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At least 1,279 records · Page 71Linked to original sources
PubMed2004

Gene expression in the urinary bladder: a common carcinoma in situ gene expression signature exists disregarding histopathological classification.

The presence of carcinoma in situ (CIS) lesions in the urinary bladder is associated with a high risk of disease progression to a muscle invasive stage. In this study, we used microarray expression profiling to examine the gene expression patterns in superficial transitional cell carcinoma (sTCC) with surrounding CIS (13 patients), without surrounding CIS lesions (15 patients), and in muscle invasive carcinomas (mTCC; 13 patients). Hierarchical cluster analysis separated the sTCC samples according to the presence or absence of CIS in the surrounding urothelium. We identified a few gene clusters that contained genes with similar expression levels in transitional cell carcinoma (TCC) with surrounding CIS and invasive TCC. However, no close relationship between TCC with adjacent CIS and invasive TCC was observed using hierarchical cluster analysis. Expression profiling of a series of biopsies from normal urothelium and urothelium with CIS lesions from the same urinary bladder revealed that the gene expression found in sTCC with surrounding CIS is found also in CIS biopsies as well as in histologically normal samples adjacent to the CIS lesions. Furthermore, we also identified similar gene expression changes in mTCC samples. We used a supervised learning approach to build a 16-gene molecular CIS classifier. The classifier was able to classify sTCC samples according to the presence or absence of surrounding CIS with a high accuracy. This study demonstrates that a CIS gene expression signature is present not only in CIS biopsies but also in sTCC, mTCC, and, remarkably, in histologically normal urothelium from bladders with CIS. Identification of this expression signature could provide guidance for the selection of therapy and follow-up regimen in patients with early stage bladder cancer.

Biopsy↗
PubMed2005

Using a xenograft model of human breast cancer metastasis to find genes associated with clinically aggressive disease.

Metastasis is the primary cause of death from breast cancer. A xenograft model was used to identify genes potentially involved with metastasis, comparing expression in the poorly metastatic GI101A human breast cancer cell line and a highly metastatic variant, GILM2. cDNA microarray analyses of these isogenic variants were done using 16K Operon 70-mer oligonucleotide microarray slides. Differentially expressed genes were identified by ANOVA, and differences of > or =2.5-fold were found for 106 genes. Changes in protein or RNA expression were confirmed for 10 of 12 genes. Three markers, heat shock protein 70 (HSP-70), chemokine (C-X-C motif) ligand 1 (CXCL-1), and secreted leukocyte protease inhibitor (SLPI), were studied further with breast cancer tissue microarrays using a novel method of automated quantitative analysis. This uses cytokeratin to define pixels as breast cancer (tumor mask) within the tissue array spot and then measures intensity of marker expression using a cyanine 5-conjugated antibody within the mask. Scores were correlated with clinicopathologic variables. High HSP-70 expression and high nuclear CXCL-1 expression in primary tumors were both associated with decreased survival (P = 0.05 and 0.027, respectively). Expression of each marker was strongly associated with lymph node involvement (P = 0.0002, 0.008, 0.0012, and 0.012 for HSP-70, nuclear CXCL-1, cytoplasmic CXCL-1, and SLPI, respectively). Identification of genes associated with metastasis in experimental models may have clinical implications for the management of breast cancer, because some of these are associated with lymph node metastasis and survival and might be useful as prognostic markers or molecular targets for novel therapies.

Animals↗
PubMed2006

Common cancer biomarkers.

There is an increasing interest in complementing conventional histopathologic evaluation with molecular tools that could increase the sensitivity and specificity of cancer staging for diagnostic and prognostic purposes. This study strove to identify cancer-specific markers for the molecular detection of a broad range of cancer types. We used 373 archival samples inclusive of normal tissues of various lineages and benign or malignant tumors (predominantly colon, melanoma, ovarian, and esophageal cancers). All samples were processed identically and cohybridized with an identical reference RNA source to a custom-made cDNA array platform. The database was split into training (n = 201) and comparable prediction (n = 172) sets. Leave-one-out cross-validation and gene pairing analysis identified putative cancer biomarkers overexpressed by malignant lesions independent of tissue of derivation. In particular, seven gene pairs were identified with high predictive power (87%) in segregating malignant from benign lesions. Receiver operator characteristic curves based on the same genes could segregate malignant from benign tissues with 94% accuracy. The relevance of this study rests on the identification of a restricted number of biomarkers ubiquitously expressed by cancers of distinct histology. This has not been done before. These biomarkers could be used broadly to increase the sensitivity and accuracy of cancer staging and early detection of locoregional or systemic recurrence. Their selective expression by cancerous compared with paired normal tissues suggests an association with the oncogenic process resulting in stable expression during disease progression when the presently used differentiation markers are unreliable.

