Monitoring gene expression profile changes in ovarian carcinomas using cDNA microarray

Gene 229 (1999) 101–108

Monitoring gene expression profile changes in ovarian carcinomas using cDNA microarray Kai Wang a, *, Lu Gan a, Eric Jeffery a, Margit Gayle a, Allen M. Gown b, Marilyn Skelly b, Peter S. Nelson c, WeiLap V. Ng c, Miche`l Schummer c, Leroy Hood c, John Mulligan a a Chiroscience R&D, Inc., 1631 220th SE., Bothell, WA 98021, USA b Department of Pathology, University of Washington, Seattle, WA 98195, USA c Department of Molecular Biotechnology, University of Washington, Seattle, WA 98195, USA Received 27 October 1998; received in revised form 5 January 1999; accepted 11 January 1999; Received by J.L. Slightom

Abstract The development of cancer is the result of a series of molecular changes occurring in the cell. These events lead to changes in the expression level of numerous genes that result in different phenotypic characteristics of tumors. In this report we describe the assembly and utilization of a 5766 member cDNA microarray to study the differences in gene expression between normal and neoplastic human ovarian tissues. Several genes that may have biological relevance in the process of ovarian carcinogenesis have been identified through this approach. Analyzing the results of microarray hybridizations may provides new leads for tumor diagnosis and intervention. © 1999 Elsevier Science B.V. All rights reserved. Keywords: Array; Carcinogenesis; Ovarian cancer; RT–PCR; Tumor marker

1. Introduction Cancers have been defined as a group of cells exhibiting an unrestrained proliferation phenotype. The development of cancer is the result of a series of molecular changes that have occurred in the cell. These events lead to expression changes of numerous genes, accompanied by different histological or clinical classification of this abnormal cell growth. Current methods of comparing global gene-expression profile changes in different tissues or different pathological specimens include high-density oligo arrays (Lockhart et al., 1996), differential display (Liang and Pardee, 1992), serial analysis of gene expression (SAGE) ( Velculescu et al., 1995), differential cDNA screening (cDNA array) (Peitu et al., 1996), large-scale cDNA sequencing (Lee et al., 1995), expressed sequence tag ( EST ) database comparison ( Vasmatzis et al., 1998) and two-dimensional gel electro* Corresponding author. Tel.: +1 425-489-8046; fax: +1 425-489-8019; e-mail: [email protected]. Abbreviations: DAD 1, defender against cell death 1 (an apoptotic suppresser gene); EST, expressed sequence tag; PCR, polymerase chain reaction; SAGE, serial analysis of gene expression; SSC, standard saline citrate buffer.

phoresis of cellular proteins. Approaches such as differential display and SAGE are suitable for the in-depth sampling of gene expression changes in a few samples of interest, but due to their gel-based methodology they are laborious and limited by the number of samples that can be studied simultaneously. High-density oligo arrays, on the other hand, are high throughput and provide identity and expression level changes of selected genes simultaneously (Lockhart et al., 1996). However, this method can only be applied to known gene sequences, since those arrayed oligos have to be synthesized from reported sequences. Through the advancement of miniaturization of cDNA array fabrication and attachment chemistry between DNA molecules and the surface of solid supporting materials, a microarray with a density of over 1000 independent cDNA clones per cm2 on poly-L-lysine-treated glass plate has been described recently (Schena et al., 1995). This approach allows monitoring of the expression level of thousands of both selected known genes and cDNAs representing uncharacterized genes between numerous biological samples in a comparative, parallel fashion. Ovarian cancer represents the most lethal neoplasm of the female genital tract, because of its symptomless progression in the early stage. Therefore, most ovarian

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cancers are diagnosed at an advanced stage of the disease, and the resulting treatment outcomes are poor (Ovarian Cancer: Screening, Treatment and Followup. NIH Consensus Statement, 1994). To identify and monitor gene expression profile changes in ovarian tumor specimens may shed light not only on the cause of these pathological changes, but also provide the opportunity to identify novel targets for disease detection and intervention. In this report, we describe the assembly and utilization of ovarian cDNA microarray designed to assess the differences in gene expression between normal and neoplastic ovarian tissues.

