Identification of genes differentially expressed between gastric cancers and normal gastric mucosa with cDNA microarrays

Cancer Letters 184 (2002) 197–206 www.elsevier.com/locate/canlet

Identification of genes differentially expressed between gastric cancers and normal gastric mucosa with cDNA microarrays Seongeun Lee a,1, Myungin Baek a,1, Hankwang Yang b, Yung-Jue Bang b, Woo Ho Kim b, Ji-Hong Ha c, Dae-Kee Kim a, Doo-Il Jeoung a,* a

In2Gen Company, Seoul National University Cancer Research Center, 6F, 28, Yongon-Dong, Chongno-Gu, Seoul 110-799, South Korea b College of Medicine, Seoul National University, Yongon-Dong, Chongno-Gu, Seoul 110-799, South Korea c Department of Genetic Engineering, Kyungpook National University, Daegu 702-701, South Korea Received 11 February 2002; received in revised form 3 April 2002; accepted 5 April 2002

Abstract To identify genes whose alterations lead to gastric cancer, gene expression profiles have been obtained from 22 gastric cancer tissues and their surrounding gastric mucosa tissues. A total of 16 genes were differentially expressed in more than 50% of gastric cancer tissues compared with surrounding gastric mucosa tissues. Genes such as HMG-Y, fibroblast collagenase inhibitor, and osteopontin are among those that are overexpressed in over 50% of the gastric cancer tissues. Dihydrodiol dehydrogenase, ribonuclease A, and glutathione peroxidase are among those genes that are underexpressed in over 50% of the gastric cancer tissues. We identified genes that are associated with clinical phenotypes of patients with gastric cancers. Alpha-II spectrin, Na/KATPase and KIAA0111 are those that are enhanced in intestinal type of gastric cancer. Gene such as platelet-endothelial tetraspan antigen 3 was enhanced in highly metastatic gastric cancer tissues. q 2002 Elsevier Science Ireland Ltd. All rights reserved. Keywords: Gastric cancer; cDNA microarray; Metastasis; Lauren

1. Introduction Gastric cancer is one of the most common cancers worldwide. Gastric cancer, in general, is resistant to chemo- and radiation therapy. Surgery remains the only curative treatment. Analysis of genes whose alteration leads to gastric cancer is essential for understanding and development of therapeutics for gastric cancer. So far, only a few genes have been associated with gastric carcinogenesis. These include c-met, cerbB2, K-sam, and E-cadherin [1–3]. Microarray tech* Corresponding author. Tel.: 182-2-3668-7472; fax: 182-23672-8394. E-mail address: [email protected] (D.-I. Jeoung). 1 The first two authors contributed equally to this paper.

nologies have emerged as key tools for genomic expression analysis for the purpose of studying disease states, identifying drug targets, and profiling time-, tissue- or stage-dependent changes [4,5]. Identification and characterization of genes that are differentially expressed in gastric cancers compared with their corresponding mucosa tissues is a prerequisite not only for diagnosis but also for development of anticancer drugs. With the advent of current cDNA microarray, we can examine expression profiles of various cancer tissues simultaneously on a genomic scale. In this study, we exploited cDNA microarray analysis to identify genes that are differentially expressed in gastric cancer tissues compared with surrounding

0304-3835/02/$ - see front matter q 2002 Elsevier Science Ireland Ltd. All rights reserved. PII: S 0304-383 5(02)00197-0

