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Molecular Biology and Genetics |
Departments of Pediatrics [D. S. R., D. E. M., R. K., D. M. K., S. M. H.], Surgery [M. P. B.], Pathology [M. B.], Human Genetics [D. M. K.], and The Comprehensive Cancer Center [M. B., J. T., S. M. H.], University of Michigan Medical School, Ann Arbor, Michigan 48109
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ABSTRACT |
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 Top ABSTRACT INTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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6800 genes that were analyzed, a set of 360 genes provided a molecular signature that distinguished between GBMs and pilocytic astrocytomas. Many transcripts that were increased in GBM were not previously associated with gliomas and were found to encode proteins with properties that suggest their involvement in cell proliferation or cell migration. Microarray-based data for a subset of genes was validated using real-time quantitative reverse transcription-PCR. Immunohistochemical analysis also localized the protein products of specific genes of interest to the neoplastic cells of high-grade astrocytomas. Our study has identified a large number of novel genes with distinct expression patterns in high-grade and low-grade gliomas. |
INTRODUCTION |
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TopABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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Molecular expression profiles using oligonucleotide or cDNA-based microarrays have been used to derive a molecular-based classification of several cancer types including B-cell lymphomas, malignant melanomas, and breast cancer (4, 5, 6)
. To date, few studies have reported the expression profile of astrocytic tumors. In studies of the expression profile of 588 known genes in a set gliomas, IGFBP2 gene was found to be overexpressed in GBMs (7
, 8) . More recently, a study analyzing the expression of 1176 cancer-associated genes in 11 grade II astrocytomas uncovered six genes, including TIMP3, EGFR, and GDNPF, that were expressed in 64–100% of grade II tumors and were not expressed in three non-tumor tissue samples derived from three different brain regions (9)
. It was also reported that seven genes, including PDGFR-
, PTN, LRP, and SPARC, were up-regulated by at least 2-fold in 20–60% of the grade II tumors compared with the non-tumor brain tissue samples.
In this study, we used oligonucleotide microarrays to compare the expression pattern of 6800 genes between grade IV and grade I astrocytic tumors. We have identified a group of 360 genes that distinguish grade IV from grade I astrocytomas and have found clusters of tumors that conform to their clinicopathological classification as well as outlier tumors that clustered with tumors of a different grade.
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MATERIALS AND METHODS |
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TopABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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Microarray Analysis.
Total RNA was isolated using Trizol reagent (Life Technologies, Inc.) from 51 tissue samples, followed by clean-up on a RNeasy spin column (Qiagen), and 15 µg was used to generate cRNA probes. A mixture of control cRNAs (made from the bacterial genes BioB, BioC, and BioD and the phage gene cre, purchased from Affymetrix) were used for quality control. Aliquots of each sample (10 µg of fragmented cRNA in 200 µl of hybridization cocktail) were hybridized to HuGeneFL arrays at 45°C for 16 h in a rotisserie oven set at 60 rpm. The arrays were then washed with nonstringent wash buffer (6x SSPE) at 25°C, followed by stringent wash buffer [100 mM MES (pH 6.7), 0.1 M NaCl, and 0.01% Tween 20] at 50°C, stained with streptavidin phycoerythrin (Molecular Probes), washed again with 6x SSPE, stained with biotinylated anti-streptavidin IgG, followed by a second staining with streptavidin phycoerythrin and a third wash with 6x SSPE.
