http://www.biomedcentral.com/bmcmedgenomics/ The latest research articles published by BMC Medical Genomics 2009-11-30T00:00:00Z This is an RSS newsfeed from BioMed Central It is intended to be used with an RSS reader. For more information about RSS newsfeeds from BioMed Central, visit http://www.biomedcentral.com/info/about/rss/ Background: Despite the significant progress made in colon cancer chemotherapy, advanced disease remains largely incurable and novel efficacious chemotherapies are urgently needed. Histone deacetylase inhibitors (HDACi) represent a novel class of agents which have demonstrated promising preclinical activity and are undergoing clinical evaluation in colon cancer. The goal of this study was to identify genes in colon cancer cells that are differentially regulated by two clinically advanced hydroxamic acid HDACi, vorinostat and LBH589 to provide rationale for novel drug combination partners and identify a core set of HDACi-regulated genes. Methods: HCT116 and HT29 colon cancer cells were treated with LBH589 or vorinostat and growth inhibition, acetylation status and apoptosis were analyzed in response to treatment using MTS, Western blotting and flow cytometric analyses. In addition, gene expression was analyzed using the Illumina Human-6 V2 BeadChip array and Ingenuity(R) Pathway Analysis. Results: Treatment with either vorinostat or LBH589 rapidly induced histone acetylation, cell cycle arrest and inhibited the growth of both HCT116 and HT29 cells. Bioinformatic analysis of the microarray profiling revealed significant similarity in the genes altered in expression following treatment with the two HDACi tested within each cell line. However, analysis of genes that were altered in expression in the HCT116 and HT29 cells revealed cell-line-specific responses to HDACi treatment. In addition a core cassette of 11 genes modulated by both vorinostat and LBH589 were identified in both colon cancer cell lines analyzed. Conclusions: This study identified HDACi-induced alterations in critical genes involved in nucleotide metabolism, angiogenesis, mitosis and cell survival which may represent potential intervention points for novel therapeutic combinations in colon cancer. This information will assist in the identification of novel pathways and targets that are modulated by HDACi, providing much-needed information on HDACi mechanism of action and providing rationale for novel drug combination partners. We identified a core signature of 11 genes which were modulated by both vorinostat and LBH589 in a similar manner in both cell lines. These core genes will assist in the development and validation of a common gene set which may represent a molecular signature of HDAC inhibition in colon cancer. http://www.biomedcentral.com/1755-8794/2/67 Melissa LaBonte Peter Wilson William Fazzone Susan Groshen Heinz Lenz Robert Ladner BMC Medical Genomics 2009, 2:67 2009-11-30T00:00:00Z doi:10.1186/1755-8794-2-67 BMC Medical Genomics 1755-8794 2 67 2009-11-30T00:00:00Z PDF Background: In molecular profiling studies of cancer patients, experimental and clinical data are combined in order to understand the clinical heterogeneity of the disease: clinical information for each subject needs to be linked to tumour samples, macromolecules extracted, and experimental results. This may involve the integration of clinical data sets from several different sources: these data sets may employ different data definitions and some may be incomplete. Methods: In this work we employ semantic web techniques developed within the CancerGrid project, in particular the use of metadata elements and logic-based inference to annotate heterogeneous clinical information, integrate and query it. Results: We show how this integration can be achieved automatically, following the declaration of appropriate metadata elements for each clinical data set; we demonstrate the practicality of this approach through application to experimental results and clinical data from five hospitals in the UK and Canada, undertaken as part of the METABRIC project (Molecular Taxonomy of Breast Cancer International Consortium). Conclusions: We describe a metadata approach for managing similarities and differences in clinical datasets in a standardized way that uses Common Data Elements (CDEs). We apply and evaluate the approach by integrating the five different clinical datasets of METABRIC. http://www.biomedcentral.com/1755-8794/2/66 Irene Papatheodorou Charles Crichton Lorna Morris Peter Maccallum Molecular Taxonomy of Breast Camcer International Consortium METABRIC Group Jim Davies James Brenton Carlos Caldas BMC Medical Genomics 2009, 2:66 2009-11-30T00:00:00Z doi:10.1186/1755-8794-2-66 BMC Medical Genomics 1755-8794 2 66 2009-11-30T00:00:00Z PDF Background: Sinonasal adenocarcinomas are uncommon tumors which develop in the ethmoid sinus after exposure to wood dust. Although the etiology of these tumors is well defined, very little is known about their molecular basis and no diagnostic tool exists for their early detection in high-risk workers. Methods: To identify genes involved in this disease, we performed gene expression profiling using cancer-dedicated microarrays, on nine matched samples of sinonasal adenocarcinomas and non-tumor sinusal tissue. Microarray results were validated by quantitative RT-PCR and immunohistochemistry on two additional sets of tumors. Results: Among the genes with significant differential expression we selected LGALS4, ACS5, CLU, SRI and CCT5 for further exploration. The overexpression of LGALS4, ACS5, SRI, CCT5 and the downregulation of CLU were confirmed by quantitative