Skip to main content

Advertisement

  • Research
  • Open Access
  • Global gene expression in neuroendocrine tumors from patients with the MEN1 syndrome

    • 1Email author,
    • 1,
    • 2,
    • 1,
    • 1 and
    • 1, 2
    Molecular Cancer20054:9

    https://doi.org/10.1186/1476-4598-4-9

    • Received: 11 November 2004
    • Accepted: 03 February 2005
    • Published:

    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.

    Keywords

    • Neuroendocrine Tumor
    • Multiple Endocrine Neoplasia Type
    • Global Gene Expression
    • Normal Islet
    • Islet Tumor

    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
    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
    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
    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
    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
    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
    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, St. Louis, MO, USA
    (2)
    John Cochran Veterans Administration Medical Center, St. Louis, MO, USA

    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