
==== Front
Mol CancerMolecular Cancer1476-4598BioMed Central London 1476-4598-4-91569138110.1186/1476-4598-4-9ResearchGlobal gene expression in neuroendocrine tumors from patients with the MEN1 syndrome Dilley William G 1dilleyb@wustl.eduKalyanaraman Somasundaram 1omlimited@hotmail.comVerma Sulekha 2vermas@slu.eduCobb J Perren 1cobb@wustl.eduLaramie Jason M 1laramiej@bu.eduLairmore Terry C 12lairmoret@wustl.edu1 Department of Surgery, Washington University School of Medicine, St. Louis, MO, USA2 John Cochran Veterans Administration Medical Center, St. Louis, MO, USA2005 3 2 2005 4 9 9 11 11 2004 3 2 2005 Copyright © 2005 Dilley et al; licensee BioMed Central Ltd.2005Dilley et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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.
==== Body
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 [18-21] 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 [22-26]. 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 . 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 [32-35]. 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 [38-40]. Somatostatin has shown a wide range of growth inhibitory activity in vitro and in vivo [41-57].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.

Competing interests
The author(s) declare that they have no competing interests.

Authors' contributions
All authors contributed equally to this manuscript.

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.
==== Refs
Wermer P  Genetic aspects of adenomatosis of the endocrine glands. Am J Med 1954 16 363 371 13138607 10.1016/0002-9343(54)90353-8 
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 10548885 
Knudsen AGJ  Mutation and Cancer Proc Natl Acad Sci USA 1971 68 820 823 5279523 
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 9103196 10.1126/science.276.5311.404 
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 11295574 10.1016/S1043-2760(00)00372-6 
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 10090472 
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 9465067 10.1073/pnas.95.4.1630 
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 9989505 10.1016/S0092-8674(00)80967-8 
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 10500243 
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 12960363 10.1073/pnas.1834524100 
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 10654608 
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 9824159 10.1038/sj.onc.1202164 
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 11500056 10.1006/bbrc.2001.5405 
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 7902670 
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 11274402 10.1073/pnas.061358098 
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 11526476 10.1038/sj.onc.1204529 
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 11302744 10.1006/bbrc.2001.4723 
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 10220381 10.1073/pnas.96.9.4844 
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 10874048 10.1074/jbc.M909923199 
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 10652273 
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 11526502 10.1038/sj.onc.1204678 
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 10359783 10.1073/pnas.96.12.6745 
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 10521349 10.1126/science.286.5439.531 
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 11568890 10.1002/path.889 
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 11606367 
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 11742071 10.1073/pnas.211566398 
Wu TD  Analysing gene expression data from DNA microarrays to identify candidate genes J Pathol 2001 195 53 65 11568891 10.1002/1096-9896(200109)195:1<53::AID-PATH891>3.0.CO;2-H 
Harkin DP  Uncovering functionally relevant signaling pathways using microarray-based expression profiling Oncologist 2000 5 501 507 11110602 10.1634/theoncologist.5-6-501 
Li C Hung Wong W  Model-based analysis of oligonucleotide arrays: model validation, design issues and standard error application Genome Biol 2001 2 RESEARCH0032 11532216 
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 11134512 10.1073/pnas.011404098 
Komminoth P Heitz PU Kloppel G  Pathology of MEN-1: morphology, clinicopathologic correlations and tumour development J Intern Med 1998 243 455 464 9681843 10.1046/j.1365-2796.1998.00274.x 
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 11687580 10.1074/jbc.M108658200 
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 12193586 10.1210/en.2002-220564 
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 12086925 
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 14676124 
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 14688275 10.1074/jbc.M308073200 
Wu MX  Roles of the stress-induced gene IEX-1 in regulation of cell death and oncogenesis Apoptosis 2003 8 11 18 12510147 10.1023/A:1021688600370 
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 11244505 10.1038/sj.onc.1204061 
Schilling D Pittelkow MR Kumar R  IEX-1, an immediate early gene, increases the rate of apoptosis in keratinocytes Oncogene 2001 20 7992 7997 11753682 10.1038/sj.onc.1204965 
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 12682234 
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 10094925 
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 12472080 
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 10900262 10.1073/pnas.130196697 
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 10473050 
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 11344219 10.1210/jc.86.5.2144 
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 12639942 10.1210/en.2002-220949 
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 10029602 
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 8675590 10.1210/jc.81.7.2638 
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 10447088 10.1016/S0196-9781(99)00037-6 
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 9486581 10.1002/(SICI)1097-0142(19980301)82:5<909::AID-CNCR16>3.0.CO;2-4 
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 10404076 10.1002/(SICI)1097-0215(19990812)82:4<592::AID-IJC20>3.0.CO;2-0 
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 11290438 10.1016/S0959-8049(00)00437-8 
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 11180877 10.1007/s005340070021 
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 10626285 
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 10486467 
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 8631606 10.1002/(SICI)1097-0215(19960315)65:6<870::AID-IJC27>3.0.CO;2-6 
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 12359227 10.1016/S0006-291X(02)02307-0 
Lee MP Feinberg AP  Genomic imprinting of a human apoptosis gene homologue, TSSC3 Cancer Res 1998 58 1052 1056 9500470 
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 10749982 10.1093/hmg/9.5.757 
Gebre-Medhin S Olofsson C Mulder H  Islet amyloid polypeptide in the islets of Langerhans: friend or foe? Diabetologia 2000 43 687 695 10907112 10.1007/s001250051364 
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 11112211 
Karlsson E Sandler S  Islet amyloid polypeptide promotes beta-cell proliferation in neonatal rat pancreatic islets Diabetologia 2001 44 1015 1018 11484079 10.1007/s001250100601 
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 12530540 10.1515/BC.2002.196 
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 12441106 10.1016/S0022-2836(02)01044-6 
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 9864407 
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 10477279 10.1042/0264-6021:3420677 
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 10362017 10.1002/(SICI)1097-4652(199907)180:1<53::AID-JCP6>3.0.CO;2-P 
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 12072406 10.1210/en.143.7.2715 
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 9139882 10.1002/(SICI)1097-0215(19970502)71:3<442::AID-IJC23>3.0.CO;2-G 
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 15333582 10.1093/hmg/ddh271 
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 10626746 10.1016/S0198-8859(99)00118-4