The chips were scanned using the GeneArray scanner (Affymetrix) at 3-µm resolution. Each Hu6800 chip consisted of 287,296 24-x-24-um features that were 25 base-long single-stranded DNA. For each feature, the Affymetrix (GeneChip 4.0) software ignored a 1-pixel border and the 75th percentile of the remaining pixels stored in file (CEL files). For each transcript or probe-set, there were typically 20 pairs of features (probe-pairs) on the arrays, 20 of which were designed to be complementary to a specific sequence (PM features), and the other 20 being identical to the PM features except for a substitution of a base centrally located (MM features). We have developed software to read these files for further processing of the data. For standardization, a reference array was used that was selected based on having a relatively average range of intensities. Probe-pairs for which either the PM or MM feature were saturated (pure white = large pixel values) in the image of the reference array, or for which PM-MM was 
1000 were excluded from further consideration. The latter cases usually gave highly negative PM-MM values on all chips. Saturated features (with measures >98% of the saturated value) on other chips were imputed separately for PM or MM values. For a saturated PM value, the ratios of nonsaturating PM values for the chip divided by the standard were averaged for a probe-set by taking the antilogarithm of the mean of the log ratios. This factor was multiplied by the PM values of the standard to impute values for the chip under consideration. A one-sided Wilcoxon signed-rank test was performed on the PM-MM differences to determine whether the transcript represented by the probe-set was present or absent. The average intensity for each probe-set was computed as the mean of the PM-MM differences, after trimming away the 25% highest and lowest differences.
To normalize the data, we first removed a set of 64 probe-sets that were used for quality control and one additional probe-set that consistently yielded high negative values. The distribution of the remaining average intensities for each chip was normalized to the median value of all of the chips by setting 99 evenly spaced quantiles to have the same values as the median, and using linear interpolation between these points for the remaining probe-sets (software generated and kindly supplied by Dr. Kerby Shedden, University of Michigan). To compute fold changes between the different tissue-type groups, we first replaced all values less than 100 with 100 to reduce spuriously large (or negative)-fold change indices. We then used the ratio of the mean normalized intensity as the fold difference between any two groups. To determine the significant differences between the mean normalized intensities, we first added 100 to each normalized intensity and replaced all of the remaining negative values with 0. We then added 10 to each of the new values and performed a logarithmic (log10) transformation of the data to stabilize the variance and to minimize the effect of negative normalized intensities. We then fit one-way ANOVA models with separate means for each of the four groups. We performed six F tests testing each of the hypotheses that the means of the trimmed average intensities in two of the four groups were identical. Complete hierarchical clustering of the genes and individual samples was done using the Cluster program4 to arrive at a Pearson correlation coefficient (10) . Normalized intensities were further normalized using the Cluster program resulting in a mean intensity value of 0 and SD of 1.
RT-PCR.
For the 20 tumor samples (10 grade I and 10 grade IV), we relied on the TaqMan assay (Perkin-Elmer model 7700; Foster City, CA) to quantitate the amount of FOXG1B, FOXM1, SDC1, FLN1, and ZYX mRNA. To decrease background from genomic DNA, we designed the reverse primer to be complementary to the polyadenylic tail including 3–10 nucleotides 3' to the polyT region described previously (11
, 12)
. The forward and reverse primers and fluorescently tagged probe used for the FOXG1B gene in the assay were 5'-TGTTGTCTTAAAATTTCTTGATTGTGATAC, 5'-TTTTTTTTTTTTTTTTTTTTTTTTTATATGAA, and 5'-6FAM-TGGTCATATGCCCGTGTTTGTCACTTACA-TAMRA, respectively. The forward and reverse primers and fluorescently tagged probe used for the FOXM1 gene in the assay were 5'-TGCCCCTCAGCTTTGCA, 5'-TTTTTTTTTTTTTTTTTGTCCACCTT, and 5'-6FAM-CTGGCTCACACCCATGCGGTCA-TAMRA, respectively. The forward and reverse primers and fluorescently tagged probe used for the SDC1 gene in the assay were 5'-CTGGGCTGGAATCAGGAATATT, 5'-TTTTTTTTTTTTTTTTTTTTTTTAGAAGAAATA, and 5'-6FAM-TTGCCAAAAGCAAAAGACTATCACTCTTTGGA-TAMRA, respectively. The forward and reverse primers and fluorescently tagged probe used for the FLN1 gene in the assay were 