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gastric mucosa tissues. We also identified genes that are associated with clinical phenotypes of patients. 2. Experimental 2.1. Tissues Gastric cancer tissues were obtained with informed consent from patients who underwent surgical operation at Seoul National University Hospital (Seoul, South Korea). Cancerous and their surrounding gastric mucosa tissues were obtained from same patients, and were immediately frozen in liquid nitrogen. Histological grading of gastric cancer tissues was decided according to the World Health Organization (WHO) classification. Tumor content of each gastric cancer tissue ranges between 60 and 70% under the microscopic observation. Gastric mucosa tissues used in this study were resected further from the site of tumor (at least 10 cm). The average age of patients is 61 years. Most of the gastric cancer tissues are of male origin (17/22). 2.2. RNA isolation Isolation of total RNA from each tissue was carried out by using Trizol agent according to the instruction manual provided by Life Technologies, Inc. (Carlsbad, CA). One hundred milligrams of gastric mucosa tissue or gastric cancer tissue was used for isolation of total RNA. Messenger RNA was isolated by using Oligotex bead according to the instruction manual provided by Qiagen Company (Valencia). 2.3. cDNA microarray analysis There are three steps (labeling, hybridization, and washing) in cDNA microarray. We carried out these three steps according to the manufacturer’s protocol (NEN Company, Boston, MA). cDNA derived from mRNA of cancer tissue was labeled with Cy-5 (red) and cDNA derived from mRNA of surrounding gastric mucosa tissue was labeled with Cy-3 (green). 2.4. Microarray scanning and data analysis For complete gene description and grid orientation on NEN MICROMAX cDNA microarrays, refer to the Web page http://lifesciences.perkinelmer.com. Fluor-

escent images of hybridized microarrays were obtained using a gene PIX 4000 microarray scanner (Axon Instruments, Foster City, CA). Images were analyzed with Gene PIX program. Single spots or areas with blemish were excluded from the analysis. Red spots in microarray mean that these genes are overexpressed in gastric cancer tissues and green spots mean that these genes are underexpressed in gastric cancer tissues. Normalization of cDNA concentrations of gastric mucosa and cancer tissues was carried out by cDNA microarray hybridization of slides containing plant genes with unknown functions. The fluorescent intensity of each spot was corrected by subtracting background signals. The fluorescent intensities from spots of plant were used as background signals. Expression ratio between cancer and gastric mucosa tissue was determined by using GeneSpring software (Silicon Genetics, Redwood City, CA). We performed hierarchical analysis using relative gene expression ratio (Cy5/Cy3) to examine the relatedness among expression profiles of 2400 genes and those in gastric cancer tissues. Clustering of gene expression profiles was done by ‘Cluster’ software (Version 2.11) and was visualized by ‘Tree view’ software (Version 1.50) written by Eisen et al. Scatter plot analysis was used to check the correlation of the intensity value for each cDNA between cancer and surrounding gastric mucosa tissue. All statistical tests were performed with GeneSpring software version 4.0.7. Spots with background level intensities were discarded. P , 0:05 was used to determine statistical significance. 2.5. Reverse transcription-polymerase chain reaction analysis Reverse transcription-polymerase chain reaction (RT-PCR) analysis was carried out according to the standard procedures. All primers were commercially synthesized (Bioneer Co. Chungwon, South Korea). For first strand cDNA synthesis, total RNA (2 mg) was converted into cDNA by superscript reverse transcriptase (Life Technologies, Inc.). The reaction was carried out according to the instruction manual provided by the manufacturer (Life Technologies, Inc.). Obtained cDNA was subjected to PCR using various primers listed in Table 3. PCR condition for each gene was empirically determined. Amplification

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Table 1 Characteristics of the patients and their cancer tissues a Case

Size (cm)

Borrman

Lauren

Differentiation

Depth

Stage

Sex

Age

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

9 6 6.5 8 8.5 6 7.5 9 10 5 8 7 17 13 6 6 6 9 6 7 13 7

4 3 ND b 3 3 3 2 2 4 3 3 3 3 3 3 3 3 3 2 2 4 2

Diffuse Diffuse Diffuse Diffuse Diffuse Diffuse Intestinal Diffuse Diffuse Diffuse Diffuse Diffuse Diffuse Intestinal Diffuse Intestinal Diffuse Diffuse Intestinal Diffuse Diffuse Intestinal

Signet Signet Poor Poor Poor Poor Moderate Poor Mucinous Signet Poor Poor Poor Moderate Poor Moderate Mucinous Poor Moderate Undifferentiate Poor Moderate

