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PubMed2006

Global gene expression profiling of pleural mesotheliomas: overexpression of aurora kinases and P16/CDKN2A deletion as prognostic factors and critical evaluation of microarray-based prognostic prediction.

Most gene expression profiling studies of mesothelioma have been based on relatively small sample numbers, limiting their statistical power. We did Affymetrix U133A microarray analysis on 99 pleural mesotheliomas, in which multivariate analysis showed advanced-stage, sarcomatous histology and P16/CDKN2A homozygous deletion to be significant independent adverse prognostic factors. Comparison of the expression profiles of epithelioid versus sarcomatous mesotheliomas identified many genes significantly overexpressed among the former, including previously unrecognized ones, such as uroplakins and kallikrein 11, both confirmed by immunohistochemistry. Examination of the gene expression correlates of survival showed that more aggressive mesotheliomas expressed higher levels of Aurora kinases A and B and functionally related genes involved in mitosis and cell cycle control. Independent confirmation of the negative effect of Aurora kinase B was obtained by immunohistochemistry in a separate patient cohort. A role for Aurora kinases in the aggressive behavior of mesotheliomas is of potential clinical interest because of the recent development of small-molecule inhibitors. We then used our data to develop microarray-based predictors of 1 year survival; these achieved a maximal accuracy of 68% in cross-validation. However, this was inferior to prognostic prediction based on standard clinicopathologic variables and P16/CDNK2A status (accuracy, 73%), and adding the microarray model to the latter did not improve overall accuracy. Finally, we evaluated three recently published microarray-based outcome prediction models, but their accuracies ranged from 63% to 67%, consistently lower than reported. Gene expression profiling of mesotheliomas is an important discovery tool, but its power in clinical prognostication has been overestimated.

Adult↗

PubMed2006

Single cell gene expression profiling in Alzheimer's disease.

Development and implementation of microarray techniques to quantify expression levels of dozens to hundreds to thousands of transcripts simultaneously within select tissue samples from normal control subjects and neurodegenerative diseased brains has enabled scientists to create molecular fingerprints of vulnerable neuronal populations in Alzheimer's disease (AD) and related disorders. A goal is to sample gene expression from homogeneous cell types within a defined region without potential contamination by expression profiles of adjacent neuronal subpopulations and nonneuronal cells. The precise resolution afforded by single cell and population cell RNA analysis in combination with microarrays and real-time quantitative polymerase chain reaction (qPCR)-based analyses allows for relative gene expression level comparisons across cell types under different experimental conditions and disease progression. The ability to analyze single cells is an important distinction from global and regional assessments of mRNA expression and can be applied to optimally prepared tissues from animal models of neurodegeneration as well as postmortem human brain tissues. Gene expression analysis in postmortem AD brain regions including the hippocampal formation and neocortex reveals selectively vulnerable cell types share putative pathogenetic alterations in common classes of transcripts, for example, markers of glutamatergic neurotransmission, synaptic-related markers, protein phosphatases and kinases, and neurotrophins/neurotrophin receptors. Expression profiles of vulnerable regions and neurons may reveal important clues toward the understanding of the molecular pathogenesis of various neurological diseases and aid in identifying rational targets toward pharmacotherapeutic interventions for progressive, late-onset neurodegenerative disorders such as mild cognitive impairment (MCI) and AD.

Alzheimer Disease↗

PubMed2004

Algorithmic fusion of gene expression profiling for diffuse large B-cell lymphoma outcome prediction.

Many different methods and techniques have been investigated for the processing and analysis of microarray gene expression profiling datasets. It is noted that the accuracy and reliability of the results are often dependent on the measurement approaches applied, and no single measurement so far is guaranteed to generate a satisfactory result. In this paper, an algorithmic fusion approach is presented for extracting genes that are predictive to clinical outcomes (survival-fatal) of diffuse large B-cell lymphoma on a set of microarray data for gene expression profiling. The approach integrates a set of measurements from different aspects in terms of the discrepancy indications and merit expectations of the gene expression patterns with respect to the clinical outcomes. A combination of statistical and non-statistical criteria, continuous and discrete parameterizations, as well as model-based and modeless evaluations is applied in the approach. By integrating these measurements, a set of genes that are indicative to the clinical outcomes are better captured from the gene expression profiling dataset.

Algorithms↗

PubMed2003

Gene-expression profiles of human tumor xenografts in nude mice treated orally with the EGFR tyrosine kinase inhibitor ZD1839.