RT-PCR. Immunohistochemistry was performed for LGALS4 (Galectin 4), ACS5 (Acyl-CoA synthetase) and CLU (Clusterin) proteins: LGALS4 was highly up-regulated, particularly in the most differentiated tumors, while CLU was lost in all tumors. The expression of ACS5, was more heterogeneous and no correlation was observed with the tumor type. Conclusion: Within our microarray study in sinonasal adenocarcinoma we identified two proteins, LGALS4 and CLU, that were significantly differentially expressed in tumors compared to normal tissue. A further evaluation on a new set of tissues, including precancerous stages and low grade tumors, is necessary to evaluate the possibility of using them as diagnostic markers. http://www.biomedcentral.com/1755-8794/2/65 Dominique Tripodi Sylvia Quemener Karine Renaudin Christophe Ferron Olivier Malard Isabelle Guisle-Marsollier Veronique Sebille-Rivain Christian Verger Christian Geraut Catherine Gratas-Rabbia-Re BMC Medical Genomics 2009, 2:65 2009-11-10T00:00:00Z doi:10.1186/1755-8794-2-65 BMC Medical Genomics 1755-8794 2 65 2009-11-10T00:00:00Z XML Background: One intractable problem with using microarray data analysis for cancer classification is how to reduce the extremely high-dimensionality gene feature data to remove the effects of noise. Feature selection is often used to address this problem by selecting informative genes from among thousands or tens of thousands of genes. However, most of the existing methods of microarray-based cancer classification utilize too many genes to achieve accurate classification, which often hampers the interpretability of the models. For a better understanding of the classification results, it is desirable to develop simpler rule-based models with as few marker genes as possible. Methods: We screened a small number of informative single genes and gene pairs on the basis of their depended degrees proposed in rough sets. Applying the decision rules induced by the selected genes or gene pairs, we constructed cancer classifiers. We tested the efficacy of the classifiers by leave-one-out cross-validation (LOOCV) of training sets and classification of independent test sets. Results: We applied our methods to five cancerous gene expression datasets: leukemia (acute lymphoblastic leukemia [ALL] vs. acute myeloid leukemia [AML]), lung cancer, prostate cancer, breast cancer, and leukemia (ALL vs. mixed-lineage leukemia [MLL] vs. AML). Accurate classification outcomes were obtained by utilizing just one or two genes. Some genes that correlated closely with the pathogenesis of relevant cancers were identified. In terms of both classification performance and algorithm simplicity, our approach outperformed or at least matched existing methods. Conclusion: In cancerous gene expression datasets, a small number of genes, even one or two if selected correctly, is capable of achieving an ideal cancer classification effect. This finding also means that very simple rules may perform well for cancerous class prediction. http://www.biomedcentral.com/1755-8794/2/64 Xiaosheng Wang Osamu Gotoh BMC Medical Genomics 2009, 2:64 2009-10-30T00:00:00Z doi:10.1186/1755-8794-2-64 BMC Medical Genomics 1755-8794 2 64 2009-10-30T00:00:00Z XML Background: Autoimmune diabetes (T1D) onset is preceded by a long inflammatory process directed against the insulin-secreting β cells of the pancreas. Deciphering the early autoimmune mechanisms represents a challenge due to the absence of clinical signs at early disease stages. The aim of this study was to identify genes implicated in the early steps of the autoimmune process, prior to inflammation, in T1D. We have previously established that insulin autoantibodies (E-IAA) predict early diabetes onset delineating an early phenotypic check point (window 1) in disease pathogenesis. We used this sub-phenotype and applied differential gene expression analysis in the pancreatic lymph nodes (PLN) of 5 weeks old Non Obese Diabetic (NOD) mice differing solely upon the presence or absence of E-IAA. Analysis of gene expression profiles has the potential to provide a global understanding of the disease and to generate novel hypothesis concerning the initiation of the autoimmune process. Methods: Animals have been screened weekly for the presence of E-IAA between 3 and 5 weeks of age. E-IAA positive or negative NOD mice at least twice were selected and RNAs isolated from the PLN were used for microarray analysis. Comparison of transcriptional profiles between positive and negative animals and functional annotations of the resulting differentially expressed genes, using software together with manual literature data mining, have been performed. Results: The expression of 165 genes was modulated between E-IAA positive and negative PLN. In particular, genes coding for insulin and for proteins known to be implicated in tissue remodelling and Th1 immunity have been found to be highly differentially expressed. Forty one genes showed over 5 fold differences between the two sets of samples and 30 code for extracellular proteins. This class of proteins represents potential diagnostic markers and drug targets for T1D. Conclusion: Our data strongly suggest that the immune related mechanisms taking place at this early age in the PLN, correlate with homeostatic changes influencing tissue integrity of the adjacent pancreatic tissue. Functional analysis of the identified genes suggested that similar mechanisms might be operating during pre-inflammatory processes deployed in tissues i) hosting parasitic microorganisms and ii) experiencing unrestricted invasion by tumour cells. http://www.biomedcentral.com/1755-8794/2/63 