Open Access

Different cDNA microarray patterns of gene expression reflecting changes during metastatic progression in adenoid cystic carcinoma

  • Dan Huang1, 2Email author,
  • Wantao Chen1,
  • Ronggen He1,
  • Fan Yu1,
  • Zhiyuan Zhang1 and
  • Weiliu Qiu1
World Journal of Surgical Oncology20031:28

DOI: 10.1186/1477-7819-1-28

Received: 30 August 2003

Accepted: 19 December 2003

Published: 19 December 2003

Abstract

Background

The metastatic ability of tumor cells is determined by level of expression of specific genes that may be identified with the aid of cDNA microarray containing thousands of genes and can be used to establish the expression profile of disease related genes in complex biological system.

Materials and Methods

Salivary adenoid cystic carcinoma cell line and its high metastases adenoid cystic carcinoma clone were used as model systems to reveal the gene expression alteration related to metastasis mechanism by cDNA microarray analysis. The correlation of metastatic phenotypic changes and expression levels of 4 selected genes (encoding CD98, L6, RPL29, and TSH) were further validated by using RT-PCR analysis of human tumor specimens from primary adenoid cystic carcinoma and corresponding metastasis lymph nodes.

Results

Of the 7,675 clones of known genes and expressed sequence tags (ESTs) that were analyzed, 30 showed significantly different (minimum 3 fold) expression levels in two cell lines. Out of 30 genes found differentially expressed, 18 were up regulated (with ratio more than 3) and 12 down regulated (with ratio less than 1/3).

Conclusion

Some of these genes are known to be involved in human tumor antigen, immune surveillance, adhesion, cell signaling pathway and growth control. It is suggested that the microarray in combination with a relevant analysis facilitates rapid and simultaneous identification of multiple genes of interests and in this study it provided a profound clue to screen candidate targets for early diagnosis and intervention.

Keywords

cDNA microarray metastasis cell line gene expression salivary gland neoplasm tumor carcinoma adenoid cystic

Background

Adenoid cystic carcinoma (ACC) of the salivary gland is characterized by a prolonged clinical course, high rate of local recurrence and the delayed onset of distant hematogenous metastases. Late distant metastases and local recurrences are responsible for a rather low long-term survival rate and poor treatment results [1]. However, the molecular mechanism behind the metastasis development is poorly understood, largely because the tumor metastasis is a complex process involving several distinct steps such as escape from primary tumor, dissemination through the circulation, lodgment in small vessels at distinct sites, penetration of the vessel wall and growth in the new site as a secondary tumor [2]. A possible breakthrough in understanding of tumor metastasis has emerged with the combination of a putative hypothesis and the development of high throughput cDNA microarray technology. It is hypothesized that the long-term development of metastasis induces genetic alteration and subpopulation of cancerous cells that characterize the metastatic cells with specific gene expression involved in temporal and spatial procession of metastasis [3]. On the other hand, examination of large-scale expression alteration of specific genes involved in tumor metastasis is made feasible by the recently developed cDNA microarray technology that allows the simultaneous analysis of the expression levels of thousands of genes [4].

Consistent with the above view, in this study, we used cDNA microarray technique to examine the differences in the gene expression profiles between a salivary adenoid cystic carcinoma cell line ACC-2 and a highly metastatic salivary adenoid cystic carcinoma clone ACC-M, which was screened from ACC-2 by combination of in vivo selection and cloning in vitro [5]. Since ACC-2 and ACC-M share identical genetic background except for different metastatic behavior, it is presumed that the differentially expressed genes ACC-2 and ACC-M as metastasis related genes, which play direct or indirect roles in the progression of metastasis. The results of the cDNA microarray analysis were further conformed by RT-PCR to determine the expression level of specific mRNA in the primary tumors and corresponding metastasis lymph nodes. Such a survey not only furthers an insight into the underlying mechanism of the adenoid cystic carcinoma metastasis, but also helps identify candidate marker for diagnosis and prognosis of ACC and molecular targets for metastasis intervention.