5'-GGACCGCGAGAGCATCAA, 5'-GGAGATGGAGTAGTGCAGGATCA, and 5'-6FAM-CTGGTGTCCATCGACAGCAAGGCC-TAMRA, respectively. The forward and reverse primers and fluorescently tagged probe used for the ZYX gene in the assay were 5'-GGGAGACCCTCCAGGACATT, 5'-TTTTTTTTTTTTTTTTTTTTTCTGGAAA, and 5'-6FAM-CCCATGCTGCCAAGTTGTAGCTATAGCTACAA-TAMRA, respectively. The forward and reverse primers and fluorescently tagged probe used for the ß-actin (ACTB) gene were 5'-AACTTGAGATGTATGAAGGCTTTTGG, 5'-TTTTTTTTTTTTTTTTTTTTTTTTTTTTTAAG, and 5'-6FAM-CAACTGGTCTCAAGTCAGTGTACAGGTAAGCCCT-TAMRA, respectively. To measure the relative abundance of two genes in a given RNA sample, the amplification value derived using the first sequence (FOXG1B, FOXM1, SDC1, FLN1 or ZYX) was divided by the amplification value using the second sequence (ACTB). Derivation of this fraction is independent of RNA sample concentration, eliminating the requirement to measure RNA concentration accurately. To compare the measured expression data derived from the Taqman assay and the microarray analysis, we scaled each data set by dividing by the mean amplification ratios and the intensity values for a given gene, respectively.
Immunostaining.
Paraffin-embedded serial sections of either grade IV or grade I tumors analyzed by microarray analysis and TaqMan, were stained with H&E, with a mouse monoclonal antibody against the human filamin protein (1:25 dilution of anti-ABP200; Transduction Laboratories, Lexington, KY), or with a goat polyclonal antibody against the human syndecan-1 protein (1:100 dilution; Santa Cruz Biotechnology, Santa Cruz, CA). Sections stained for filamin were pretreated with a 10 mM citrate buffer for 10 min to enhance antigen retrieval. Sections stained for syndecan-1 were blocked with 0.1% avidin for 10 min and then with 0.01% biotin for 10 min before the addition of the anti-syndecan-1 antibody. We used a Ventana ES automated immunostainer (Ventana Medical Systems, Tucson, AZ.) and LSAB+ peroxidase-based visualization (DAKO, Carpinteria, CA).
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RESULTS |
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TopABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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6800 genes using Affymetrix Hu6800 GeneChip microarrays for 45 astrocytic tumors, consisting of 19 grade I, 5 grade II, and 21 grade IV tumors, and for 6 normal tissue samples obtained from the neocortex of the temporal lobe from 6 patients who underwent surgery for epilepsy. The design of these oligonucleotide-arrays has been previously described (13)
. A one-sided Wilcoxon signed-rank test of the individual oligonucleotide features for each probe set was used to determine whether a transcript was either present or absent in a given sample. We determined that from the entire set of genes represented on the Hu6800 chip, 4648 genes had measurable intensity values in at least 2 of the 51 samples analyzed (P < 0.01). We compared the mean intensity values for each gene in one sample type to the mean intensity values obtained from other sample types to determine fold difference in the measured expression levels. Table 1
summarizes the number of gene differences between the comparison groups. Of the 4648 genes, 360 genes differed in their expression between grade IV and grade I tumors by at least 1.5-fold in mean intensity (P < 0.01). Of the 360 genes, 167 had increased and 193 had decreased measured expression levels in grade IV tumors relative to grade I tumors. A total of 183 genes (including 47 of the 167 genes) were expressed at a higher level in grade IV relative to 5 grade II astrocytomas. In addition, 703 genes, including 86 of the set of 167 genes, were expressed at a higher level in grade IV tumors compared with normal brain tissue samples.
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. Most genes that were overexpressed in grade IV relative to grade I were also overexpressed relative to grade II and normal brain. Likewise, most genes that were overexpressed in grade I relative to grade IV were overexpressed relative to grade II and normal brain. A subset of genes were reduced in their expression in grade IV relative to all other. Dendrograms based on the expression patterns of the 360 genes and of the entire set of 4648 genes are shown in Fig. 1, B and C
, respectively. On the basis of the set of 360 genes, samples derived from grade IV tumors (coded in red) clustered independently from samples derived from grade I (coded in blue). A similar distinction between the two sample types was observed based on the expression pattern of the entire gene set. In both dendrograms, two samples derived from tumors that were diagnosed as either grade IV or grade I clustered with samples derived from tumors of differing grades.