S Ss Sm Ss Ss Ss Ss Ss Ss Ss Ss S Ss Ss 5 Ss 5 Ss Ss Ss Ss ss

4 6 1 3 3 3 3 2 6 6 4 6 3 4 4 3 5 3 2 2 6 4

F M F F M F M M M M M F M M M M M M M M M M

41 37 71 27 70 79 77 66 72 56 53 48 67 62 56 65 54 97 72 70 45 50

a Histological grading of each gastric cancer tissue was decided according to the classification of WHO. S denotes serosa, Ss denotes subserosa, and Sm denotes submucosa. b Not determined.

of GAPDH was performed with primers (forward) 5 0 ACCACAGTCCATGCCATCAC-3 0 and primer (reverse) 5 0 -TCCACCACCCTGTTGCTGTA-3 0 . PCR consisted of 27 cycles of 30 s at 948C, 30 s at 608C, and 1 min at 728C. PCR was performed in 20 ml reaction mixture containing 10 £ PCR buffer (1.5 mM MgCl2), Taq polymerase (1 unit), template (20 ng of cDNA), 5 pmol of each primer, dNTP (2.5 mM each), double distilled water up to 20 ml.

3. Results 3.1. Identification of genes that are differentially expressed in gastric cancer tissues compared with surrounding gastric mucosa tissues We wanted to identify genes that are associated with gastric cancer. For this, we performed cDNA microarray analysis of 2400 genes to determine genes that are differentially expressed in gastric

cancer tissues compared with surrounding gastric mucosa tissues. Table 1 describes the list of gastric cancer tissues. Gastric cancer tissues were obtained from 22 patients who underwent surgical resection. Significant numbers of these tissues are diffuse and poorly differentiated types. We carried out cDNA microarray analysis to identify genes that are enhanced or reduced in cancer tissues compared with surrounding gastric mucosa tissues. The level of RNA expression in cancer tissue (cy5) was compared with gastric mucosa tissue (cy3) of the same patient, and their ratio (cy5/cy3) was determined. These two values of each patient were scatter plotted, and their linear relationship was evaluated statistically. Close correlation with a high correlation coefficient was obtained in almost all of the samples (Fig. 1). From the results of cDNA microarray analysis, we found that nine genes showed at least two-fold overexpression in at least 50% of the gastric caner tissues compared with surrounding gastric mucosa tissues (Table 2). We carried out RT-PCR analysis

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Fig. 1. Scatter plot analysis. Log10(intensity of cy5) and log10(intensity of cy3) of each patient were scatter plotted. F1 denotes log10(intensity of cy3) and F2 denotes log10 (intensity of cy5). R denotes correlation coefficient.

to verify the data (Fig. 2). As shown in Table 2, there was a close correlation between RT-PCR and cDNA microarray data. The list of primers that are used in RT-PCR is given in Table 3. We found that several kinds of collagen genes were overexpressed. This suggests that many of these gastric cancer tissues contain stromal cells. CAS gene is known to have dual functions in apoptosis and cellular proliferation [6]. HMG-Y reportedly

is associated with tumor progression [7]. Fibroblast collagenase inhibitor reportedly is associated with tumor invasion [8,9]. Osteopontin is a secreted phosphoprotein and plays a role in inflammation, tumor invasion, and immune reaction [10]. It interacts with integrins and CD44 as major receptors. We found that osteopontin was overexpressed in most of the gastric cancer tissues. It was also reported that osteopontin was a substrate for MMP-3 and MMP-7 [11]. Many of

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Table 2 Genes differentially expressed between cancerous and surrounding gastric mucosa tissues (in the case of osteopontin, a number of bad spots were discarded) Gene

Array a

. Two-fold 7.6 8.0 8.5

RT-PCR

Function

16/22 17/22 14/22

Cytoskeletal protein Cytoskeletal protein Putative apoptosisassociated gene Apoptosis and cell proliferation (dual function) Cytoskeletal protein Tumor progression Tumor invasion Ion transport

b

Overexpressed Prepro alpha2 (1) collagen Prepro alpha1 (1) collagen CPE-R

No. (Cy5/Cy3) 15/22 14/22 12/22

CAS

14/22

4.0

16/22

Pro alpha1 (3) collagen HMG-Y Fibroblast collagenase inhibitor Na/K transporting ATPase beta3 subunit Osteopontin