Materials and Methods

Cell lines and specimens

Cell lines ACC-2 and clone ACC-M were previously established in the tumor biology laboratory of Shanghai Ninth Hospital in Shanghai Second Medical University. Cell line ACC-2 was derived from surgically excised primary tumor tissue from a histologically diagnosed patient with adenoid cystic carcinoma of the palate. The microscopic features of the tumor showed a dominantly solid pattern of ACC. The patient did not receive any therapy before surgery. High pulmonary metastases clone ACC-M was selected from cell line ACC-2 after five repeated selection in vivo combined with an in vitro cloning technique and analysis of platelet aggregation activity [6].

Cells were cultured at 37°C in 5% CO2 humidified incubator in RPMI 1640 medium supplemented with 10% heat-inactivated fetal bovine serum (FBS), 1% penicillin and streptomycin.

Four pairs of fresh human specimens from the same patient were included in the RT-PCR analysis. In each pair, one specimen was from primary tumor tissue and the other from metastatic lymph node. Both of them were confirmed by histological examination. All specimens were collected after obtaining the informed consent from the patients. Tissues were frozen at -80°C immediately after separation, till analysis.

Total RNA extraction and mRNA isolation

Cells were grown to 80% confluence in culture bottles, and total RNA was extracted using standard Trizol reagent (Life Technologies, Gaitherbur, MD) following the manufacturer's protocol. Briefly, cells were first homogenized in Trizol solution and incubated for 20 minutes on ice, and then a 1/5 volume of chloroform was added to the homogenates. After vigorous agitation for 5 minutes, the inorganic phase was separated by centrifugation at 12000 g for 15 min at 4°C. RNA was then precipitated in one volume of isopropanol and centrifuged at 10000 g for 20 minutes at 4°C. RNA pellets were washed with 70% ice-cold ethanol and then dissolved in diethyl pyrocarbonate (DEPC)-treated H2O. The amount and quality of total RNA were checked by electrophoresis on a 0.8% formaldehyde agarose gel. Then the polyA mRNA was purified using the Oligotex poly (dT) resin® (Qiagen, German) according to the manufacturer's instruction.

Synthesis and purification of labeled cDNA probe

1–2μg purified mRNA was labeled in reverse transcription using M-MLV reverse transcriptase (Promega, Madison, USA) in the presence of Easytides deoxyadenosin 5' triphosphate [alpha-33P] (NEN® Life Science Product, Boston, USA). Before labeling, total RNA was treated with DNase (Boehringer Mannheim, Germany) and RNasin (Promega, Madison, USA) at 37°C for 15 minutes to remove contaminated DNA. For each labeling, the treated RNA was first incubated with 5 μl oligo dT (Life technologies, USA) at 70°C for 10 min. Then 6 μl 0 5× first strand reaction buffer, 1 μl 0.1 M DTT, 1.5 μl dNTP mixture containing dATP, dGTP and dTTP at 20 Um (Promega, USA), 10 μl alpha-33P dCTP and 1.5 μl superscript reverse transcriptase were added and incubated at 37°C for 90 min. The labeled first strand cDNA probes were purified by spin column (Clontech, CA) to remove the unincorporated nucleotides.

cDNA microarray preparation

Human cDNA clones picked from cDNA libraries were terminal sequenced. A cDNA array was assembled with a total of 7675 cDNA clones representing the same number of independent cDNA clusters. All cDNA fragments were amplified by PCR and verified by gel electrophoresis. PCR products were spotted onto two 8 × 12 cm Hybond®-N nylon membranes (Amersham Pharmacia, Buckinghamshire, UK) using an arrayer (BioRobotics, Cambridge, United Kingdom). Each spot carried ~100 nl in volume and was 0.4 mm in diameter, and each cDNA fragment was placed in two different spots (double-offset). Eight housekeeping genes encoding ribosomal protein S9 (RPS9), β-actin (ACTB), glyceraldehyde-3-phosphate dehydrogenase, hypoxanthine phosphoribosyltransferase 1, Mr 23,000 highly basic protein (RPL13A), ubiquitin C, phospholipase A2, and ubiquitin thiolesterase (UCHL1) were spotted and evenly distributed as hybridization intra-membrane control, each in 12 places. Hybridization data was considered invalid if among the 12 spots representing the same gene, the intensity of the darkest spot exceeded 1.5-fold of that of the weakest spot.