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shows a representation of the measured expression level of EGFR, MDM2, CDK4, CD44, and IGFBP2 in each sample. The color of each cell represents the number of SDs above (red) or below (green) the mean of the log-transformed normalized intensity values obtained for that gene in all of the samples. Consistent with previous reports, we observed an increased expression of these genes predominantly in GBMs.
Tables 2
and 3
list a subset of the genes in the gene clusters shown in Fig. 1A
, including the ratio and P of the mean intensity values that distinguished grade IV astrocytoma from grade I pilocytic astrocytoma. A large subset of the 360 genes was functionally categorized, based on known or inferred biological function of their protein product. Many of the genes that were overexpressed in grade IV tumors encode proteins that are involved in cell proliferation or enhanced cell migration. These include DNA repair/replication/maintenance proteins, transcription and translation regulators, cell cycle-mediating proteins, ECMs or ECM-associated proteins, cytoskeleton and cytoskeletal organizing proteins, and cell adhesion molecules.
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. Remarkably, the pattern of gene expression from sample to sample observed by microarrays paralleled the pattern observed using Taqman (Fig. 2B)
. The minimum and maximum correlation coefficients (r) between grade IV and grade I samples are 0.62 (FLN1) and 0.92 (FOXM1), respectively.
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. We also observed a high level of syndecan-1, the gene product of SDC1, in the neoplastic cells of grade IV tumors. Syndecan-1 is a cell-surface, heparan-sulfate proteoglycan predominately expressed in epithelial cells (18)
. It functions as a coreceptor for soluble (e.g., FGF2) and insoluble (e.g., ECM components) ligands, as a soluble paracrine effector, and as a mediator of cell-surface protein internalization (18)
. No detectable staining for syndecan-1 was observed in grade I tumors.
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DISCUSSION |
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TopABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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. Some of the genes involved, first identified in yeast, are members of a highly conserved family of genes encoding minichromosome maintenance proteins that form a prereplicative complex that binds to the origin of replication sites (19)
. These proteins have previously been correlated with cellular dysplasia and tumor malignancy of several types of cancers but, to date, have not been associated with gliomas (20)
. Some members of the winged-helix family of transcription family were overexpressed in GBMs. FOXG1B and FOXM1 encode for the proteins fork head box G1B and fork head box M1, respectively. FoxG1B, also known as brain factor-1, was originally isolated from a fibrosarcoma-inducing virus and is the putative oncogene of the avian sarcoma virus 31 (21) . It has restricted expression in neurons of the developing brain and interacts with DNA-binding proteins that interfere with the transcriptional activation by Smad proteins. This interaction inactivates the transforming growth factor ß/activin-regulated cell cycle arrest and growth inhibition (22 , 23) . FoxM1 exhibits a wider expression pattern in the embryo than does FoxG1B and has been implicated in the induction of hyperproliferation (24) . We also observed increased expression in GBMs of genes that inhibit apoptosis. For example, we detected increased survivin expression in GBMs relative to normal brain or to grade I or grade II tumors. Survivin has been found to be overexpressed in other cancers. It maintains the proliferative state of cells by counteracting a default induction of apoptosis in G2-M phase and favoring aberrant progression of transformed cells through mitosis (25) .