14/22 14/22 16/22 12/22

5.3 4.4 4.1 3.8

15/22 15/22 18/22 15/22

10/10

55

21/22

Cell-mediated immunity, tumor progression

Underexpressed Dihydrodiol dehydrogenase Ribonuclease A Putative serine/threonine protein kinase Ezrin

No. (Cy5/Cy3) 22/22 21/22 14/22

, 0.5-fold 0.09 0.15 0.28

20/22 19/22 17/22

14/22

0.31

18/22

MT-11 Rearranged mRNA for glutamine synthase

14/22 14/22

0.24 0.23

14/22 14/22

Glutathione peroxidase

15/22

0.42

16/22

Drug resistance Antitumor activity Rho-dependent signaling pathway Cell motility, adhesion, and invasion Drug detoxification Cell growth, development, and tumorigenesis Drug detoxification

a b

Cy3, mucosa tissue; Cy5, cancer tissue. Average fold.

the genes overexpressed in gastric cancer tissues are involved in cellular proliferation and tumor invasion. We found that seven genes were underexpressed in at least 50% of the gastric cancer tissues compared with their surrounding gastric mucosa tissues. Genes that are underexpressed in gastric cancer tissues include MT-11 and glutathione peroxidase. These genes are involved detoxification processes [12,13]. This suggests that gastric cancer undergoes dedifferentiation process. Ezrin is known to play a role in cell motility, invasion, and adherence [14]. Recently, it was reported that RNase A has significant antitumor effect [15]. Dihydrodiol dehydrogenase (DHD) is underexpressed in almost every gastric cancer tissues we examined. It is reported that DHD is associated

with drug-resistance and is used as a prognostic marker of non-small cell lung cancer [16]. DHD is also reported to be involved in drug detoxification [17]. The lack of DHD in gastric cancer tissues suggests that gastric cancer tissue is more susceptible to carcinogenesis. Taken together, gastric cancer is associated with enhanced or decreased expression of genes involved in cellular proliferation, tumor progression, drug detoxification, immunity, and cytoskeletal structure. The functional characterization of these genes would be necessary to understand the process of gastric carcinogenesis. We thought that phenotypic diversity of gastric cancers is associated with different gene expression patterns of gastric cancer tissues. Therefore, we

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Fig. 2. RT-PCR of differentially expressed genes. T denotes tumor tissues, and M denotes mucosa tissues. Full names of gene abbreviations are as follows: DHD, dihydrodiol dehydrogenase; FCI, fibroblast collagenase inhibitor; GPX, glutathione peroxidase; OPN, osteopontin.

checked whether molecular profiling analysis could be used to classify various gastric cancer tissues. We understand that using single reference sample would make it easier to discover differences among the cancerous samples. However, using gastric mucosa tissue of each patient would eliminate influence of individual genetic variations among cancer patients.

We carried out hierarchical clustering analysis of 22 gastric cancer tissues using 2400 genes. This hierarchical clustering analysis was based on gene expression ratio between cancerous and surrounding gastric mucosa tissues (cy5/cy3 ratio). By hierarchical clustering analysis, we found that gastric cancers are classified into five groups. However, we did not find

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Table 3 The list of primers used in RT-PCR analysis Gene

Sense (5 0 ! 3 0 )

Antisense (5 0 ! 3 0 )

Tm (8C)

Prepro alpha2 (1) collagen Prepro alpha1 (1) collagen CAS (chromosome segregation protein) HCPE-R Pro alpha1 (3) collagen HMG-Y (high mobility group Y) atgagtgagtcgagctcgaag catgggagccctgcagcggc FCI (fibroblast collagenase inhibitor) Na/K-ATPase beta subunit Osteopontin DHD (dihydrodiol dehydrogenase) RNase A STFK (putative ser/thr protein kinase) gcagaaggacaggacaaagc ggcactctaacgctcgtttc Ezrin

gtggatacgcggactttgtt ggtgctaaaggtgccaatggt tgcagctgacaaaattcctg

aggttcacccttcacaccag ctccagcctctccatctttg ggacaggcggtagacaacat

54 52 60

30 34 31

591 669 654

catctcctctgttccgggta cctccaactgctcctactcg

accctcccaggctcattagt tcgaagcctctgtgtccttt

60 52

30 34

327 536

MT-11 (metallotheionein 11) Glutamine synthase Glutathione peroxidase GAPDH

58

Cycle

Product (bp)