Biomarkers, Tumor↗
PubMed2006

Recursive SVM feature selection and sample classification for mass-spectrometry and microarray data.

BACKGROUND: Like microarray-based investigations, high-throughput proteomics techniques require machine learning algorithms to identify biomarkers that are informative for biological classification problems. Feature selection and classification algorithms need to be robust to noise and outliers in the data. RESULTS: We developed a recursive support vector machine (R-SVM) algorithm to select important genes/biomarkers for the classification of noisy data. We compared its performance to a similar, state-of-the-art method (SVM recursive feature elimination or SVM-RFE), paying special attention to the ability of recovering the true informative genes/biomarkers and the robustness to outliers in the data. Simulation experiments show that a 5%- approximately 20% improvement over SVM-RFE can be achieved regard to these properties. The SVM-based methods are also compared with a conventional univariate method and their respective strengths and weaknesses are discussed. R-SVM was applied to two sets of SELDI-TOF-MS proteomics data, one from a human breast cancer study and the other from a study on rat liver cirrhosis. Important biomarkers found by the algorithm were validated by follow-up biological experiments. CONCLUSION: The proposed R-SVM method is suitable for analyzing noisy high-throughput proteomics and microarray data and it outperforms SVM-RFE in the robustness to noise and in the ability to recover informative features. The multivariate SVM-based method outperforms the univariate method in the classification performance, but univariate methods can reveal more of the differentially expressed features especially when there are correlations between the features.

Algorithms↗
PubMed2007

Interpretation of multiple probe sets mapping to the same gene in Affymetrix GeneChips.

BACKGROUND: Affymetrix GeneChip technology enables the parallel observations of tens of thousands of genes. It is important that the probe set annotations are reliable so that biological inferences can be made about genes which undergo differential expression. Probe sets representing the same gene might be expected to show similar fold changes/z-scores, however this is in fact not the case. RESULTS: We have made a case study of the mouse Surf4, chosen because it is a gene that was reported to be represented by the same eight probe sets on the MOE430A array by both Affymetrix and Bioconductor in early 2004. Only five of the probe sets actually detect Surf4 transcripts. Two of the probe sets detect splice variants of Surf2. We have also studied the expression changes of the eight probe sets in a public-domain microarray experiment. The transcripts for Surf4 are correlated in time, and similarly the transcripts for Surf2 are also correlated in time. However, the transcripts for Surf4 and Surf2 are not correlated. This proof of principle shows that observations of expression can be used to confirm, or otherwise, annotation discrepancies. We have also investigated groups of probe sets on the RAE230A array that are assigned to the same LocusID, but which show large variances in differential expression in any one of three different experiments on rat. The probe set groups with high variances are found to represent cases of alternative splicing, use of alternative poly(A) signals, or incorrect annotations. CONCLUSION: Our results indicate that some probe sets should not be considered as unique measures of transcription, because the individual probes map to more than one transcript dependent upon the biological condition. Our results highlight the need for care when assessing whether groups of probe sets all measure the same transcript.

Animals↗
PubMed2006

Transcriptional profiling of mesenchymal stromal cells from young and old rats in response to Dexamethasone.

