci S, Fraccaro M, Larsson C: Homozygotes for the autosomal dominant neoplasia syndrome (MEN1). Am J Hum Genet. 1993, 53: 1167-1172.PubMed CentralPubMedGoogle Scholar
  15. Kaji H, Canaff L, Lebrun JJ, Goltzman D, Hendy GN: Inactivation of menin, a Smad3-interacting protein, blocks transforming growth factor type beta signaling. Proc Nat Acad Sci USA. 2001, 98: 3837-3842.PubMed CentralView ArticlePubMedGoogle Scholar
  16. Heppner C, Bilimoria KY, Agarwal SK, Kester M, Whitty LJ, Guru SC, Chandrasekharappa SC, Collins FS, Spiegel AM, Marx SJ, Burns AL: The tumor suppressor protein menin interacts with NF-kappaB proteins and inhibits NF-kappaB-mediated transactivation. Oncogene. 2001, 20: 4917-4925.View ArticlePubMedGoogle Scholar
  17. Ohkura N, Kishi M, Tsukada T, Yamaguchi K: Menin, a gene product responsible for multiple endocrine neoplasia type 1, interacts with the putative tumor metastasis suppressor nm23. Biochem Biophys Res Commun. 2001, 282: 1206-1210.View ArticlePubMedGoogle Scholar
  18. Liberati NT, Datto MB, Frederick JP, Shen X, Wong C, Rougier-Chapman EM, Wang XF: Smads bind directly to the Jun family of AP-1 transcription factors. Proc Nat Acad Sci USA. 1999, 96: 4844-4849.PubMed CentralView ArticlePubMedGoogle Scholar
  19. Lopez-Rovira T, Chalaux E, Rosa JL, Bartrons R, Ventura F: Interaction and functional cooperation of NF-kappa B with Smads. Transcriptional regulation of the junB promoter. J Biol Chem. 2000, 275: 28937-28946.View ArticlePubMedGoogle Scholar
  20. Bitzer M, von Gersdorff G, Liang D, Dominguez-Rosales A, Beg AA, Rojkind M, Bottinger EP: A mechanism of suppression of TGF-beta/SMAD signaling by NF-kappa B/RelA. Genes Dev. 2000, 14: 187-197.PubMed CentralPubMedGoogle Scholar
  21. Rahmani M, Peron P, Weitzman J, Bakiri L, Lardeux B, Bernuau D: Functional cooperation between JunD and NF-kappaB in rat hepatocytes. Oncogene. 2001, 20: 5132-5142.View ArticlePubMedGoogle Scholar
  22. Alon U, Barkai N, Notterman DA, Gish K, Ybarra S, Mack D, Levine AJ: Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proc Nat Acad Sci USA. 1999, 96: 6745-6750.PubMed CentralView ArticlePubMedGoogle Scholar
  23. Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, Mesirov JP, Coller H, Loh ML, Downing JR, Caligiuri MA, Bloomfield CD, Lander ES: Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science. 1999, 286: 531-537.View ArticlePubMedGoogle Scholar
  24. Alizadeh AA, Ross DT, Perou CM, van de Rijn M: Towards a novel classification of human malignancies based on gene expression patterns. J Pathol. 2001, 195: 41-52.View ArticlePubMedGoogle Scholar
  25. Su AI, Welsh JB, Sapinoso LM, Kern SG, Dimitrov P, Lapp H, Schultz PG, Powell SM, Moskaluk CA, Frierson HFJ, Hampton GM: Molecular classification of human carcinomas by use of gene expression signatures. Cancer Res. 2001, 61: 7388-7393.PubMedGoogle Scholar
  26. Ramaswamy S, Tamayo P, Rifkin R, Mukherjee S, Yeang CH, Angelo M, Ladd C, Reich M, Latulippe E, Mesirov JP, Poggio T, Gerald W, Loda M, Lander ES, Golub TR: Multiclass cancer diagnosis using tumor gene expression signatures. Proc Nat Acad Sci USA. 2001, 98: 15149-15154.PubMed CentralView ArticlePubMedGoogle Scholar
  27. Wu TD: Analysing gene expression data from DNA microarrays to identify candidate genes. J Pathol. 2001, 195: 53-65.View ArticlePubMedGoogle Scholar
  28. Harkin DP: Uncovering functionally relevant signaling pathways using microarray-based expression profiling. Oncologist. 2000, 5: 501-507.View ArticlePubMedGoogle Scholar
  29. Li C, Hung Wong W: Model-based analysis of oligonucleotide arrays: model validation, design issues and standard error application. Genome Biol. 2001, 2: RESEARCH0032PubMed CentralPubMedGoogle Scholar