2. Materials and methods 2.1. Tissue specimens and RNA Most of the frozen ovarian cancer specimens were obtained from the Gynecologic Oncology Group, Cooperative Human Tissue Network (GOG/CHTN ) (Columbus, OH ); the remaining samples were acquired by Dr A.M. Gown through the University of Washington Medical Center. Frozen sections were made from these tissues and examined independently of the original pathological report. Total RNA was extracted, using the standard Triazol RNA isolation protocol (Life Technologies, Gaithersburg, MD), from tissue blocks that contained over 75% of neoplastic cells. The amount and the quality of RNA were checked by electrophoresis on a 1% formamide agarose gel. Normal tissue RNA samples were obtained from either Clontech (Palo Alto, CA) or Biochain Institute (San Leandro, CA). 2.2. Northern blot analysis Northern blot was prepared with total RNA from normal and tumor tissues as described (Maniatis et al., 1989). The membrane was hybridized overnight with 32P-labeled DNA fragments in 50% formamide (v/v), 5×standard saline citrate (SSC ) buffer (1×SSC= 0.15 M NaCl/0.15 M sodium citrate), 0.02 M sodium phosphate (pH 6.7), 100 mg/ml denatured salmon sperm DNA, 1% SDS, 0.5% non-fat dried milk and 10% dextran sulphate at 37°C. Following hybridization, the membrane was washed twice in 2×SSC with 0.1% SDS at room temperature and twice in 0.5×SSC with 0.1% SDS at 65°C. The filter was then blotted dry and exposed to X-ray film at −70°C with intensifying screens. 2.3. Polymerase chain reaction Polymerase chain reactions (PCR) (Saiki et al., 1988) with vector-specific primers were used to isolate cDNA inserts for array construction and sequencing. The primer sequences were as follows: LacF (5∞)

CGCCAGGGTTTTCCCAGTCACGACGTTG and LacR (5∞) GTATGTTGTGTGGAATTGTGAGCGGA. The PCR reactions were prepared according to standard protocol and the reaction mixtures were subjected to 35 cycles of amplification. Each cycle consisted of 20 s of denaturation at 94°C, 45 s of primer annealing at 60°C, followed by 90 s of extension at 72°C. PCR with gene-specific primers on a panel of cDNA samples from normal and neoplastic tissues was used to verify the result of array hybridization. First-strand cDNA was synthesized from approx. 1 mg of DNase I treated total RNA using oligo d( T ) with Superscript reverse transcriptase (Life Technologies, Gaithersburg, MD). Approx. 20 ng of cDNA was added to 50 ml of PCR mixture that consisted of 50 mM NaCl, 20 mM Tris–HCl (pH 9.0), 2.5 mM MgCl , 0.5 mM dithio2 threitol (DTT ), 100 mM of each nucleotide, 1 mM of each gene-specific primer and 1 unit of Taq DNA polymerase. Gene-specific primers were synthesized from GenBank sequence entries or sequencing results of arrayed clones. A ubiquitously expressed apoptotic suppresser gene, defender against cell death 1 (DAD 1), was used as control (Nakashima et al., 1993). The primer sequences for DAD1 are as follows: DAD1F (5∞) TGAGCTCCACTCCGCGTCTGAAG and DAD1R (5◊) GGCATGGAGTTCTTTAATTTGG. 2.4. cDNA libraries and array construction The poly(A) mRNA was selected through an oligo(dT )-cellulose column and cDNAs were synthesized and cloned into either lambda-ZAP (Stratagene, La Jolla, CA) or pSPORT (Life Technologies, Gaithersburg, MD) directional cloning vectors following the protocols provided by the manufacturers. All of the cDNA libraries were taken through one round of amplification, except the cDNA library from normal ovary that was not amplified. Libraries were plated out on agarose plates containing X-gal and IPTG. The white recombinant colonies or phage plaques were picked randomly and placed directly into individual PCR tubes. The cDNA inserts were amplified from the clones with vector primers. An aliquot of the PCR product was examined by agarose gel electrophoresis. PCR products were purified through Sephacryl-400HR (Pharmacia LKB, Uppsala, Sweden) packed 96-well mini-columns as described ( Wang et al., 1995). For arraying, the purified cDNA inserts were lyophilized and resuspended in 5 ml of 3×SSC. EST clones containing selected oncogenes, tumor suppresser genes, cytokines, cytokine receptors and housekeeping genes were obtained from Research Genetics (Huntsville, AL). The EST clones were re-sequenced at our sequencing facility to verify their identify. The cDNA inserts were amplified, purified, and lyophilized for arraying as described above.