BACKGROUND: Marrow-derived stromal cells (MSCs) maintain the capability of self-renewal and differentiation into multiple lineages in adult life. Age-related changes are recognized by a decline in the stemness potential that result in reduced regeneration potential of the skeleton. To explore the molecular events that underline skeletal physiology during aging we catalogued the profile of gene expression in ex vivo cultured MSCs derived from 3 and 15 month old rats. The ex vivo cultured cells were analyzed following challenge with or without Dexamethasone (Dex). RNA retrieved from these cells was analyzed using Affymetrix Gene Chips to compare the effect of Dex on gene expression in both age groups. RESULTS: The molecular mechanisms that underline skeletal senescence were studied by gene expression analysis of RNA harvested from MSCs. The analysis resulted in complex profiles of gene expression of various differentiation pathways. We revealed changes of lineage-specific gene expression; in general the pattern of expression included repression of proliferation and induction of differentiation. The functional analysis of genes clustered were related to major pathways; an increase in bone remodeling, osteogenesis and muscle formation, coupled with a decrease in adipogenesis. We demonstrated a Dex-related decrease in immune response and in genes that regulate bone resorption and an increase in osteoblastic differentiation. Myogenic-related genes and genes that regulate cell cycle were induced by Dex. While Dex repressed genes related to adipogenesis and catabolism, this decrease was complementary to an increase in expression of genes related to osteogenesis. CONCLUSION: This study summarizes the genes expressed in the ex vivo cultured mesenchymal cells and their response to Dex. Functional clustering highlights the complexity of gene expression in MSCs and will advance the understanding of major pathways that trigger the natural changes underlining physiological aging. The high throughput analysis shed light on the anabolic effect of Dex and the relationship between osteogenesis, myogenesis and adipogenesis in the bone marrow cells.

Aging↗
PubMed2006

Identification of genes regulated by Wnt/beta-catenin pathway and involved in apoptosis via microarray analysis.

BACKGROUND: Wnt/beta-catenin pathway has critical roles in development and oncogenesis. Although significant progress has been made in understanding the downstream signaling cascade of this pathway, little is known regarding Wnt/beta-catenin pathway modification of the cellular apoptosis. METHODS: To identify potential genes regulated by Wnt/beta-catenin pathway and involved in apoptosis, we used a stably integrated, inducible RNA interference (RNAi) vector to specific inhibit the expression and the transcriptional activity of beta-catenin in HeLa cells. Meanwhile, we designed an oligonucleotide microarray covering 1384 apoptosis-related genes. Using oligonucleotide microarrays, a series of differential expression of genes was identified and further confirmed by RT-PCR. RESULTS: Stably integrated inducible RNAi vector could effectively suppress beta-catenin expression and the transcriptional activity of beta-catenin/TCF. Meanwhile, depletion of beta-catenin in this manner made the cells more sensitive to apoptosis. 130 genes involved in some important cell-apoptotic pathways, such as PTEN-PI3K-AKT pathway, NF-kappaB pathway and p53 pathway, showed significant alteration in their expression level after the knockdown of beta-catenin. CONCLUSION: Coupling RNAi knockdown with microarray and RT-PCR analyses proves to be a versatile strategy for identifying genes regulated by Wnt/beta-catenin pathway and for a better understanding the role of this pathway in apoptosis. Some of the identified beta-catenin/TCF directed or indirected target genes may represent excellent targets to limit tumor growth.

Apoptosis↗
PubMed2002

CLUSFAVOR 5.0: hierarchical cluster and principal-component analysis of microarray-based transcriptional profiles.

CLUSFAVOR (CLUSter and Factor Analysis with Varimax Orthogonal Rotation) 5.0 standardizes input data; sorts data according to gene-specific coefficient of variation, standard deviation, average and total expression, and Shannon entropy; performs hierarchical cluster analysis using nearest-neighbor, unweighted pair-group method using arithmetic averages (UPGMA), or furthest-neighbor joining methods, and Euclidean, correlation, or jack-knife distances; and performs principal-component analysis.

Animals↗
PubMed2003

Application of independent component analysis to microarrays.

We apply linear and nonlinear independent component analysis (ICA) to project microarray data into statistically independent components that correspond to putative biological processes, and to cluster genes according to over- or under-expression in each component. We test the statistical significance of enrichment of gene annotations within clusters. ICA outperforms other leading methods, such as principal component analysis, k-means clustering and the Plaid model, in constructing functionally coherent clusters on microarray datasets from Saccharomyces cerevisiae, Caenorhabditis elegans and human.

Algorithms↗
PubMed2004

Cluster busters.

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Animals↗
PubMed2002

Comprehensive microarray analysis of bone morphogenetic protein 2-induced osteoblast differentiation resulting in the identification of novel markers for bone development.