  30. Li C, Wong WH: Model-based analysis of oligonucleotide arrays: expression index computation and outlier detection. Proc Nat Acad Sci USA. 2001, 98: 31-36.PubMed CentralView ArticlePubMedGoogle Scholar
  31. Komminoth P, Heitz PU, Kloppel G: Pathology of MEN-1: morphology, clinicopathologic correlations and tumour development. J Intern Med. 1998, 243: 455-464.View ArticlePubMedGoogle Scholar
  32. Cardozo AK, Heimberg H, Heremans Y, Leeman R, Kutlu B, Kruhoffer M, Orntoft T, Eizirik DL: A comprehensive analysis of cytokine-induced and nuclear factor-kappa B-dependent genes in primary rat pancreatic beta-cells. J Biol Chem. 2001, 276: 48879-48886.View ArticlePubMedGoogle Scholar
  33. Shalev A, Pise-Masison CA, Radonovich M, Hoffmann SC, Hirshberg B, Brady JN, Harlan DM: Oligonucleotide microarray analysis of intact human pancreatic islets: identification of glucose-responsive genes and a highly regulated TGFbeta signaling pathway. Endocrinology. 2002, 143: 3695-3698.View ArticlePubMedGoogle Scholar
  34. Scearce LM, Brestelli JE, McWeeney SK, Lee CS, Mazzarelli J, Pinney DF, Pizarro A, Stoeckert CJJ, Clifton SW, Permutt MA, Brown J, Melton DA, Kaestner KH: Functional genomics of the endocrine pancreas: the pancreas clone set and PancChip, new resources for diabetes research. Diabetes. 2002, 51: 1997-2004.View ArticlePubMedGoogle Scholar
  35. Maitra A, Hansel DE, Argani P, Ashfaq R, Rahman A, Naji A, Deng S, Geradts J, Hawthorne L, House MG, Yeo CJ: Global expression analysis of well-differentiated pancreatic endocrine neoplasms using oligonucleotide microarrays. Clin Cancer Res. 2003, 9: 5988-5995.PubMedGoogle Scholar
  36. Schnepp RW, Mao H, Sykes SM, Zong WX, Silva A, La P, Hua X: Menin induces apoptosis in murine embryonic fibroblasts. J Biol Chem. 2004, 279: 10685-10691.PubMed CentralView ArticlePubMedGoogle Scholar
  37. Wu MX: Roles of the stress-induced gene IEX-1 in regulation of cell death and oncogenesis. Apoptosis. 2003, 8: 11-18.View ArticlePubMedGoogle Scholar
  38. Arlt A, Grobe O, Sieke A, Kruse ML, Folsch UR, Schmidt WE, Schafer H: Expression of the NF-kappa B target gene IEX-1 (p22/PRG1) does not prevent cell death but instead triggers apoptosis in Hela cells. Oncogene. 2001, 20: 69-76.View ArticlePubMedGoogle Scholar
  39. Schilling D, Pittelkow MR, Kumar R: IEX-1, an immediate early gene, increases the rate of apoptosis in keratinocytes. Oncogene. 2001, 20: 7992-7997.View ArticlePubMedGoogle Scholar
  40. Osawa Y, Nagaki M, Banno Y, Brenner DA, Nozawa Y, Moriwaki H, Nakashima S: Expression of the NF-kappa B target gene X-ray-inducible immediate early response factor-1 short enhances TNF-alpha-induced hepatocyte apoptosis by inhibiting Akt activation. J Immunol. 2003, 170: 4053-4060.View ArticlePubMedGoogle Scholar
  41. Albini A, Florio T, Giunciuglio D, Masiello L, Carlone S, Corsaro A, Thellung S, Cai T, Noonan DM, Schettini G: Somatostatin controls Kaposi's sarcoma tumor growth through inhibition of angiogenesis. FASEB Journal. 1999, 13: 647-655.PubMedGoogle Scholar
  42. Arnold R, Wied M, Behr TH: Somatostatin analogues in the treatment of endocrine tumors of the gastrointestinal tract. Expert Opin Pharmacother. 2002, 3: 643-656.View ArticlePubMedGoogle Scholar
  43. Benali N, Cordelier P, Calise D, Pages P, Rochaix P, Nagy A, Esteve JP, Pour PM, Schally AV, Vaysse N, Susini C, Buscail L: Inhibition of growth and metastatic progression of pancreatic carcinoma in hamster after somatostatin receptor subtype 2 (sst2) gene expression and administration of cytotoxic somatostatin analog AN-238. Proc Nat Acad Sci USA. 2000, 97: 9180-9185.PubMed CentralView ArticlePubMedGoogle Scholar