483

ctgttgttgctgtggctgat

30 tgcagttttccagcaatgag

58

30

305

cagtctgtcctgatggagca ttgcagtgatttgcttttgc atgatcctcaacaagccagg

ggagcaaagctgacctgaac aaccacactatcacctcggc cagtcaccagcatagagcca

60 58 60

31 27 31

340 485 531

tcctgatactgctggtgctg

ggtatctcgctgctctgacc

55

30

451

58 gcgagaaggaggagttgatg agtgatgcgcttctcctcat cttctccttgcctcgaaatg ttgtttggctgggatagagg catcccatgtccaccatgta accacagtccatgccatcac

correlation between molecular profiling and clinical phenotypes such as age, sex, Lauren classification, Borrman classification, and differentiation (data not shown). 3.2. Identification of genes those are associated with clinical phenotypes Because of the lack of association between molecular profiling and clinical phenotypes, we wanted to identify genes that are associated with gastric cancers of specific clinical phenotypes. First, we selected genes that have fluorescent intensity of .500. We used relative expression ratio (cy5/cy3) to identify genes associated with specific clinical phenotypes. We identified several genes that are relatively enhanced in highly metastatic gastric cancer tissues (n ¼ 5) compared with low metastatic gastric cancer tissues (n ¼ 17). For example, platelet-endothelial tetraspan antigen 3 associates with PKC and links PKC to specific integrins [18]. Complement Clr is a protease that is known to

553 30 54

32

533

aggagcagcagctcttcttg cagagcagggcatttagtcc gggagtgtggtagacccaga tccaccaccctgttgctgta

57 55 57 60

30 30 30 27

123 402 503 451

cleave IGFBP5 [19]. Decreased level of IGFBP5 is often associated with apoptosis. These three genes showed cy5/cy3 ratio of 2 or higher in all five highly metastatic gastric cancer tissues compared with surrounding gastric mucosa tissues. We also identified genes that are relatively enhanced in low metastatic gastric cancer tissues. HMG-Y is relatively enhanced in low metastatic gastric cancer tissues. It reportedly promotes tumor progression and mesenchymal transition of human epithelial cells [7]. According to Lauren classification, gastric cancers are divided into intestinal and diffuse type. We identified several genes that are relatively enhanced in intestinal type of gastric cancers (n ¼ 5) compared with diffuse type of gastric cancer (n ¼ 17) (Table 2). These include alpha II-spectrin and KIAA0111. Alpha II-spectrin (alpha fodrin) reportedly is cleaved by caspase-3 in response to apoptotic signaling [20]. This suggests that alpha II-spectrin is involved in apoptosis. It also induces vinculin-specific CTL [21]. Na/K-ATPase beta subunit is also associated with intestinal type of gastric cancer. We

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Table 4 Genes associated with gastric cancers of specific clinical phenotypes a P value b 0.014 0.03 0.032

A 6.5 (4/5) 3.2 (4/5) 2.5 (4/5)

B 1.3 (3/17) 0.6 (1/17) 1.3 (1/17)

Low

Gene Complement component C1r Alpha-2 macroglobulin Platelet-endothelial tetraspan antigen 3 HMG-Y protein isoform

0.045

1.7 (1/5)

4.9 (12/17)

Categories Lauren

Type Intestinal Intestinal Intestinal

Gene Alpha-II spectrin Na/K-ATPase beta subunit KIAA0111

P value 0.02 0.021 0.036

C 2.6 (415) 8.9 (5/5) 3.1 (4/5)

D 1.4 (3/17) 2.3 (7/17) 1.1 (1/17)