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Chloramphenical acetyltransferase (CAT ), luciferase and kanamycin resistance genes were included in the array as negative control. 2.5. Fluorescent probe, hybridization and scanning The preparation of fluorescent labeled probes was essentially as described (Schena et al., 1995), except that approx. 30 mg of DNase I-treated total RNA was used in the labeling reaction. After pooling the Cy3-dCTP (Amersham) and Cy5-dCTP (Amersham) incorporated cDNA samples, the labeled cDNA was degraded by incubating the sample at 37°C for 10 min in the presence of 25 mM NaOH. After being neutralized with Tris– HCl, the probe was precipitated and resuspended in 5 ml of 10×SSC with 0.2% SDS. Array hybridization, scanning and data collection protocols have been described (Schena et al., 1995) and were performed at Synteni (Fremont, CA). 2.6. DNA sequencing and sequence analysis The identities of differentially expressed genes were determined by DNA sequencing using vector-specific primers (either M13 forward or reverse primer). Cycle sequencing reactions with Taq DNA polymerase were performed with fluorescently labeled dideoxynucleotides (Dye-terminator, Applied BioSystems, Foster City, CA). The reactions were resolved on a fluorescent DNA sequencer (Applied BioSystems, Foster City, CA). The sequences were edited using the Sequencher DNA analysis program (Gene Codes, Ann Arbor, MI ). Sequence database searches were performed with BLAST sequence comparison programs at NCBI (http://www.ncbi.nlm. nih.gov/blast/http://www.ncbi.nlm.nih.gov/BLAST/).

3. Results and discussion 3.1. A 5766 member microarray was assembled with ESTs and clones from several cDNA libraries To investigate gene expression profile changes in the development of ovarian carcinomas, a cDNA microarray was assembled with selected ESTs and clones from several ovarian cDNA libraries. The array consisted of a total of 5766 members, representing 5376 randomly picked clones from several cDNA libraries, 342 EST clones and three bacterial genes occupying 48 different spots on the array to serve as negative controls (Table 1). The arrayed known cDNAs included several housekeeping genes and some previously identified cancer-related genes. In order to evaluate the complexity of arrayed cDNA clones, a microtiter plate containing 96 randomly picked cDNA clones was selected and sequenced. Database

Table 1 Composition of ovarian cDNA microarray No. cDNA clones Ovarian cancer library No. 1 Ovarian cancer library No. 2 Ovarian surface epithelial cell library Normal ovary EST clones Oncogenes and tumor suppressor genes Cytokines and cytokine receptors Cell division-related Growth factors and growth factor receptors Others Negative controls Total arrayed clones

Total No. 5376

864 768 2592 1152 342 70 15 72 20 165 48 5766

The number of clones from each cDNA library is listed. The EST clones are grouped into different categories. Cell division-related genes include cyclines, different histones, different DNA and RNA polymerases, and several DNA repair enzymes. Genes like actin and G3PDH are grouped into others.

comparison reveals transcripts from 94 different genes, including five ribosomal protein-encoding genes, three mitochondrial transcripts, two Selenoprotein P genes (GenBank accession No.: Z11793) and two Vimentin transcripts (GenBank accession No.: M25246) from these 96 cDNA clones. This result indicates that these randomly picked arrayed clones comprise a complex cDNA population. Due to the fabrication and scanning protocols of the microarray, several locations without DNA samples were also dotted, probed and scanned. The average fluorescent signals of these blank spots were used as background to correct the hybridization signals for arrayed DNA samples. To determine the reliability of hybridization signals and experimental variables unrelated to the differences in hybridization probes, the array was hybridized independently three times with Cy3- and Cy5-labeled liver cDNA probes simultaneously. The results show that on average, over 80% of the clones exhibit a Cy5 to Cy3 signal ratio, less than 1.5-fold intensity differences, and only about 3% of the clones have a ratio greater than 2. Scatter plots with the values of Cy3 and Cy5 fluorescent signals also reveal a very tight distribution pattern and clustered in an almost 45° diagonal line as expected. The scatter plot for one of the liver/liver hybridization result is shown in Fig. 1A. A small number of clones exhibit greater than 2-fold signal differences in the liver/liver hybridization experiments. The arrayed cDNAs exhibiting these signal differences are not consistent between identical replications of these liver/liver control hybridization (data not shown). This suggests that a small fraction of the observed signal variations in hybridization experiments may be contributed by minor differences in probe preparation, hybridization condition, data collection and the amount of immobi-