  44. Borgstrom P, Hassan M, Wassberg E, Refai E, Jonsson C, Larsson SA, Jacobsson H, Kogner P: The somatostatin analogue octreotide inhibits neuroblastoma growth in vivo. Pediatr Res. 1999, 46: 328-332.View ArticlePubMedGoogle Scholar
  45. Chatzistamou I, Schally AV, Varga JL, Groot K, Armatis P, Busto R, Halmos G: Antagonists of growth hormone-releasing hormone and somatostatin analog RC-160 inhibit the growth of the OV-1063 human epithelial ovarian cancer cell line xenografted into nude mice. J Clin Endocrinol Metab. 2001, 86: 2144-2152.PubMedGoogle Scholar
  46. Florio T, Morini M, Villa V, Arena S, Corsaro A, Thellung S, Culler MD, Pfeffer U, Noonan DM, Schettini G, Albini A: Somatostatin inhibits tumor angiogenesis and growth via somatostatin receptor-3-mediated regulation of endothelial nitric oxide synthase and mitogen-activated protein kinase activities. Endocrinology. 2003, 144: 1574-1584.View ArticlePubMedGoogle Scholar
  47. Georgii-Hemming P, Stromberg T, Janson ET, Stridsberg M, Wiklund HJ, Nilsson K: The somatostatin analog octreotide inhibits growth of interleukin-6 (IL-6)-dependent and IL-6-independent human multiple myeloma cell lines. Blood. 1999, 93: 1724-1731.PubMedGoogle Scholar
  48. Hoelting T, Duh QY, Clark OH, Herfarth C: Somatostatin analog octreotide inhibits the growth of differentiated thyroid cancer cells in vitro, but not in vivo. J Clin Endocrinol Metab. 1996, 81: 2638-2641.PubMedGoogle Scholar
  49. Ishihara S, Hassan S, Kinoshita Y, Moriyama N, Fukuda R, Maekawa T, Okada A, Chiba T: Growth inhibitory effects of somatostatin on human leukemia cell lines mediated by somatostatin receptor subtype 1. Peptides. 1999, 20: 313-318.View ArticlePubMedGoogle Scholar
  50. Jungwirth A, Schally AV, Halmos G, Groot K, Szepeshazi K, Pinski J, Armatis P: Inhibition of the growth of Caki-I human renal adenocarcinoma in vivo by luteinizing hormone-releasing hormone antagonist Cetrorelix, somatostatin analog RC-160, and bombesin antagonist RC-3940-II. Cancer. 1998, 82: 909-917.View ArticlePubMedGoogle Scholar
  51. Kahan Z, Nagy A, Schally AV, Hebert F, Sun B, Groot K, Halmos G: Inhibition of growth of MX-1, MCF-7-MIII and MDA-MB-231 human breast cancer xenografts after administration of a targeted cytotoxic analog of somatostatin, AN-238. Int J Cancer. 1999, 82: 592-598.View ArticlePubMedGoogle Scholar
  52. Kiaris H, Schally AV, Nagy A, Szepeshazi K, Hebert F, Halmos G: A targeted cytotoxic somatostatin (SST) analogue, AN-238, inhibits the growth of H-69 small-cell lung carcinoma (SCLC) and H-157 non-SCLC in nude mice. Eur J Cancer. 2001, 37: 620-628.View ArticlePubMedGoogle Scholar
  53. Kikutsuji T, Harada M, Tashiro S, Ii S, Moritani M, Yamaoka T, Itakura M: Expression of somatostatin receptor subtypes and growth inhibition in human exocrine pancreatic cancers. J Heptobiliary Pancreat Surgery. 2000, 7: 496-503.View ArticleGoogle Scholar
  54. Krenning EP, Valkema R, Kooij PP, Breeman WA, Bakker WH, de Herder WW, van Eijck CH, Kwekkeboom DJ, de Jong M, Jamar F, Pauwels S: The role of radioactive somatostatin and its analogues in the control of tumor growth. Recent Results Cancer Res. 2000, 153: 1-13.View ArticlePubMedGoogle Scholar
  55. Mishima M, Yano T, Jimbo H, Yano N, Morita Y, Yoshikawa H, Schally AV, Taketani Y: Inhibition of human endometrial cancer cell growth in vitro and in vivo by somatostatin analog RC-160. Am J Obstet Gynecol. 1999, 181: 583-590.View ArticlePubMedGoogle Scholar
  56. Pinski J, Schally AV, Halmos G, Szepeshazi K, Groot K: Somatostatin analog RC-160 inhibits the growth of human osteosarcomas in nude mice. Int J Cancer. 1996, 65: 870-874.View ArticlePubMedGoogle Scholar