Hybridization and image analysis

Prepared nylon membranes were prehybridized in 20 ml of prehybridization solution (6×SSC, 0.5% SDS, 5×Denhardt's, and 100 μg/ml denatured salmon sperm DNA) at 68°C for 3 hours. Overnight hybridization with the 33P-labeled cDNA in 6 ml of hybridization solution (6×SSC, 0.5% SDS, and 100 μg/ml salmon sperm DNA) was followed by stringent washing (0.1×SSC, 0.5% SDS, at 65°C for 1 h). Membranes were exposed to phosphor screen overnight and scanned using an FLA-3000A Plate/Fluorescent Image Analyzer (Fuji Photo Film, Tokyo, Japan). Radioactive intensity of each spot was linearly digitalized to 65,536 gray-grades in a pixel size of 50 μm in an image reader and recorded using the Array gauge software (Fuji Photo Film, Tokyo, Japan). After subtraction of background chosen from an area where no cDNA was spotted, genes with intensities >10 were considered as positive signals to ensure that they were distinguished from background with statistical significance >99.9%. Normalization among arrays was based on the sum of background-subtracted signals from all genes on the membrane. On the basis of each gene's ratio of expression level between the ACC-M and ACC-2(ACC-M/ACC-2), we divided those with significantly differential expression into two classes: up-regulated with the ratio over 3.0, down-regulated with the ratio below 1/3.

Semi-quantitative RT-PCR

Four selected genes, CD98, L6, RPL29, and TSH, were selected and investigated in this study to evaluate the difference of expression of them between primary ACC tumor tissue and ACC metastasis lymph node in vivo, thus confirming the reliability of our approach. 1 μg each of total RNA from 4 primary tumor tissue samples and 4 corresponding metastasis lymph node tissue samples were used as templates in a total volume of 20 μl reverse transcription reaction system. The cDNA were then transferred to a PCR master mixture containing 1×PCR Buffer, 1.8 mM MgCl2, and 2.5 units of Taq polymerase, 1 μM gene specific primers, as listed in Table 1. Primers were designed using the Primer3 program in the Center for Genome Research at the Whitehead Institute http://www-genome.wi.mit.edu/cgi-bin/primer/primer3_www.cgi. To avoid problems associated with genomic DNA contamination primers were made to span an intron. PCR conditions were as follows: 1 cycle of 95°C for 10 minutes, 25–30 cycles of 95°C for 1 minute, 60°C for 1 minute, and 72°C for 1 minute, and final extension at 72°C for 5 minutes after the last cycle. Amplified fragments were visualized by ethidium bromide staining of the agarose gel and photographed under UV light.
Table 1

Primers used for RT-PCR

Gene

Primers

Product size

RPL29

5'ACATGGCCAAGTCCAAGAAC3' 5'TTAGGCCCTTTTTGTTGTGC3'

165

CD98

5'TCTTGATTGCGGGGACTAAC3' 5'GAGCCTTGCCTGAGACAAAC3'

197

L6

5'AGGGCCAGTACCTTCTGGAT3' 5'AAGCCACATATGCCTCCAAG3'

169

TSH

5'CGTGCCACTGCAGTACTTGT3' 5'TTGAAGCGTGTCAAAGTGGT3'

165

Results

Identification of genes differentially expressed between ACC-2 and ACC-M cells

The cDNA microarray technique was applied to analyze the expression patterns of ACC-2 and ACC-M cells. All tests were considered valid with the maximal variation of hybridization intra-membrane control signal intensity being 1.4-fold. Among relevant 7,675 genes and expressed sequence tags (ESTs) assembled in the cDNA microarray, 30 were found differentially expressed, with the difference signal intensity ratio of more than 3. Genes and ESTs were divided into two groups on the basis of the difference in signal intensity: Out of 30 differentially expressed genes or ESTs, 18 were up-regulated (signal intensity ratio more than 3) in ACC-M clone compared withACC-2 cell line and 12 down-regulated (signal intensity ratio less than 1/3). Results are shown in table 2.
Table 2

List of the up-regulated and down-regulated genes and ESTs in the microarray

Gene bank Accession No

Name

Chromosomal Localization

Ratio of ACC-M/ACC-2

Up-Regulated

   

U05861

hepatic dihydrodiol dehydrogenase

10p15-p14

5.91

D17793

KIAA0119

10p15-p14

6.47

AB007870

KIAA0410

13q12.13

3.63

X16901

RAP30 subunit

13q14

3.18

U09848

zinc finger protein LNF139

7q21.3-q22.1

4.53

X84003

TAF1118

1p13.1

3.40

D90282

carbamyl phosphate synthetase I

2q35

3.40

U40490

nicotinamide nucleotide transhydrogenase

5p13.1-5cen

4.25

D84488

small GTP-binding protein

1q32

3.05

D14533

XPAC protein

9q22.3

3.12

X60221

H+-ATP synthase subunit b

1p13.2

3.12

A1436789

EST

?