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Fig. 1. Scatter plots for a Cy5-liver/Cy3-liver control hybridization (A) and a Cy5-ovarian tumor/Cy3-normal ovary hybridization (B). The value of Cy3 and Cy5 hybridization signals from each clone were plotted directly onto the plot.

lized DNA between fabricated arrays. However, these signal variations are unlikely to appear at the same location in different hybridization experiments; these errors can be addressed by averaging the signal intensities from replicate experiments. Hybridization results of the microarray with Cy3-labeled cDNA probe from normal ovary and Cy5-labeled cDNA probe from ovarian tumor demonstrate that, on average, approx. 30% of the cDNAs exhibit more than a 2-fold expression level change and about 9% of the cDNAs had a difference in expression of greater than 3-fold. Scatter plots with tumor probes revealed a very wide distribution pattern ( Fig. 1B). Unlike the results from control hybridization experiments, most of the clones that display more than 3-fold expression level differences in tumor probes are reproducible in different hybridizations. 3.2. Clones exhibiting differential expression profiles in ovarian cancer specimens were sequenced To investigate and monitor the gene expression profile changes in ovarian cancers, replicates of the fabricated cDNA arrays were hybridized independently with cDNA probes that were generated from seven different ovarian tumor specimens, including two papillary serous ovarian tumors, two endometroid ovarian cancers, one poorly differentiated ovarian tumor, one mucinous and one clear cell ovarian tumor. Based on the results of the

control hybridization experiments described above, two criteria were used to classify candidate cDNAs as differentially expressed between normal and neoplastic ovary. The cDNA clones exhibited a 3-fold or greater change in expression level in more than one ovarian tumor probe, and the signal intensity exceeded the background. After analyzing the ovarian cancer hybridization results, 726 such clones were selected for further analysis. Of these, 295 cDNAs exhibit greater than 3-fold overexpression in tumor probe relative to normal probe, and 431 cDNAs reveal greater than 3-fold overexpression in normal probe relative to tumor probe. The corresponding cDNA inserts for these 726 clones were re-amplified from the original PCR amplification plates and then sequenced. 31 clones were not re-amplified and 37 clones did not yield readable sequences; therefore, the identities of these 68 clones were not determined. The remaining cDNAs contain 94 various ribosomal protein-encoding genes, 149 mitochondria-derived transcripts and sequences from 248 different genes that include 12 novel sequences, 49 EST matches and 178 known genes. The 149 mitochondrial transcripts are all over-expressed in tumor samples and may be the result of a highly proliferative phenotype for cancer cells. Except the ribosomal protein S26 (GenBank accession No.: X69654), the 94 various sequences encoding ribosomal proteins all show a much lower expression level in the cancer samples. Comparing our results to those obtained

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in pancreatic and colon cancers with SAGE ( Velculescu et al., 1995) (http://welchlink.welch.jhu.edu/~molgeng/Table1.htm), indicates that the micro-array approach described here identified fewer differentially expressed novel sequences, and putative genes with only matches to sequences in the EST database. This may be the result of different experimental approach, different tumor specimens used in the study, or, more likely, the result of cDNA library amplification process employed, since the library amplification process will change representation of expressed genes. Except the library from normal ovary, all cDNA libraries used in the array construction have been amplified once.