To date, no single or multiple molecular markers have been successful in predicting sensitivity of individual patients to anti-cancer drugs. As the nature of a specific cancer is considered to be defined by the proteins being expressed in the tumor cells, systematic analysis of gene-expression profiles may provide information reflecting sensitivity of a given tumor to certain drugs. Recent progress in genome technology has enabled us to examine expression profiles of thousands of genes in a single experiment. We used this approach to examine 13 xenografts of human tumors implanted into nude mice for sensitivity to an orally active, selective epidermal growth factor receptor tyrosine kinase inhibitor (EGFR-TKI), ZD1839 (Iressa). To identify genes that might be associated with sensitivity to this drug we used a cDNA microarray representing 23,040 genes to analyze expression profiles of the 13 xenografts and identified 114 genes whose expression levels correlated significantly with sensitivity of the tumors to ZD1839. We then investigated alteration of expression profiles in response to the ZD1839 treatment in four non-small cell lung cancer (NSCLC) xenografts, of which two (LC6 and LC11) were sensitive and the other two (Lu116 and L27) were resistant to this EGFR-TKI. Systematic analysis of expression at various time points during oral treatment for 14 days, compared with corresponding untreated samples, identified a set of genes whose expression levels changed in the two sensitive tumors but not in the two resistant tumors. The data obtained here should provide useful information on the molecular mechanism underlying clinical responses to EGFR-TKIs, aid the development of novel therapies for lung cancer, and potentially identify predictive molecular markers for sensitivity to ZD1839.

Administration, Oral↗

PubMed2004

Expression profiling of phyB mutant demonstrates substantial contribution of other phytochromes to red-light-regulated gene expression during seedling de-etiolation.

Different Arabidopsis phytochrome (phy) family members (phyA through phyE) display differential photosensory and/or physiological functions in regulating growth and developmental responses to light signals. To identify the genes regulated by phyB in response to continuous monochromatic red light (Rc) during the induction of seedling de-etiolation, we have performed time-course, microarray-based expression profiling of wild type (WT) and phyB null mutants. Comparison of the observed expression patterns with those induced by continuous monochromatic far-red light (FRc; perceived exclusively by phyA) in WT and phyA null-mutant seedlings suggests early convergence of the FRc and Rc photosensory pathways to control a largely common transcriptional network. phyB mutant seedlings retain a surprisingly high level of responsiveness to Rc for the majority of Rc-regulated genes on the microarray, indicating that one or more other phys have a major role in regulating their expression. Combined with the robust visible morphogenic phenotype of the phyB mutant in Rc, these data suggest that different members of the phy family act in organ-specific fashion in regulating seedling de-etiolation. Specifically, phyB appears to be the dominant, if not exclusive, photoreceptor in regulating a minority population of genes involved in suppression of hypocotyl cell elongation in response to Rc signals. By contrast, this sensory function is apparently shared by one or more other phys in regulating the majority Rc-responsive gene set involved in other important facets of the de-etiolation process in the apical region, such as cotyledon cell expansion.

Arabidopsis↗

PubMed2005

Robustness of gene expression profiling in glioma specimen samplings and derived cell lines.

One of the most promising applications of microarrays is class distinction through gene expression profiling as a diagnostic tool. However, as there is apparent spatial heterogeneity in the morphology of cancer cells within a tumor, it is unclear if tumor sampling can be applied and yield consistent signals. In this report, we examined six brain tumors, four glioblastoma, and two oligodendroglioma biopsies. The six brain tumor tissues from two distinct different classes were dissected in four distinct areas and gene expression was profiled using microarrays. We used hierarchical clustering to compare the variability of gene expression profiles between spatially distinct biopsies of the same tumor as compared to the variability between tumors of the same histologic group. We conclude that, in general, repeat spatially distinct samples are not needed for microarray experiments and the gene expression signatures are robust across the tumor. Predominantly, variation was much greater between samples from different patients than from the multiple samplings of given tumor. Further, we compared biopsy expression profiles to the cell lines derived from those tissues. In general, the tumor cell lines vary greatly from the parental tissues and cluster more strongly with each other than the parental tissue. We select and examine the set of genes altered in expression to allow adaptation to cell culture.

Brain Neoplasms↗

PubMed2005

Bioinformatics approaches for cross-species liver cancer analysis based on microarray gene expression profiling.