3.13

W05842

EST

?

3.18

AA186737

EST

?

3.05

N6482

EST

?

4.36

A1254779

EST

?

3.50

AA582554

EST

?

3.40

AA478034

EST

?

3.10

Down-Regulated

   

AB018010

CD98

11q13

0.22

Z49148

ribosomal protein L29

12q23.1

0.23

L13435

3p21.1 gene

3p21

0.31

U23143

mitochondriol serine hydroxymethyltransferase

12q12-q14

0.32

AW072071

EST

?

0.25

YO0483

gluthathione peroxidase

?

0.30

A1804930

EST

?

0.07

AJ249732

G8 protein

6p21.3

0.25

W20128

EST

?

0.32

AI005336

EST

?

0.25

S70585

thyroid-stimulating hormone (TSH)

6q12-q21

0.30

M90657

tumor antigen (L6)

3q21-q25

0.30

Semi-quantitative RT-PCR analysis

RT-PCR was performed on the primary tumor tissues and metastasis lymph nodes to give in vivo validation to the array results for the selected genes. The results were revealed in electrophoresis pattern shown in Figure 1, confirming the results of the cDNA microarray data. Expression level in ACC metastasis lymph nodes compared with ACC primary tumor tissues showed similarly down-regulation of CD98, RPL29, TSH and L6 in all the samples.
Figure 1

Semi-quantitative RT-PCR analysis of selected genes. Total RNA isolated from 4 primary tumor tissue samples and 4 corresponding metastasis lymph node tissue samples were used as a template for RT-PCR using the gene-specific primers described in Table 2. RT-PCR showing the down-regulation of (A) RPL29, (B) CD98,(C)L6 and (D)TSH. (E)GAPDH was used as an internal reference. Pi, primary tumor; Mi, metastasis tumor (i refer to the number of sample).

Discussion

Several approaches have been used to study the gene profile alteration that may occur in the development of adenoid cystic carcinoma and metastasis [711]. However, most studies were highly focused and do not provide insight into global gene expression pattern although they contributed greatly to our current understanding [12]. Focusing on gene expression patterns of a multitude of genes, referred to as genetic fingerprints, is proving to be a much more powerful approach than studying a small number of genes that may not hold much clinical significance individually [13]. Recently developed cDNA microarray technology makes it possible for us to analyze multi gene related process such as metastasis due to the wide spectrum of genes and high throughput analysis [14]. In this study, we have chosen human adenoid cystic carcinoma cell lines as a model system to study changes in gene expression that should be of importance in the pathogenesis of metastasis. Cell lines are important tools for in vitro studies of neoplasms, and are highly valuable in studies requiring considerable amount of material [15]. On the other hand, since adenoid cystic carcinoma itself is heterogeneous in nature, cell lines can supply a homogeneous cell population exempted from the disturbance of non-parenchyma cells in ACC tumor, including stroma cells, adipose cells, endothelial cells, and infiltrating lymphocytes, and give a more accurate data interpretation.

In this study, cDNA microarray analysis of cell line ACC-2 and cell clone ACC-M revealed several differentially expressed genes. The reliability of our approach was further validated using RT-PCR on 4 paired specimens from primary tumor and metastatic lymph node. The difference of expression level of 4 selected genes disclosed by semi-quantitative RT-PCR was consistent with the results of cDNA microarray analysis. Among those differentially expressed genes, some of genes were already known as active participants related to tumor and metastasis.