Hyperproliferation is a unifying feature of high-grade gliomas and is accompanied by a high degree of invasion into the surrounding brain parenchyma by neoplastic cells. We observed increased expression in GBMs of genes encoding for proteins likely involved in cell migration. Apart from cell adhesion molecules, we found several cytoskeleton and cytoskeletal-associated proteins that were highly expressed in grade IV tumors. Proteins that promote both cross-linking and severing of actin networks are necessary for efficient cell locomotion (17) . Actin-binding protein-280 (filamin) and members of the gelsolin family of proteins (e.g., capping protein and villin) function in these capacities, respectively. We also found an increase in the level of ZYX mRNA in GBM relative to low-grade tumors. ZYX encodes for the protein zyxin, which is also involved in the assembly of actin filaments during cell migration (26) . More recently, it has been shown that zyxin shuttles between the nucleus and sites of cell adhesion. In the nucleus, it binds to and inactivates the tumor suppressor protein h-warts/LATS1 (27) . Whether zyxin functions in GBMs to promote cell migration, inhibit h-warts/LATS1, or both, is unknown.
Compared with GBMs, genes that encode proteins involved in cell motility were expressed at a reduced level in grade I tumors. Moreover, genes involved in suppressing migration were expressed at higher levels in grade I relative to grade IV tumors (e.g., SIAT8A: ratio, 1.7; P < 0.001; TIMP3: ratio, 1.6; P < 0.001; and TIMP4: ratio, 2.3; P <0.001). Polysialylation of NCAM is achieved by 2,8-polysialyltransferases (encoded by SIAT8A), which inhibit NCAM-mediated cell adhesion and migration (28
, 29)
. Members of the family of TIMPs are also known to inhibit cell migration by inhibiting proteolysis of ECM and ECM receptors on the surface of the cell. TIMP4 has been shown to inhibit the invasive potential of human breast cancer cells (30)
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Microarray analysis and Taqman assay yielded concordant results for differences in gene expression between grade IV and grade I tumors for the genes tested, which consisted of ZYX, SDC1, FLN1, FOXG1B, and FOXM1. In general, changes in transcript abundance were underestimated by microarray analysis (Table 1)
. However, for each gene across 20 samples, we obtained a high correlation between the two assays. For two of the five genes that we investigated by immunochemistry, we further demonstrated that their protein products were overexpressed in tumor cells of GBMs relative to pilocytic astrocytomas. These two proteins were chosen from the five based on the availability of commercial antibodies for immunohistochemical assays. Their abundant expression in the tumor cells of GBMs suggests that they may play a role in the infiltration of these cells into the surrounding normal brain.
One grade I tumor (no. 265) clustered with grade IV tumors based on the set of genes that displayed a significant difference in their expression pattern between the two groups. This tumor exhibited increased FLN1 mRNA, as measured by both microarray analysis and TaqMan, as well as increased FLN1 protein, as measured by immunohistochemistry, compared with other pilocytic astrocytomas. This tumor also displayed several cellular features associated with high-grade tumors, including hypercellularity, an increase in nuclear pleomorphism and a high nuclear:cytoplasm ratio. The existence of a "diffuse" variant and subsequent malignant transformation of pilocytic astrocytomas has been proposed as an explanation for the aberrant grouping of this particular tumor (31) . However, more studies are needed to determine the pathological significance of low-grade tumors that cluster with GBMs.
Our study has uncovered a large number of genes with distinctive expression patterns in grade IV and grade I tumors. A substantial number of these genes have not been previously associated with gliomas. The study provides a molecular profile for astrocytic neoplasms which may be relevant to diagnosis and therapy.
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ACKNOWLEDGMENTS |
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FOOTNOTES |
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1 Supported in part by NIH Grant CA84953 from the National Cancer Institute. 
2 To whom requests for reprints should be addressed, at Department of Pediatrics, University of Michigan Medical Center, 1150 West Medical Center Drive, A520 Medical Science Research Building I, Ann Arbor, MI 48109-0656. Phone: (734) 763-9311; Fax: (734) 647-8148; E-mail: shanash{at}umich.edu
3 The abbreviations used are: GBM, glioblastoma multiforme; IGFBP2, insulin-like growth factor binding protein 2; PM, perfect match; MM, mismatch; ECM, extracellular matrix; RT-PCR, reverse transcription-PCR; TIMP, tissue inhibitor of metalloproteinase(s).