Categories Differentiation

Type Moderate Moderate Moderate

Gene Alpha-II spectrin Na/K-ATPase beta subunit KIAA0111

P value 0.019 0.023 0.036

E 2.6 (4/5) 8.9 (5/5) 3.1 (4/5)

F 1.2 (1/11) 23 (3/11) 1.1 (0/11)

Categories Metastasis

Type High High High

a Highly metastatic gastric cancer tissues are those that are at stage 6 (Table 1). Expression ratio between cancerous and surrounding gastric mucosa tissues (cy5/cy3) was used to identify genes that are associated with gastric cancers of specific clinical phenotypes. Student’s t-test was employed to confirm statistical significance of genes associated with specific clinical phenotype. A denotes average expression change (cy5/cy3 ratio) of each gene in highly metastatic tumor tissues compared with their corresponding mucosa tissues. B denotes average expression change (cy5/cy3 ratio) of each gene in lowly metastatic tumor tissues compared with their corresponding mucosa tissues. C denotes average expression change (cy5/cy3) of each gene in intestinal type of gastric tumor tissues compared with their corresponding mucosa tissues. D denotes average expression change (cy5/cy3) of each gene in diffuse type of gastric tumor tissues compared with their corresponding mucosa tissues. E denotes average expression change (cy5/cy3) of each gene in moderately differentiated type of gastric tumor tissues compared with their corresponding mucosa tissues. F denotes average expression change (cy5/cy3) of each gene in poorly differentiated type of gastric tumor tissues compared with their corresponding mucosa tissues. The numbers in parentheses denote number of tissues in which expression change (cy5/cy3) is greater than two-fold. b Student t-test (single sided).

identified several genes that are relatively enhanced in moderately (n ¼ 5) or poorly differentiated gastric cancers (n ¼ 11). Alpha-II spectrin and KIAA0111 are associated with moderately differentiated gastric cancers. All those gastric cancers of intestinal type (Lauren grade) belong to moderately differentiated type. Therefore, alpha-II spectrin, KIAA0111, and Na/K-ATPase beta subunit would be valuable markers for classifying gastric cancers. We did not find any genes that are associated with Borrman phenotypes (data not shown). Taken together, we identified genes that are relatively enhanced in gastric cancers with specific clinical phenotypes. Identification of these genes would be valuable for understanding of mechanism of gastric tumorigenesis. These genes would be also valuable for classification of gastric cancers.

4. Discussion The incidence of gastric cancer has recently

declined. However, it is still one of the most common causes of cancer-related deaths in Asian countries. The molecular mechanism of gastric carcinogenesis is not well understood because of lack of sufficient information on genetic alterations leading to gastric carcinogenesis. Through cDNA microarray analysis, we identified genes that are differentially expressed in gastric cancer tissues compared with their surrounding gastric mucosa tissues. Overexpressed genes in gastric cancer tissues include HMG-Y, osteopontin, and fibroblast collagenase inhibitor. These genes are known to be involved in cellular proliferation and metastasis. Genes underexpressed in gastric cancer tissues include DHD, ribonuclease A, glutathione peroxidase. These genes are involved in diverse cellular processes such as drug detoxification and tumor suppression. We used gene expression ratio (cy5/ cy3) between cancerous and surrounding gastric mucosa tissue to identify genes that are differentially expressed in gastric cancers. Using single normal