BACKGROUND: The completion of the sequencing of human, mouse and rat genomes and knowledge of cross-species gene homologies enables studies of differential gene expression in animal models. These types of studies have the potential to greatly enhance our understanding of diseases such as liver cancer in humans. Genes co-expressed across multiple species are most likely to have conserved functions. We have used various bioinformatics approaches to examine microarray expression profiles from liver neoplasms that arise in albumin-SV40 transgenic rats to elucidate genes, chromosome aberrations and pathways that might be associated with human liver cancer. RESULTS: In this study, we first identified 2223 differentially expressed genes by comparing gene expression profiles for two control, two adenoma and two carcinoma samples using an F-test. These genes were subsequently mapped to the rat chromosomes using a novel visualization tool, the Chromosome Plot. Using the same plot, we further mapped the significant genes to orthologous chromosomal locations in human and mouse. Many genes expressed in rat 1q that are amplified in rat liver cancer map to the human chromosomes 10, 11 and 19 and to the mouse chromosomes 7, 17 and 19, which have been implicated in studies of human and mouse liver cancer. Using Comparative Genomics Microarray Analysis (CGMA), we identified regions of potential aberrations in human. Lastly, a pathway analysis was conducted to predict altered human pathways based on statistical analysis and extrapolation from the rat data. All of the identified pathways have been known to be important in the etiology of human liver cancer, including cell cycle control, cell growth and differentiation, apoptosis, transcriptional regulation, and protein metabolism. CONCLUSION: The study demonstrates that the hepatic gene expression profiles from the albumin-SV40 transgenic rat model revealed genes, pathways and chromosome alterations consistent with experimental and clinical research in human liver cancer. The bioinformatics tools presented in this paper are essential for cross species extrapolation and mapping of microarray data, its analysis and interpretation.

Animals↗

PubMed2004

Blood expression profiles for tuberous sclerosis complex 2, neurofibromatosis type 1, and Down's syndrome.

Blood gene expression profiling has been applied to a variety of hematological malignancies, autoimmune disorders, and infectious diseases. This study applies this approach to genetic diseases without obvious blood phenotypes. Three genetic diseases including tuberous sclerosis complex 2, neurofibromatosis type 1, and Down's syndrome were compared with a group of healthy controls. RNA from whole blood was surveyed using Affymetrix U133A arrays. Each disease was associated with a unique gene expression pattern in blood that can be accurately distinguished by a classifier. Genes on chromosome 21 were overexpressed in Down's syndrome, and genes controlling cell cycle and proliferation were associated with tuberous sclerosis complex type 2 or neurofibromatosis type 1. A subset of genes involved in cardiac development or remodeling were overexpressed in patients with Down's syndrome and congenital heart defects. These findings suggest that blood gene expression profiling on a broader basis might be useful for genetic disease screening/diagnosis and might help elucidate mechanisms and pathways that lead to genotype-phenotype differences.

Cluster Analysis↗

PubMed2001

Gene expression profiling of clear cell renal cell carcinoma: gene identification and prognostic classification.

To better understand the molecular mechanisms that underlie the tumorigenesis and progression of clear cell renal cell carcinoma (ccRCC), we studied the gene expression profiles of 29 ccRCC tumors obtained from patients with diverse clinical outcomes by using 21,632 cDNA microarrays. We identified gene expression alterations that were both common to most of the ccRCC studied and unique to clinical subsets. There was a significant distinction in gene expression profile between patients with a relatively nonaggressive form of the disease [100% survival after 5 years with the majority (15/17 or 88%) having no clinical evidence of metastasis] versus patients with a relatively aggressive form of the disease (average survival time 25.4 months with a 0% 5-year survival rate). Approximately 40 genes most accurately make this distinction, some of which have previously been implicated in tumorigenesis and metastasis. To test the robustness and potential clinical usefulness of this molecular distinction, we simulated its use as a prognostic tool in the clinical setting. In 96% of the ccRCC cases tested, the prediction was compatible with the clinical outcome, exceeding the accuracy of prediction by staging. These results suggest that two molecularly distinct forms of ccRCC exist and that the integration of expression profile data with clinical parameters could serve to enhance the diagnosis and prognosis of ccRCC. Moreover, the identified genes provide insight into the molecular mechanisms of aggressive ccRCC and suggest intervention strategies.