4 Internet address: http://rana.stanford.edu/software.
Received 3/19/01. Accepted 7/17/01.
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S.-H. Hong, D. E. Misek, H. Wang, E. Puravs, T. J. Giordano, J. K. Greenson, D. E. Brenner, D. M. Simeone, C. D. Logsdon, and S. M. Hanash An Autoantibody-Mediated Immune Response to Calreticulin Isoforms in Pancreatic Cancer Cancer Res., August 1, 2004; 64(15): 5504 - 5510. [Abstract] [Full Text] [PDF]
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 K. E. Strunk, V. Amann, and D. W. Threadgill Phenotypic Variation Resulting From a Deficiency of Epidermal Growth Factor Receptor in Mice Is Caused by Extensive Genetic Heterogeneity That Can Be Genetically and Molecularly Partitioned Genetics, August 1, 2004; 167(4): 1821 - 1832. [Abstract] [Full Text] [PDF]
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D. R. Rhodes, J. Yu, K. Shanker, N. Deshpande, R. Varambally, D. Ghosh, T. Barrette, A. Pandey, and A. M. Chinnaiyan Large-scale meta-analysis of cancer microarray data identifies common transcriptional profiles of neoplastic transformation and progression PNAS, June 22, 2004; 101(25): 9309 - 9314. [Abstract] [Full Text] [PDF]
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 H. K. Lee, A. K. Hsu, J. Sajdak, J. Qin, and P. Pavlidis Coexpression Analysis of Human Genes Across Many Microarray Data Sets Genome Res., June 1, 2004; 14(6): 1085 - 1094. [Abstract] [Full Text] [PDF]
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 L F Callado, J I Martin-Gomez, J Ruiz, J M Garibi, and J J Meana Imidazoline I2 receptor density increases with the malignancy of human gliomas J. Neurol. Neurosurg. Psychiatry, May 1, 2004; 75(5): 785 - 787. [Abstract] [Full Text] [PDF]
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Y. Iwadate, T. Sakaida, T. Hiwasa, Y. Nagai, H. Ishikura, M. Takiguchi, and A. Yamaura Molecular Classification and Survival Prediction in Human Gliomas Based on Proteome Analysis Cancer Res., April 1, 2004; 64(7): 2496 - 2501. [Abstract] [Full Text] [PDF]
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D. Xie, D. Yin, X. Tong, J. O'Kelly, A. Mori, C. Miller, K. Black, D. Gui, J. W. Said, and H. P. Koeffler Cyr61 Is Overexpressed in Gliomas and Involved in Integrin-Linked Kinase-Mediated Akt and {beta}-Catenin-TCF/Lef Signaling Pathways Cancer Res., March 15, 2004; 64(6): 1987 - 1996. [Abstract] [Full Text] [PDF]
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D. Xie, D. Yin, H.-J. Wang, G.-T. Liu, R. Elashoff, K. Black, and H. P. Koeffler Levels of Expression of CYR61 and CTGF Are Prognostic for Tumor Progression and Survival of Individuals with Gliomas Clin. Cancer Res., March 15, 2004; 10(6): 2072 - 2081. [Abstract] [Full Text] [PDF]
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 C. E. Schmalbach, D. B. Chepeha, T. J. Giordano, M. A. Rubin, T. N. Teknos, C. R. Bradford, G. T. Wolf, R. Kuick, D. E. Misek, D. K. Trask, et al. Molecular Profiling and the Identification of Genes Associated With Metastatic Oral Cavity/Pharynx Squamous Cell Carcinoma Arch Otolaryngol Head Neck Surg, March 1, 2004; 130(3): 295 - 302. [Abstract] [Full Text] [PDF]
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 C. D. Katsetos, A. Legido, E. Perentes, and S. J. Mork Class III {beta}-Tubulin Isotype: A Key Cytoskeletal Protein at the Crossroads of Developmental Neurobiology and Tumor Neuropathology J Child Neurol, December 1, 2003; 18(12): 851 - 866. [Abstract] [PDF]