CD98 is found under expressed by 4.5-fold in ACC-M clone compared with ACC-2 in our study. CD98, a heterodimeric type II transmembrane glycoprotein present in all established human cell lines and most malignant human cells, functions in amino acid transport, cell fusion and homotypic cell aggregation [16]. It is also an important regulator of integrin activation and an oncogenic protein, playing a role in integrin signaling pathways [1719]. It is known that integrin is key factor for cell invasion and migration, allowing the cancer cell to penetrate itself into the tissue and attach to the target tissue's basal matrix by binding to components of the extra cellular matrix [20]. Integrin-CD98 association and relative mechanisms to regulate cell adhesion, migration, and metastasis have been profoundly reported recently. Zent et al have shown that CD98 can associate with isolated cytoplasmic portions of some β1 integrin isoforms [21]. Kolesnikova et al have recently shown that CD98 constitutively and specifically associated with various β1 integrin subunits by reciprocal immunoprecipitation experiments [12]. They propose that by cross-linking CD98, it acts as a "molecular facilitator" in the plasma membrane, clustering β1 integrins to form high-density complexes, thus subsequently lead to integrin activation and adhesion, integrin signaling, and anchorage-independent growth. In this context, our observation of down-regulated expression of CD98 in high metastasis cell clone might contribute to the elucidation of suppressive role of CD98 in molecular mechanism of metastasis although further investigation is needed to postulate relationship of the distribution of CD98 and the tumor-spreading pattern and differentiation and metastatic behavior in ACC.

Ribosomal protein L29 gene (Heparin/Heparan Sulfate Interacting Protein Gene) was shown to be down regulated in high metastasis cell clone ACC-M. HIP is a cell surface binding protein, highly expressed in most human epithelial cell lines, including uterine and fibroblastic cells [22]. By binding directly to heparin it functions as a cell-cell and cell-extracellular matrix adhesion molecule [2326]. Recent studies suggested that HIP may play a role in cell migration and metastasis of melanoma and breast cancer cells [27, 28]. Wang et al [29], reported Heparin/Heparan sulfate interacting protein (HIP) to be up-regulated in colorectal carcinoma, a significant inverse correlation between HIP levels and the presence of distant metastasis (Duke's stage D) was also noted. This indicated up-regulation of HIP to be an early event in carcinogenesis, and its increased expression may facilitate growth. Later in tumor progression, HIP may be down-regulated to decreased cell adhesion, favoring metastasis. Our data supports the concept of down-regulation of HIP in tumors with metastasis.

Under expression of tumor-associated antigen L6 gene in high metastatic cell clone and metastasis lymph nodes is another finding in this report. Tumor-associated antigen L6 is a kind of cell surface antigen that is known to express in the lung, breast, colon, and ovarian carcinomas, mediating cell to cell and cell-to matrix interaction [30]. However, other research have shown that the expression level of L6 was found to increase in metastatic-tumor-derived cells compared with primary-tumor-derived cells [31], thus leaving the controversy open, and requiring further investigations.

Thyroid stimulating hormone (TSH) has been reported to be associated with enhanced tumor proliferation and aggressiveness in follicular thyroid cancer cells [32]. Loss of TSH stimulation allows the lung metastasis the highest basal invasive potential [33]. Similar findings of decrease in TSH mRNA expression level had been observed in ACC-M cell line and metastasis lymph node, supporting the previous report of metastasis inhibiting role of TSH [33]. Our results show that a decreased transcription of this gene could be helpful for the tumor metastasis by decreased cell adhesion and loss of contact inhibition control.

It is clear from our results that high metastasis ACC cell clone has a genome-wide genetic expression profile that differs from primary ACC cell line. Having determined that such differences exist, the next question is to what extent are these gene expression alterations related to metastasis? Or whether there is genetic expression profile heterogeneity among tumors of a particular histopathologic grade and metastatic potential? Future studies are needed to directly compare gene expression profiles between different samples of same stage to affirm this possibility.

Analysis of cell lines using cDNA microarray also revealed several novel differentially expressed genes, which have not been previously reported to be related to tumor metastasis, for example, TAF1118 gene, zinc finger protein LNF139 gene, hepatic dihydrodiol dehydrogenase gene etc. The further validation and detailed mechanism of these genes in conferring the phenotype of high metastasis to a cell need more extensive investigation. The global molecular regulatory network would also shed a light on our understanding of the mechanism of pathogenesis of metastasis and potential gene therapy[34].