  57. Zatelli MC, Tagliati F, Piccin D, Taylor JE, Culler MD, Bondanelli M, degli Uberti EC: Somatostatin receptor subtype 1-selective activation reduces cell growth and calcitonin secretion in a human medullary thyroid carcinoma cell line. Biochem Biophys Res Commun. 2002, 297: 828-834.View ArticlePubMedGoogle Scholar
  58. Lee MP, Feinberg AP: Genomic imprinting of a human apoptosis gene homologue, TSSC3. Cancer Res. 1998, 58: 1052-1056.PubMedGoogle Scholar
  59. Muller S, van den Boom D, Zirkel D, Koster H, Berthold F, Schwab M, Westphal M, Zumkeller W: Retention of imprinting of the human apoptosis-related gene TSSC3 in human brain tumors. Hum Mol Genet. 2000, 9: 757-763.View ArticlePubMedGoogle Scholar
  60. Gebre-Medhin S, Olofsson C, Mulder H: Islet amyloid polypeptide in the islets of Langerhans: friend or foe?. Diabetologia. 2000, 43: 687-695.View ArticlePubMedGoogle Scholar
  61. Itoh H, Takei K: Immunohistochemical and statistical studies on the islets of Langerhans pancreas in autopsied patients after gastrectomy. Hum Pathol. 2000, 31: 1368-1376.View ArticlePubMedGoogle Scholar
  62. Karlsson E, Sandler S: Islet amyloid polypeptide promotes beta-cell proliferation in neonatal rat pancreatic islets. Diabetologia. 2001, 44: 1015-1018.View ArticlePubMedGoogle Scholar
  63. Rumora L, Hadzija M, Barisic K, Maysinger D, Grubiic TZ: Amylin-induced cytotoxicity is associated with activation of caspase-3 and MAP kinases. Biol Chem. 2002, 383: 1751-1758.View ArticlePubMedGoogle Scholar
  64. Zhang S, Liu J, MacGibbon G, Dragunow M, Cooper GJ: Increased expression and activation of c-Jun contributes to human amylin-induced apoptosis in pancreatic islet beta-cells. J Mol Biol. 2002, 324: 271-285.View ArticlePubMedGoogle Scholar
  65. Miyagi N, Kato S, Terasaki M, Aoki T, Sugita Y, Yamaguchi M, Shigemori M, Morimatsu M: Fibroblast growth factor-9 (glia-activating factor) stimulates proliferation and production of glial fibrillary acidic protein in human gliomas either in the presence or in the absence of the endogenous growth factor expression. Oncol Rep. 1999, 6: 87-92.PubMedGoogle Scholar
  66. Weksler NB, Lunstrum GP, Reid ES, Horton WA: Differential effects of fibroblast growth factor (FGF) 9 and FGF2 on proliferation, differentiation and terminal differentiation of chondrocytic cells in vitro. Biochem J. 1999, 342: 677-682.PubMed CentralView ArticlePubMedGoogle Scholar
  67. Giri D, Ropiquet F, Ittmann M: FGF9 is an autocrine and paracrine prostatic growth factor expressed by prostatic stromal cells. J Cellr Physiol. 1999, 180: 53-60.View ArticleGoogle Scholar
  68. Tsai SJ, Wu MH, Chen HM, Chuang PC, Wing LY: Fibroblast growth factor-9 is an endometrial stromal growth factor. Endocrinology. 2002, 143: 2715-2721.View ArticlePubMedGoogle Scholar
  69. Matsumoto-Yoshitomi S, Habashita J, Nomura C, Kuroshima K, Kurokawa T: Autocrine transformation by fibroblast growth factor 9 (FGF-9) and its possible participation in human oncogenesis. Int J Cancer. 1997, 71: 442-450.View ArticlePubMedGoogle Scholar
  70. Busygina V, Suphapeetiporn K, Marek LR, Stowers RS, Xu T, Bale AE: Hypermutability in a Drosophila model for multiple endocrine neoplasia type 1. Hum Mol Genet. 2004, 13: 2399-2408.View ArticlePubMedGoogle Scholar
  71. Olack BJ, Swanson CJ, Howard TK, Mohanakumar T: Improved method for the isolation and purification of human islets of langerhans using Liberase enzyme blend. Human Immunol. 1999, 60: 1303-1309.View ArticleGoogle Scholar

Copyright

© Dilley et al; licensee BioMed Central Ltd. 2005

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Advertisement