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 A. Korshunov, K. Neben, G. Wrobel, B. Tews, A. Benner, M. Hahn, A. Golanov, and P. Lichter Gene Expression Patterns in Ependymomas Correlate with Tumor Location, Grade, and Patient Age Am. J. Pathol., November 1, 2003; 163(5): 1721 - 1727. [Abstract] [Full Text] [PDF]
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K. A. Shedden, J. M. G. Taylor, T. J. Giordano, R. Kuick, D. E. Misek, G. Rennert, D. R. Schwartz, S. B. Gruber, C. Logsdon, D. Simeone, et al. Accurate Molecular Classification of Human Cancers Based on Gene Expression Using a Simple Classifier with a Pathological Tree-Based Framework Am. J. Pathol., November 1, 2003; 163(5): 1985 - 1995. [Abstract] [Full Text] [PDF]
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S. Godard, G. Getz, M. Delorenzi, P. Farmer, H. Kobayashi, I. Desbaillets, M. Nozaki, A.-C. Diserens, M.-F. Hamou, P.-Y. Dietrich, et al. Classification of Human Astrocytic Gliomas on the Basis of Gene Expression: A Correlated Group of Genes with Angiogenic Activity Emerges As a Strong Predictor of Subtypes Cancer Res., October 15, 2003; 63(20): 6613 - 6625. [Abstract] [Full Text] [PDF]
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J. van den Boom, M. Wolter, R. Kuick, D. E. Misek, A. S. Youkilis, D. S. Wechsler, C. Sommer, G. Reifenberger, and S. M. Hanash Characterization of Gene Expression Profiles Associated with Glioma Progression Using Oligonucleotide-Based Microarray Analysis and Real-Time Reverse Transcription-Polymerase Chain Reaction Am. J. Pathol., September 1, 2003; 163(3): 1033 - 1043. [Abstract] [Full Text] [PDF]
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 S. Taylor, S. Smith, B. Windle, and A. Guiseppi-Elie Impact of surface chemistry and blocking strategies on DNA microarrays Nucleic Acids Res., August 15, 2003; 31(16): e87 - e87. [Abstract] [Full Text] [PDF]
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 M. Ho, E. Yang, G. Matcuk, D. Deng, N. Sampas, A. Tsalenko, R. Tabibiazar, Y. Zhang, M. Chen, S. Talbi, et al. Identification of endothelial cell genes by combined database mining and microarray analysis Physiol Genomics, May 13, 2003; 13(3): 249 - 262. [Abstract] [Full Text] [PDF]
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 S. J. Miller, H. Li, T. A. Rizvi, Y. Huang, G. Johansson, J. Bowersock, A. Sidani, J. Vitullo, K. Vogel, L. M. Parysek, et al. Brain Lipid Binding Protein in Axon-Schwann Cell Interactions and Peripheral Nerve Tumorigenesis Mol. Cell. Biol., March 15, 2003; 23(6): 2213 - 2224. [Abstract] [Full Text] [PDF]
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T. J. Giordano, D. G. Thomas, R. Kuick, M. Lizyness, D. E. Misek, A. L. Smith, D. Sanders, R. T. Aljundi, P. G. Gauger, N. W. Thompson, et al. Distinct Transcriptional Profiles of Adrenocortical Tumors Uncovered by DNA Microarray Analysis Am. J. Pathol., February 1, 2003; 162(2): 521 - 531. [Abstract] [Full Text] [PDF]
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 A. Tefferi, M. E. Bolander, S. M. Ansell, E. D. Wieben, and T. C. Spelsberg Primer on Medical Genomics Part III: Microarray Experiments and Data Analysis Mayo Clin. Proc., September 1, 2002; 77(9): 927 - 940. [Abstract] [PDF]
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S. Ramaswamy and T. R. Golub DNA Microarrays in Clinical Oncology J. Clin. Oncol., April 1, 2002; 20(7): 1932 - 1941. [Abstract] [Full Text] [PDF]
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