Osteoblasts are cells responsible for matrix deposition during bone development and although temporal expression of many genes has been related to osteoblast differentiation, a complete description of osteoblast-specific gene regulation will lead to a better understanding of osteoblast function. In this study, microarray technology was used to analyze gene expression on a broad scale during osteoblast differentiation. Expression analysis of 9596 sequences revealed 342 genes and expressed sequence tags (ESTs) to be modulated differentially during a time course experiment in which murine C2C12 mesenchymal progenitor cells were induced to differentiate into mature osteoblasts by treatment with bone morphogenetic protein 2 (BMP-2). By means of hierarchical clustering, these genes were grouped by similarities in their expression profiles, resulting in subsets of early, intermediate, and late response genes, which are representative of the distinct stages of osteoblast differentiation. To identify new bone markers, the bone specificity of the late response genes was determined by comparing BMP-induced expression in C2C12 and MC3T3 osteoblasts with that in NIH3T3 fibroblasts. This resulted in the identification of nine novel genes and ESTs that were induced specifically in osteoblasts, in addition to the well-known markers ALP and osteocalcin. For at least one of these novel genes, Wnt inhibitory factor 1, and two of the ESTs, expression in developing bone was verified in vivo by in situ hybridization of E16.5 mouse embryos. In conclusion, by a combination of in vitro and in vivo screening approaches, a set of new genes related to osteoblast differentiation and skeletal development has been identified.

3T3 Cells↗
PubMed2006

Genome-scale identification of membrane-associated human mRNAs.

The subcellular localization of proteins is critical to their biological roles. Moreover, whether a protein is membrane-bound, secreted, or intracellular affects the usefulness of, and the strategies for, using a protein as a diagnostic marker or a target for therapy. We employed a rapid and efficient experimental approach to classify thousands of human gene products as either "membrane-associated/secreted" (MS) or "cytosolic/nuclear" (CN). Using subcellular fractionation methods, we separated mRNAs associated with membranes from those associated with the soluble cytosolic fraction and analyzed these two pools by comparative hybridization to DNA microarrays. Analysis of 11 different human cell lines, representing lymphoid, myeloid, breast, ovarian, hepatic, colon, and prostate tissues, identified more than 5,000 previously uncharacterized MS and more than 6,400 putative CN genes at high confidence levels. The experimentally determined localizations correlated well with in silico predictions of signal peptides and transmembrane domains, but also significantly increased the number of human genes that could be cataloged as encoding either MS or CN proteins. Using gene expression data from a variety of primary human malignancies and normal tissues, we rationally identified hundreds of MS gene products that are significantly overexpressed in tumors compared to normal tissues and thus represent candidates for serum diagnostic tests or monoclonal antibody-based therapies. Finally, we used the catalog of CN gene products to generate sets of candidate markers of organ-specific tissue injury. The large-scale annotation of subcellular localization reported here will serve as a reference database and will aid in the rational design of diagnostic tests and molecular therapies for diverse diseases.

Biomarkers, Tumor↗
PubMed2005

Analysis of microarray experiments for pulmonary fibrosis.

Microarray technology allows the investigator to examine the simultaneous expression of thousands of genes in a given cell or tissue. Such experiments that probe tens of thousands of genes produce immense amounts of information. In recent years, there has been a steady increase in the availability of tools for analysis of microarray data. Although many commercial tools are being aggressively marketed, we mostly use shareware tools. In this chapter, we present our approach to the analysis of microarray data mainly using tools that were generated by our collaborators or that are freely available. Step-by-step explanations of software operation are provided.

Algorithms↗
PubMed2005

Single-nucleotide polymorphism genotyping in DNA pools.

To undertake partial, or complete, genome screens by association-based methodology for quantitative trait loci, multiple individuals have to be screened for large numbers of genetic markers. Consequently, much recent interest has focused on methods enabling accurate allele quantification in pooled deoxyribonucleic acid (DNA) samples. Microsatellites were the favored markers in initial studies, but the extraordinary wealth of data concerning single-nucleotide polymorphisms (SNPs) has turned attention to the quantification of SNP alleles in pools. All such approaches require accurate estimation of DNA concentrations, followed by the preparation of replicate pools, their validation, and application of procedures for determining allele frequencies. This chapter describes the important steps in preparing pools and surveys a variety of techniques that have been proposed for SNP detection. Finally, we describe the application of a generic approach using pooled DNA for detection of allele frequency differences between case and control populations based on primer extension protocols and outline a strategy for estimating SNP allele frequencies employing microarrays.