Adenocarcinoma, Clear Cell↗

PubMed2005

Gene expression profiling identifies BAX-delta as a novel tumor antigen in acute lymphoblastic leukemia.

The identification of new tumor-associated antigens (TAA) is critical for the development of effective immunotherapeutic strategies, particularly in diseases like B-cell acute lymphoblastic leukemia (B-ALL), where few target epitopes are known. To accelerate the identification of novel TAA in B-ALL, we used a combination of expression profiling and reverse immunology. We compared gene expression profiles of primary B-ALL cells with their normal counterparts, B-cell precursors. Genes differentially expressed by B-ALL cells included many previously identified as TAA in other malignancies. Within this set of overexpressed genes, we focused on those that may be functionally important to the cancer cell. The apoptosis-related molecule, BAX, was highly correlated with the ALL class distinction. Therefore, we evaluated BAX and its isoforms as potential TAA. Peptides from the isoform BAX-delta bound with high affinity to HLA-A*0201 and HLA-DR1. CD8+ CTLs specific for BAX-delta epitopes or their heteroclitic peptides could be expanded from normal donors. BAX-delta-specific T cells lysed peptide-pulsed targets and BAX-delta-expressing leukemia cells in a MHC-restricted fashion. Moreover, primary B-ALL cells were recognized by BAX-delta-specific CTL, indicating that this antigen is naturally processed and presented by tumor cells. This study suggests that (a) BAX-delta may serve as a widely expressed TAA in B-ALL and (b) gene expression profiling can be a generalizable tool to identify immunologic targets for cancer immunotherapy.

Adolescent↗

PubMed2003

Open architecture expression profiling of plant transcriptomes and gene discovery using GeneCalling technology.

The recent rapid developments in genomics tools, technologies, and bioinformatics have revolutionized gene expression analysis. It is now routine to measure gene expression modulation at the genomic level. GeneCalling technology is an open architecture system capable of assaying more than 95% of genes expressed in a tissue. Unlike the closed systems, GeneCalling is not dependent upon an existing sequence or clone database. GeneCalling uses as low as 50 pg of the cDNA from samples and identifies cDNA fragments that are differentially modulated within a set of samples. With the use of 96 pairs of restriction enzymes, more than 30,000 cDNA fragments are routinely assayed to identify those that are differentially modulated. Specific processes, such as SeqCalling, Trace Poisoning, and GeneCall Poisoning, are set up to not only confirm the known genes, but also to clone and analyze unknown and novel genes that have an interesting expression profile. GeneCalling has been successfully applied to expression profiling of several plant and fungal species, and resulted in identification and characterization of genes that are useful in commercial applications towards improving agriculturally important traits in plants.

DNA, Complementary↗

PubMed2003

DNA microarrays for comparison of gene expression profiles between diagnosis and relapse in precursor-B acute lymphoblastic leukemia: choice of technique and purification influence the identification of potential diagnostic markers.

Microarrays for gene expression profiling are rapidly becoming important research tools for the identification of novel markers, for example, for novel classification of leukemias and lymphomas. Here, we review the considerations and infrastructure for microarray experiments. These considerations are illustrated via a microarray-based comparison of gene expression profiles of paired diagnosis-relapse samples from patients with precursor-B acute lymphoblastic leukemia (ALL), who relapsed during therapy or after completion of treatment. Initial experiments showed that several seemingly differentially expressed genes were actually derived from contaminating non-leukemic cells, particularly myeloid cells and T-lymphocytes. Therefore, we purified the ALL cells of the diagnosis and relapse samples if their frequency was lower than 95%. Furthermore, we observed in earlier studies that extra RNA amplification leads to skewing of particular gene transcripts. Sufficient (non-amplified) RNA of purified and paired diagnosis-relapse samples was obtained from only seven cases. The gene expression profiles were evaluated with Affymetrix U95A chips containing 12 600 human genes. These diagnosis-relapse comparisons revealed only a small number of genes (n=6) that differed significantly in expression: mostly signaling molecules and transcription factors involved in cell proliferation and cell survival were highly upregulated at relapse, but we did not observe any increase in drug-resistance markers. This finding fits with the observation that tumors with a high proliferation index have a poor prognosis. The genes that changed between diagnosis and relapse are currently not in use as diagnostic or disease progression markers, but represent potential new markers for such applications. Leukemia (2003) 17, 1324-1332. doi:10.1038/sj.leu.2402974

Biomarkers↗

PubMed2006

Multi-level gene expression profiles affected by thymidylate synthase and 5-fluorouracil in colon cancer.