We report here an average 3-fold differential expression ratio. We attribute this result to the close genetic background of the ACC-2 and ACC-M cells as they were derived from the same parental source. ACC-2 was established from primary adenoid cystic carcinoma and ACC-M was screened out from ACC-2. We have set only those genes significant whose expression was changed by at least a factor of three. However, we think that this cutoff point is arbitrary and that there might exist some important enough genes whose expression changed less than a factor of three. It is necessary to complement the preliminary cDNA microarray results with some more sensitive confirmatory experiments for target genes. There might be some false negative metastasis-related genes missed in this report especially for those with signal intensity ratio less than three. Hopefully, this technical problem could be prevented and the overall signal intensities could be adjusted by a newly introduced optimization strategy called directed application of capture oligonucleotides [35].

The measurement of gene expression can, therefore, provide information on regulatory mechanisms, biochemical pathways, cellular control mechanisms and potential targets for intervention and therapy in a variety of disease states [36]. Our results support the hypothesis that multiple specific genes may contribute to the development of distant metastases from adenoid cystic carcinoma cells. cDNA microarray analysis of the model system for differentially expressed gene involved in adenoid cystic carcinoma metastasis has revealed a variety of specific genes, including putative common metastasis-related genes, which provides a basis for rationally determining which pathways are appropriate for further study and which molecular targets are potential targets for gene therapy [37]. These findings will provide new insights to further explore the complicated molecular events of tumor metastasis. Studies of molecular mechanism regulating these changes would help to identify prognostic marker and treatment targets for cancer metastasis and allow more accurate risk stratification for clinical metastases.

Declarations

Authors’ Affiliations

(1)
Laboratory of Cancer Biology, Shanghai Second Medical University
(2)
Department of Pathology, Virginia Commonwealth University