DNA↗
PubMed2006

Individual differences of acupuncture analgesia in humans using cDNA microarray.

A large amount of evidence suggests that acupuncture stimulation enhances the experimental pain threshold in various animal models. Acupuncture analgesia is mediated by the endogenous opioid system, and the analgesic response to acupuncture shows individual variation. This study identified and characterized the genes that differ between high responders (HR) and low responders (LR) on acupuncture stimulation, using a cDNA microarray. Fifteen participants were stimulated at the LI 4 acupuncture point, and the finger withdrawal latency (FWL) test was performed to classify the HR and LR groups. Total RNA was then extracted from blood samples from each group and used as a template to synthesize cDNA. The cDNA was applied to Code Link UniSet Human 20K microarray chips. The Symptom Checklist 90-Revised (SCL-90-R) was also analyzed as a measure of psychological variation. The FWL was significantly elevated in the HR group after acupuncture stimulation, whereas there was little increase in the LR group. The ratio of HR to LR subjects was 9:6. We found that 353 and 22 genes were up- and downregulated, respectively, in the HR group. However, the SCL-90-R profiles did not differ significantly between the two groups. These results suggest that the individual variation in acupuncture analgesia, verified by measuring the FWL in the HR and LR groups, resulted from genetic inheritance rather than differences in the psychological environment.

Acupuncture Analgesia↗
PubMed2005

Adding semantics to gene expression profiles: new tools for drug discovery.

Gene expression profiles are unveiling a wealth of new potential drug targets for a wide range of diseases, offering new opportunities for drug discoveries. The emerging challenge, however, is the effective selection of the myriad of targets to identify those with the most therapeutic utility. Numerical clustering has became a commonly used method to investigate and interpret gene expression data sets but it is often inadequate to infer the genes' and proteins' role and point to candidate genes for drug development. This review illustrates how clustering methods based on semantic characteristics, such as gene ontologies, could be used to extract more knowledge from genomic data and improve drug target and discovery processes.

Animals↗
PubMed2006

A generalized clustering problem, with application to DNA microarrays.

We think of cluster analysis as class discovery. That is, we assume that there is an unknown mapping called clustering structure that assigns a class label to each observation, and the goal of cluster analysis is to estimate this clustering structure, that is, to estimate the number of clusters and cluster assignments. In traditional cluster analysis, it is assumed that such unknown mapping is unique. However, since the observations may cluster in more than one way depending on the variables used, it is natural to permit the existence of more than one clustering structure. This generalized clustering problem of estimating multiple clustering structures is the focus of this paper. We propose an algorithm for finding multiple clustering structures of observations which involves clustering both variables and observations. The number of clustering structures is determined by the number of variable clusters. The dissimilarity measure for clustering variables is based on nearest-neighbor graphs. The observations are clustered using weighted distances with weights determined by the clusters of the variables. The motivating application is to gene expression data.

Algorithms↗
PubMed2003

Global analysis of shear stress-responsive genes in vascular endothelial cells.

DNA microarray gene expression analysis was conducted in human umbilical vein endothelial cells (HUVECs) and coronary artery endothelial cells (HCAECs) exposed to laminar or turbulent shear stress. Approximately 3% of the total 5600 gene in HUVECs and HCAECs increased their expression more than two-fold or decreased it to less than half the static control in response to an arterial level of laminar shear stress (15 dynes/cm(2) for 24 hours). The proportions of shear-stress-responsive genes decreased to around 2% under the venous level of laminar shear stress (1.5 dynes/cm(2)) in both cell lines. Turbulent shear stress of 1.5 dynes/cm(2) altered the expression of 1.1% of all genes in the HCAECs. Laminar shear stress, but not turbulent shear stress, decreased the expression of a number of genes involved in DNA synthesis and the cell cycle in both HUVECs and HCAECs. Clustering analysis showed a variety of temporal profiles of gene expression in HUVECs exposed to laminar shear stress of 15 dynes/cm(2) for 3, 6, 12, 24, and 48 hours. Turbulent shear stress affected expression of many genes that play a role in vascular remodeling, including genes encoding plasminogen activators and their inhibitor, endothelin-1, transforming growth factor-beta, collagen type IV, and ephrin A1.

Arteriosclerosis↗