BACKGROUND: Thymidylate synthase (TS) is a critical target for cancer chemotherapy and is one of the most extensively studied biomarkers for fluoropyrimidine-based chemotherapy. In addition to its critical role in enzyme catalysis, TS functions as an RNA binding protein to regulate the expression of its own mRNA translation and other cellular mRNAs, such as p53, at the translational level. In this study, a comprehensive gene expression analysis at the levels of both transcriptional and post-transcriptional regulation was conducted to identify response markers using human genome array with TS-depleted human colon cancer HCT-C18 (TS-) cells and HCT-C18 (TS+) cells stably transfected with the human TS cDNA expression plasmid. RESULTS: A total of 38 genes were found to be significantly affected by TS based on the expression profiles of steady state mRNA transcripts. However, based on the expression profiles of polysome associated mRNA transcripts, over 149 genes were affected by TS overexpression. This indicates that additional post-transcriptionally controlled genes can be captured with profiling polysome associated mRNA population. This unique approach provides a comprehensive overview of genes affected by TS. Additional novel post-transcriptionally regulated genes affected by 5-fluorouracil (5-FU) treatment were also discovered via similar approach. CONCLUSION: To our knowledge, this is the first time that a comprehensive gene expression profile regulated by TS and 5-FU was analyzed at the multiple steps of gene regulation. This study will provide candidate markers that can be potentially used for predicting therapeutic outcomes for fluoropyrimidine-based cancer chemotherapy.

Antimetabolites, Antineoplastic↗

PubMed2005

Gene expression profiling predicts liver responses to a herbal remedy after partial hepatectomy in mice.

The aim of this study was to demonstrate that regenerating liver responses to a herbal remedy could be presented by gene expression profiling. Compositions of the ingredients in the remedy containing Scutellaria baicalensis Georgi and Bupleurum scorzonerifolfium Wild (S/B remedy) were analyzed and quantified by high performance liquid chromatography. By using a 70% partial hepatectomy in BALB/c mice as an in vivo model, the effects of high dose (50 mg/kg) and low dose (1 mg/kg) S/B remedy were evaluated by cDNA microarray, followed by RT-PCR and real-time PCR confirmation. Factors affecting proliferative activities of mouse hepatocytes were measured by DNA flow cytometry, BrdU incorporation assay and serum interleukin-6 (IL-6) level. Based on global gene expression profiles, the results showed that the low dose S/B remedy down-regulated expression of immediate early genes and cell cycle-related genes, whereas the high dose had opposite effects. The gene expression was further verified by real-time RT-PCR. Proliferative activities, in terms of synthetic phase fractions and G2/M phase fractions, in vehicle, low dose, and high dose groups were 18.45+/-2.56%, 14.65+/-1.06%; 9.27+/-0.85%, 7.80+/-0.11%; and 18.90+/-2.17%, 22.95+/-0.25%, respectively. The serum IL-6 level was also dose-dependent in both low and high dose S/B remedy-treated mice. We conclude that in vivo gene expression profiling correlates with liver responses to a herbal remedy, which provides a new direction for pharmaceutical studies on human diseases.

Animals↗

PubMed2006

Gene expression profiling of mouse embryonic stem cell subpopulations.

We previously demonstrated that mouse embryonic stem (ES) cells show a wide variation in the expression of platelet endothelial cell adhesion molecule 1 (PECAM1) and that the level of expression is positively correlated with the pluripotency of ES cells. We also found that PECAM1-positive ES cells could be divided into two subpopulations according to the expression of stage-specific embryonic antigen (SSEA)-1. ES cells that showed both PECAM1 and SSEA-1 predominantly differentiated into epiblast after the blastocyst stage. In the present study, we performed pairwise oligo microarray analysis to characterize gene expression profiles in PECAM1-positive and -negative subpopulations of ES cells. The microarray analysis identified 2034 genes with a more than 2-fold difference in expression levels between the PECAM1-positive and -negative cells. Of these genes, 803 were more highly expressed in PECAM1-positive cells and 1231 were more highly expressed in PECAM1-negative cells. As expected, genes known to function in ES cells, such as Pou5f1(Oct3/4)and Nanog, were found to be upregulated in PECAM1-positive cells. We also isolated 23 previously uncharacterized genes. A comparison of gene expression profiles in PECAM1-positive cells that were either positive or negative for SSEA-1 expression identified only 53 genes that showed a more than 2-fold greater difference in expression levels between these subpopulations. However, many genes that are under epigenetic regulation, such as globins, Igf2, Igf2r, andH19, showed differential expression. Our results suggest that in addition to differences in gene expression profiles, epigenetic status was altered in the three cell subpopulations.