other cancers. Epithelial glycoprotein (GenBank accession No.: M32306), for example, has been reported to be overexpressed in pancreatic and colorectal cancers (Simon et al., 1990). Other genes like, calgizzarin (GenBank Accession No.: D38583) (Tanaka et al., 1995), interferon alpha-induced gene, P27 (GenBank accession No.: X67325) (Rasmussen, et al., 1993), tumor antigen L6 (GenBank accession No.: M90657) (Marken et al., 1992), PUMP-1 (GenBank accession No.: Z11887) (Marti et al., 1992) and M2 type pyruvate kinase (GenBank accession No.: M23725) ( Tani et al., 1988) have all been shown to overexpress in other cancers. These findings illustrate that the phenotypical similarity among different cancers is also reflected at the molecular level. Over 70% of the ovarian cancers are derived from ovarian surface epithelium(NIH Consensus Statement, 1994), several genes that are preferentially expressed in epithelial cells such as cytokeratin 8 (GenBank accession No.: X12882) ( Trask et al., 1990), calgizzarin, and epithelial glycoprotein are also found overexpressed in ovarian cancer samples as expected. Oncogenes, FOS (GenBank accession No.:

3.3. Genes involved in other cancers are also involved in ovarian carcinomas Excluding the ribosomal proteins and transcripts from mitochondrial genome, the top 15 overexpressed and underexpressed genes in ovarian cancer samples are listed in Table 2. It is interesting that most of these genes listed in Table 2 are also found to be involved in Table 2 List of differentially expressed genes in ovarian cancer samples Number

Identity

GenBank accession No.

Average signal ratio with control probes (Cy5-liver/Cy3-liver)

Average signal ratio with tumor probes (Cy5-ovarian tumor/Cy3-ovary)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

FOS Chondroitin Vimentin FOSB (G0S3) Protein tyrosine phosphatase TR3 orphan receptor Glutathione S-transferase Connective tissue growth factor Novel sequence Progesterone receptor-associated protein JUN Protease nexin Monoamine oxidase G Zinc finger protein Selenoprotein P

V01512 M14219 M25146 L49169 X68277 L13740 M21758 U14750 – U28918 U65928 A03911 S62734 M92843 Z11793

0.95 1.00 0.84 1.5 0.98 1.36 1.24 1.41 0.97 1.03 0.81 1.60 0.99 0.76 0.82

0.12 0.12 0.13 0.14 0.15 0.15 0.17 0.30 0.20 0.22 0.22 0.23 0.33 0.25 0.33

16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

Non-muscle type cofilin Alpha-enolase Apoferritin H chain Glucose transporter, HepG2 P27, interferon alpha-inducible gene M2 type pyruvate kinase Tumor antigen L6 CD9 Prostaglandin G/H synthetase Calgizzarin Mesotheli HE4 extracellular protease inhibitor Cytokeratin 8 PUMP 1 (MMP 7) Epithelial glycoprotein (GA733)

X95404 M14328 X00318 K03195 X67325 M23725 M90657 M38690 L15326 D38583 U40434 X63187 X12882 Z11887 M32306

0.84 1.15 0.97 1.07 1.16 1.15 1.45 0.75 1.79 1.37 1.17 1.60 0.88 1.20 1.28

2.66 3.10 3.35 3.45 3.54 3.56 4.21 4.34 4.89 4.76 6.84 7.26 8.37 8.58 13.87

The average expression level changes from three control and seven different tumor probes are listed on the right.

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Fig. 2. Expression of mesothelin analyzed by RT–PCR (A) and Northern hybridization (C ). Tissues used in the RT–PCR panel and Northern blot are indicated on top of each lane. Types of ovarian cancer specimens used in RT–PCR are indicated on the bottom of each sample. PS, papillary serous ovarian tumor; CL, clear cell ovarian cancer; EN, endometroid ovarian cancer; PD, poorly differentiated ovarian tumor; MU, mucinous tumor. Numbers of amplification cycles are indicated on the right of each agarose gel panel. The primer sequences for mesothelin are as follows: MES F CACGAAATGAGTCCTCAGGC and MES R CTTGCACGCTGAGGTCTAGGA. Each amplification cycle for mesothelin expression analysis consisted of 5 s of denaturation at 94°C, 45 s of primer annealing at 60°C followed by 90 s of extension at 72°C. The result of DAD 1 amplification for RT–PCR control is shown in (B).