References

  1. Spiro RH: Distant metastasis in adenoid cystic carcinoma of salivary origin. Am J Surg. 1997, 174: 495-498. 10.1016/S0002-9610(97)00153-0.View ArticlePubMedGoogle Scholar
  2. Ito H, Hatori M, Kinugasa Y, Irie T, Tachikawa T, Nagumo M: Comparison of the expression profile of metastasis-associated genes between primary and circulating cancer cells in oral squamous cell carcinoma. Anticancer Res. 2003, 23: 1425-1431.PubMedGoogle Scholar
  3. Timar J, Ladanyi A, Petak I, Jeney A, Kopper L: Molecular pathology of tumor metastasis III. Pathol Oncol Res. 2003, 9: 49-72.View ArticlePubMedGoogle Scholar
  4. Qin Xu, Dan Huang: Biochip and cancer research. In Defeating head and neck cancer: crucial progress for basic research. Edited by: Li JR, He RG. 2000, Wu Han: Hubei Science and Technology Press, 227-232. 1Google Scholar
  5. Guan XF, Qiu WL, He RG, Zhou XJ: Selection of adenoid cystic carcinoma cell clone highly metastatic to the lung: an experimental study. Int J Oral Maxillofac Surg. 1997, 26: 116-119.View ArticlePubMedGoogle Scholar
  6. Guan X, Qiu W, He R: The selection of highly lung metastatic salivary adenoid cystic carcinoma clone. Zhonghua Kou Qiang Yi Xue Za Zhi. 1996, 31: 74-77.PubMedGoogle Scholar
  7. Suzuki K, Cheng J, Watanabe Y: Hepatocyte growth factor and c-Met (HGF/c-Met) in adenoid cystic carcinoma of the human salivary gland. J Oral Pathol Med. 2003, 32: 84-89. 10.1034/j.1600-0714.2003.00018.x.View ArticlePubMedGoogle Scholar
  8. Dori S, Vered M, David R, Buchner A: HER2/neu expression in adenoid cystic carcinoma of salivary gland origin: an immunohistochemical study. J Oral Pathol Med. 2002, 31: 463-467. 10.1034/j.1600-0714.2002.00017.x.View ArticlePubMedGoogle Scholar
  9. Zhang ZY, Wu YQ, Zhang WG, Tian Z, Cao J: The expression of E-cadherin-catenin complex in adenoid cystic carcinoma of salivary glands. Chin J Dent Res. 2003, 3: 36-39.Google Scholar
  10. Kiyoshima T, Shima K, Kobayashi I, Matsuo K, Okamura K, Komatsu S, Rasul AM, Sakai H: Expression of p53 tumor suppressor gene in adenoid cystic and mucoepidermoid carcinomas of the salivary glands. Oral Oncol. 2001, 37: 315-322. 10.1016/S1368-8375(00)00083-X.View ArticlePubMedGoogle Scholar
  11. Takata T, Kudo Y, Zhao M, Ogawa I, Miyauchi M, Sato S, Cheng J, Nikai H: Reduced expression of p27(Kip1) protein in relation to salivary adenoid cystic carcinoma metastasis. Cancer. 1999, 86: 928-935. 10.1002/(SICI)1097-0142(19990915)86:6<928::AID-CNCR6>3.3.CO;2-O.View ArticlePubMedGoogle Scholar
  12. Mendez E, Cheng C, Farwell DG, Ricks S, Agoff SN, Futran ND, Weymuller EA, Maronian NC, Zhao LP, Chen C: Transcriptional expression profiles of oral squamous cell carcinomas. Cancer. 2002, 95: 1482-1494. 10.1002/cncr.10875.View ArticlePubMedGoogle Scholar
  13. Webb T: Microarray studies challenge theories of metastasis. J Natl Cancer Inst. 2003, 95: 350-351. 10.1093/jnci/95.5.350.View ArticlePubMedGoogle Scholar
  14. Chee M, Yang R, Hubbell E, Berno A, Huang XC, Stern D, Winkler J, Lockhart DJ, Morris MS, Fodor SP: Accessing genetic information with high-density DNA arrays. Science. 1996, 274: 610-614. 10.1126/science.274.5287.610.View ArticlePubMedGoogle Scholar
  15. Wolf M, El-Rifai W, Tarkkanen M, Kononen J, Serra M, Elomaa I: Novel findings in gene expression detected in human ostsosarcoma by cDNA microarray. Cancer Genet Cytogenet. 2000, 123: 128-132. 10.1016/S0165-4608(00)00319-8.View ArticlePubMedGoogle Scholar
  16. Kolesnikova TV, Annion BA, Berditchevski F, Hemler ME: β1 integrins show specific association with CD98 protein in low density membranes. BMC Biochem. 2001, 2: 10-10.1186/1472-2091-2-10.PubMed CentralView ArticlePubMedGoogle Scholar
  17. Esteban F, Ruiz-Cabello F, Concha A, Perez Ayala M, Delgado M, Garrido F: Relationship of 4F2 antigen with local growth and metastatic potential of squamous cell carcinoma of the larynx. Cancer. 1990, 66: 1493-1498.View ArticlePubMedGoogle Scholar
  18. Fenczik CA, Sethi T, Ramos JW, Hughes PE, Ginsberg MH: Complementation of dominant suppression implicates CD98 in integrin activation. Nature. 1997, 390: 81-85. 10.1038/36349.View ArticlePubMedGoogle Scholar
  19. Rintoul RC, Buttery RC, Mackinnon AC, Wong WS, Mosher D, Haslett C, Sethi T: Cross-linking CD98 promotes integrin-like signaling and anchorage-independent growth. Mol Biol Cell. 2002, 13: 2841-2852. 10.1091/mbc.01-11-0530.PubMed CentralView ArticlePubMedGoogle Scholar