Animals↗

PubMed2002

Adaptive quality-based clustering of gene expression profiles.

MOTIVATION: Microarray experiments generate a considerable amount of data, which analyzed properly help us gain a huge amount of biologically relevant information about the global cellular behaviour. Clustering (grouping genes with similar expression profiles) is one of the first steps in data analysis of high-throughput expression measurements. A number of clustering algorithms have proved useful to make sense of such data. These classical algorithms, though useful, suffer from several drawbacks (e.g. they require the predefinition of arbitrary parameters like the number of clusters; they force every gene into a cluster despite a low correlation with other cluster members). In the following we describe a novel adaptive quality-based clustering algorithm that tackles some of these drawbacks. RESULTS: We propose a heuristic iterative two-step algorithm: First, we find in the high-dimensional representation of the data a sphere where the "density" of expression profiles is locally maximal (based on a preliminary estimate of the radius of the cluster-quality-based approach). In a second step, we derive an optimal radius of the cluster (adaptive approach) so that only the significantly coexpressed genes are included in the cluster. This estimation is achieved by fitting a model to the data using an EM-algorithm. By inferring the radius from the data itself, the biologist is freed from finding an optimal value for this radius by trial-and-error. The computational complexity of this method is approximately linear in the number of gene expression profiles in the data set. Finally, our method is successfully validated using existing data sets. AVAILABILITY: http://www.esat.kuleuven.ac.be/~thijs/Work/Clustering.html

Algorithms↗

PubMed2003

Systematic expression profiling of the mouse transcriptome using RIKEN cDNA microarrays.

The number of known mRNA transcripts in the mouse has been greatly expanded by the RIKEN Mouse Gene Encyclopedia project. Validation of their reproducible expression in a tissue is an important contribution to the study of functional genomics. In this report, we determine the expression profile of 57,931 clones on 20 mouse tissues using cDNA microarrays. Of these 57,931 clones, 22,928 clones correspond to the FANTOM2 clone set. The set represents 20,234 transcriptional units (TUs) out of 33,409 TUs in the FANTOM2 set. We identified 7206 separate clones that satisfied stringent criteria for tissue-specific expression. Gene Ontology terms were assigned for these 7206 clones, and the proportion of 'molecular function' ontology for each tissue-specific clone was examined. These data will provide insights into the function of each tissue. Tissue-specific gene expression profiles obtained using our cDNA microarrays were also compared with the data extracted from the GNF Expression Atlas based on Affymetrix microarrays. One major outcome of the RIKEN transcriptome analysis is the identification of numerous nonprotein-coding mRNAs. The expression profile was also used to obtain evidence of expression for putative noncoding RNAs. In addition, 1926 clones (70%) of 2768 clones that were categorized as "unknown EST," and 1969 (58%) clones of 3388 clones that were categorized as "unclassifiable" were also shown to be reproducibly expressed.

Animals↗

PubMed2005

Patient-based cross-platform comparison of oligonucleotide microarray expression profiles.

The comparison of gene expression measurements obtained with different technical approaches is of substantial interest in order to clarify whether inter-platform differences may conceal biologically significant information. To address this concern, we analyzed gene expression in a set of head and neck squamous cell carcinoma patients, using both spotted oligonucleotide microarrays made from a large collection of 70-mer probes and commercial arrays produced by in situ synthesis of sets of multiple 25-mer oligonucleotides per gene. Expression measurements were compared for 4425 genes represented on both platforms, which revealed strong correlations between the corresponding data sets. Of note, a global tendency towards smaller absolute ratios was observed when using the 70-mer probes. Real-time quantitative reverse transcription PCR measurements were conducted to verify expression ratios for a subset of genes and achieved good agreement regarding both array platforms. In conclusion, similar profiles of relative gene expression were obtained using arrays of either single 70-mer or multiple short 25-mer oligonucleotide probes per gene. Although qualitative assessments of the expression of individual genes have to be made with caution, our results indicate that the comparison of gene expression profiles generated on these platforms will help to discover disease-related gene signatures in general.

Base Sequence↗