V01512) and JUN (GenBank accession No.: U65928), which interact with each other to form a transcription regulatory protein AP1, are found, surprisingly, to be underexpressed in ovarian cancers. This observation has also been reported in breast (Smith et al., 1997) and

colon cancers (http://welchlink.welch.jhu.edu/~ molgen-g/NC-CT/NC-CT.htm) recently. In addition, many of these putative ovarian cancer markers such as HE4 protease inhibitor (GenBank accession No.: X63187) ( Kirchhoff et al., 1991), M2 type pyruvate

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kinase, and mesothelin have also been shown to be overexpressed in ovarian cancer through a large-scale EST sequencing-based cancer genome anatomy project (http://inhouse.ncbi.nlm.nih.gov/ncicgap/). These observations suggest that the results from microarray hybridization are consistent with other approaches. 3.4. Semiquantative RT–PCR was used to verify the array hybridization data A semiquantative RT–PCR based approach was adapted to verify the array hybridization data, since it allowed multiple putative markers to be screened through a panel of tumor and normal tissue RNA specimens in a short period of time. The RT–PCR panel employed consisted of cDNA samples from several normal tissues and 19 different ovarian cancer specimens. To analyze the expression of a putative marker, an aliquot of the PCR reaction was withdrawn from reaction tubes every 5 cycles after 20 cycles of amplification and analyzed by gel electrophoresis ( Fig. 2). We have analyzed 15 different putative cancer markers; the results of RT–PCR analysis from all cases coincide with the expression profiles obtained through microarray hybridization experiments (data not shown). A previously described ovarian cancer marker, mesothelin (Chang and Pastan, 1996), was also obtained through microarray hybridization. The RT–PCR result for mesothelin is illustrated in Fig. 2. A previous study suggests that the mesothelin expression is not observed in either mucinous or clear cell ovarian cancers (Chang and Pastan, 1996). RT–PCR results with an additional three clear cell tumors and one mucinous cancer also confirm that observation. It is interesting to find that not all the papillary serous and endometric ovarian cancer express mesothelin. This may be the result of different pathological stages of tumor specimens or, more likely, the heterogenic nature of cancer tissues (Ovarian Cancer: Screening, Treatment and Followup. NIH Consensus Statement, 1994). The overexpression of mesothelin with RT–PCR in ovarian cancers is further confirmed with Northern analysis ( Fig. 2C ). The protease inhibitor HE4 was originally identified as an epididymis–epithelium specific gene ( Kirchhoff et al., 1991). Our array hybridizations and RT–PCR results indicate that HE4 is highly expressed in all the ovarian cancer specimens and also in normal kidney, lung, prostate and testis (data not shown). The overexpression of HE4 protease inhibitor in ovarian cancer specimens suggests that the secreted HE4 protein may be used as a molecular marker for ovarian cancers. PUMP 1, a member of the large matrix metalloproteinase (MMP) gene family (Marti et al., 1992), has been suggested to be involved in the metastatic process of cancer cells. RT–PCR results indicate that some of the ovarian cancer specimens exhibit a very high level

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of PUMP 1 expression (data not shown). However, the relationship between PUMP 1 expression level and the invasiveness of ovarian cancer is yet to be determined. In this report, we have demonstrated the utility of cDNA microarrays in analyzing the molecular changes in a complex biological system. The data obtained by this approach are comparable to those of other methods, and the approach has the additional attribute of analyzing multiple tumor samples in a rapid fashion. In the future, the results from microarray hybridizations may be used for tumor classification or to delineate the progression of cancers based upon its gene expression patterns. Most importantly, the hybridization results may be used to identify new molecular pathways or areas that may be involved in the tumorgenesis. To identify, monitor and understand the molecular changes in tumor progression will clearly lead to the identification of new targets for tumor diagnosis and intervention in the future.

Acknowledgements We are grateful to the sequencing and computing groups in Chiroscience for their help on sequencing and identification of some of the cDNA clones. We also thank Dr Tod Bedilion at Synteni Corp. for his help on microarray hybridization experiments.

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