  20. Hood JD, Cheresh DA: Role of integrins in cell invasion and migration. Nat Rev Cancer. 2002, 2: 91-100. 10.1038/nrc727.View ArticlePubMedGoogle Scholar
  21. Zent R, Fenczik CA, Calderwood DA, Liu S, Dellos M, Ginsberg MH: Class and splice variant-specific association of CD98 with integrin β cytoplasmic domains. J Biol Chem. 2000, 275: 5059-5064. 10.1074/jbc.275.7.5059.View ArticlePubMedGoogle Scholar
  22. Liu S, Smith SE, Julian J, Rohde LH, Karin NJ, Carson DD: cDNA cloning and expression of HIP, a novel cell surface heparan sulfate/heparin-binding protein of human uterine epithelial cells and cell lines. J Biol Chem. 1996, 271: 11817-11823. 10.1074/jbc.271.20.11817.View ArticlePubMedGoogle Scholar
  23. Liu S, Hoke D, Julian J, Carson DD: Heparin/heparin sulfate (HP/HS) interacting protein (HIP) supports cell attachment and selective high affinity binding of HP/HS. J Biol Chem. 1997, 272: 25856-25862. 10.1074/jbc.272.41.25856.View ArticlePubMedGoogle Scholar
  24. Raboudi N, Julian J, Rohde LH, Carson DD: Identification of cell-surface heparin/heparan sulfate-binding proteins of a human uterine epithelial cell line (RL95). J Biol Chem. 1992, 267: 11930-11939.PubMedGoogle Scholar
  25. Liu S, Zhou F, Hook M, Carson DD: A heparin-binding synthetic peptide of heparin/heparan sulfate-interacting protein modulates blood coagulation activities. Proc Natl Acad Sci USA. 1997, 94: 1739-1744. 10.1073/pnas.94.5.1739.PubMed CentralView ArticlePubMedGoogle Scholar
  26. Liu S, Julian J, Carson DD: A peptide sequence of heparin/heparan sulfate (HP/HS)-interacting protein supports selective, high affinity binding of HP/HS and cell attachment. J Biol Chem. 1998, 273: 9718-9726. 10.1074/jbc.273.16.9718.View ArticlePubMedGoogle Scholar
  27. Marchetti D, Liu S, Spohn WC, Carson DD: Heparanase and a synthetic peptide of heparan sulfate-interacting protein recognize common sites on cell surface and extracellular matrix heparan sulfate. J Biol Chem. 1997, 272: 15891-15897. 10.1074/jbc.272.25.15891.View ArticlePubMedGoogle Scholar
  28. Jacobs AL, Julian J, Sahin AA, Carson DD: Heparin/heparan sulfate interacting protein expression and functions in human breast cancer cells and normal breast epithelia. Cancer Res. 1997, 57: 5148-5154.PubMedGoogle Scholar
  29. Wang Y, Cheong D, Chan S, Hooi SC: Heparin/Heparan Sulfate Interacting protein gene expression is up-regulated in human colorectal carcinoma and correlated with differentiation status and metastasis. Cancer Res. 1999, 59: 2989-2994.PubMedGoogle Scholar
  30. Marken JS, Schieven GL, Hellstrom I: Cloning and expression of the tumor-associated antigen L6. Proc Natl Acad Sci USA. 1992, 89: 3503-3507.PubMed CentralView ArticlePubMedGoogle Scholar
  31. Otsuka M, Kato M, Yoshikawa T, Chen H, Brown EJ, Masuho Y, Omata M, Seki N: Differential expression of the L-plastin gene in human colorectal cancer progression and metastasis. Biochem Biophys Res Commun. 2001, 289: 876-881. 10.1006/bbrc.2001.6047.View ArticlePubMedGoogle Scholar
  32. Huang E, Cheng SH, Dressman H, Pittman J, Tsou MH, Horng CF, Bild A, Iversen ES, Liao M, Chen CM, West M, Nevins JR, Huang AT: Gene expression predictors of breast cancer outcomes. Lancet. 2003, 361: 1590-1596. 10.1016/S0140-6736(03)13308-9.View ArticlePubMedGoogle Scholar
  33. Hoelting T, Goretzki PE, Duh QY: Follicular thyroid cancer cells: a model of metastatic tumor in vitro. Oncol Rep. 2001, 8: 3-8.PubMedGoogle Scholar
  34. Dan H, Jingrong L: Progress of gene therapy on head and neck cancer. Arch Med Rev. 1998, 7: 84-85.Google Scholar
  35. Peplies J, Glockner FO, Amann R: Optimization strategies for DNA microarray-based detection of bacteria with 16S rRNA-targeting oligonucleotide probes. Appl Environ Microbiol. 2003, 69: 1397-1407. 10.1128/AEM.69.3.1397-1407.2003.PubMed CentralView ArticlePubMedGoogle Scholar
  36. Brown I, Heys SD, Schofield AC: From Peas to "Chips" – The new millennium of molecular biology: A primer for the surgeon. World J Surg Oncol. 2003, 1: 21-10.1186/1477-7819-1-21.PubMed CentralView ArticlePubMedGoogle Scholar
  37. Dan H, Jinrong L: The establishment of gene transfer into rabbit sternoclaidomastoid muscle in vivo and optimization. J Head Neck Surg. 2000, 10: 308-311.Google Scholar

Copyright

© Huang et al; licensee BioMed Central Ltd. 2003

This article is published under license to BioMed Central Ltd. This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose, provided this notice is preserved along with the article's original URL.

Advertisement