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gastric tissue reference makes it easier to find differences among cancer samples. However, gastric cancer is complex in nature. Therefore, we used gastric mucosa tissue as a reference in each microarray. With this, individual genetic variation can be eliminated. Using expression ratio for the identification of genes differentially expressed in cancers has been tried before [22]. Genes that are differentially expressed in hepatocellular carcinoma were identified by expression ratio between cancerous and cirrhotic liver. By hierarchical clustering analysis using 2400 genes, we were unable to classify gastric cancers based on morphology (data not shown). This reflects genetic heterogeneity of gastric cancers. It is possible that many genes used for clustering analysis are not related to any clinical phenotypes. Because of the lack of significant correlation between clinical phenotypes and expression patterns, we checked whether there are genes that are differentially expressed in gastric cancers with different clinical phenotypes. Genes have been found that are associated with highly metastatic cancer tissues. These include complement component C1r and platelet-derived tetraspan antigen 3 (Table 4). Comparison of expression levels of these genes with the corresponding genes in lowly metastatic tissues showed significantly different pattern of expression profile (data not shown). Gene such as HMG-Y was relatively enhanced in low metastatic gastric cancer tissues (Table 4). We also identified that genes such as alpha-II spectrin, Na/K-ATPase beta subunit and KIAA0111 are relatively enhanced in intestinal type of gastric cancers (Table 4). Throughout this study, we identified genes that are differentially expressed in gastric cancer tissues compared with surrounding gastric mucosa tissues, and genes that are associated with specific clinical phenotypes. Identification of these genes in this study would be valuable for diagnosis and development of anticancer drugs. We used cDNA microarrays containing 2400 human cDNAs. The number is relatively small for extensive identification of genes with potential involvement with gastric tumorigenesis. Therefore, it is reasonable that we can identify only small number of genes differentially expressed. We wanted to identify genes that are differentially expressed in more than 50% (11/22) of the gastric cancer tissues. That is why we were able to identify only small number of genes differentially

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expressed. Some oncogenes, c-met, c-erbB2 are either absent in cDNA microarray or overexpressed in small percentage of gastric tumor tissues. It is well known fact that many of those oncogenes or potential oncogenes are overexpressed in about 20–30% of gastric tumor tissues at most. In this paper, we included only those genes that are overexpressed in more than 50% of the gastric tumor tissues. Functional role of these genes in gastric tumorigenesis remains to be known. Because of complex and diverse nature of gastric cancers, a lot more cDNA microarray analysis of gastric cancers would be required for having complete picture of gastric cancer. Also, further identification of genes differentially expressed in gastric cancers compared with surrounding gastric mucosa tissues would be required for complete understanding of mechanism of gastric tumorigenesis. References [1] P. Guilford, J. Hopkins, J. Harraway, M. McLeod, N. McLeod, P. Harawira, H. Taite, R. Scoular, A. Miller, A.E. Reeve, Ecadherin germline mutations in familial gastric cancer, Nature 393 (1998) 402–405. [2] C.S. Fuchs, R.J. Mayer, Gastric carcinoma, N. Eng. J. Med. 333 (1995) 32–41. [3] E. Tahara, S. Semba, H. Tahara, Molecular biological observations in gastric cancer, Semin. Oncol. 23 (1996) 307–315. [4] C.M. Perou, Molecular portraits of human breast tumors, Nature 406 (2000) 747–752. [5] A.A. Allzadeh, Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling, Nature 403 (2000) 503–511. [6] A. Wellmann, P. Flemming, P. Behrens, K. Wuppermann, H. Lang, K. Oldhafer, I. Pastan, U. Brinkmann, High expression of the proliferation and apoptosis associated CSE1L/CAS gene in hepatitis and liver neoplasms: correlation with tumor progression, Int. J. Mol. Med. 7 (2001) 489–494. [7] R. Reeves, D.D. Edberg, Y. Li, Architectural transcription factor HMGI (Y) promotes tumor progression and mesenchymal transition of human epithelial cells, Mol. Cell. Biol. 21 (2001) 575–594. [8] T. Yoshikawa, A. Tsuburaya, O. Kobayashi, M. Sairenji, H. Motohashi, S. Yanoma, Y. Noguchi, Intratumoral concentrations of tissue inhibitor of matrix metalloproteinase 1 in patients with gastric carcinoma a new biomarker for invasion and its impact on survival, Cancer 91 (2001) 1739–1744. [9] S.C. Pritchard, M.C. Nicolson, C. Lloret, J.A. McKay, V.G. Ross, K.M. Kerr, G.I. Murray, H.L. McLeod, Expression of matrix metalloproteinases 1,2,9 and their tissue inhibitors in stage II non-small cell lung cancer: implications for MMP inhibition therapy, Oncol. Rep. 8 (2001) 421–424. [10] S. Ashkar, G.F. Weber, V. Panoutsakopoulou, M.F. Sanchir-

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