Beatrice Regnault Jose Osorio y Fortea Dongmei Miao George Eisenbarth Evie Melanitou BMC Medical Genomics 2009, 2:63 2009-10-02T00:00:00Z doi:10.1186/1755-8794-2-63 BMC Medical Genomics 1755-8794 2 63 2009-10-02T00:00:00Z XML Background: The purpose of this study was to examine the effects of glucose reduction stress on lymphoblastic cell line (LCL) gene expression in subjects with schizophrenia compared to non-psychotic relatives. Methods: LCLs were grown under two glucose conditions to measure the effects of glucose reduction stress on exon expression in subjects with schizophrenia compared to unaffected family member controls. A second aim of this project was to identify cis-regulated transcripts associated with diagnosis. Results: There were a total of 122 transcripts with significant diagnosis by probeset interaction effects and 328 transcripts with glucose deprivation by probeset interaction probeset effects after corrections for multiple comparisons. There were 8 transcripts with expression significantly affected by the interaction between diagnosis and glucose deprivation and probeset after correction for multiple comparisons. The overall validation rate by qPCR of 13 diagnosis effect genes identified through microarray was 62%, and all genes tested by qPCR showed concordant up- or down-regulation by qPCR and microarray. We assessed brain gene expression of five genes found to be altered by diagnosis and glucose deprivation in LCLs and found a significant decrease in expression of one gene, glutaminase, in the dorsolateral prefrontal cortex (DLPFC). One SNP with previously identified regulation by a 3' UTR SNP was found to influence IRF5 expression in both brain and lymphocytes. The relationship between the 3' UTR rs10954213 genotype and IRF5 expression was significant in LCLs (p = 0.0001), DLPFC (p = 0.007), and anterior cingulate cortex (p = 0.002). Conclusion: Experimental manipulation of cells lines from subjects with schizophrenia may be a useful approach to explore stress related gene expression alterations in schizophrenia and to identify SNP variants associated with gene expression. http://www.biomedcentral.com/1755-8794/2/62 Maureen Martin Brandi Rollins P. Adolpho Sequeira Andrea Mesen William Byerley Richard Stein Emily Moon Huda Akil Edward Jones Stanley Watson Jack Barchas Lynn DeLisi Richard Myers Alan Schatzberg William Bunney Marquis Vawter BMC Medical Genomics 2009, 2:62 2009-09-22T00:00:00Z doi:10.1186/1755-8794-2-62 BMC Medical Genomics 1755-8794 2 62 2009-09-22T00:00:00Z XML Background: Identification of novel cancer-causing genes is one of the main goals in cancer research. The rapid accumulation of genome-wide protein-protein interaction (PPI) data in humans has provided a new basis for studying the topological features of cancer genes in cellular networks. It is important to integrate multiple genomic data sources, including PPI networks, protein domains and Gene Ontology (GO) annotations, to facilitate the identification of cancer genes. Methods: Topological features of the PPI network, as well as protein domain compositions, enrichment of gene ontology categories, sequence and evolutionary conservation features were extracted and compared between cancer genes and other genes. The predictive power of various classifiers for identification of cancer genes was evaluated by cross validation. Experimental validation of a subset of the prediction results was conducted using siRNA knockdown and viability assays in human colon cancer cell line DLD-1. Results: Cross validation demonstrated advantageous performance of classifiers based on support vector machines (SVMs) with the inclusion of the topological features from the PPI network, protein domain compositions and GO annotations. We then applied the trained SVM classifier to human genes to prioritize putative cancer genes. siRNA knock-down of several SVM predicted cancer genes displayed greatly reduced cell viability in human colon cancer cell line DLD-1. Conclusion: Topological features of PPI networks, protein domain compositions and GO annotations are good predictors of cancer genes. The SVM classifier integrates multiple features and as such is useful for prioritizing candidate cancer genes for experimental validations. http://www.biomedcentral.com/1755-8794/2/61 Li Li Kangyu Zhang James Lee Shaun Cordes David Davis Zhijun Tang BMC Medical Genomics 2009, 2:61 2009-09-19T00:00:00Z doi:10.1186/1755-8794-2-61 BMC Medical Genomics 1755-8794 2 61 2009-09-19T00:00:00Z XML Background: Full-thickness articular cartilage lesions that reach to the subchondral bone yet are restricted to the chondral compartment usually fill with a fibrocartilage-like repair tissue which is structurally and biomechanically compromised relative to normal articular cartilage. The objective of this study was to evaluate transcriptional differences between chondrocytes of normal articular cartilage and repair tissue cells four months post-microfracture. Methods: Bilateral one-cm2 full-thickness defects were made in the articular surface of both distal femurs of four adult horses followed by subchondral microfracture. Four months postoperatively, repair tissue from the lesion site and grossly normal articular cartilage from within the same femorotibial joint were collected. Total RNA was isolated from the tissue samples, linearly amplified, and applied to a 9,413-probe set equine-specific cDNA microarray. Eight paired comparisons matched by limb and horse were made with a dye-swap experimental design with validation by histological analyses and quantitative real-time polymerase chain reaction (RT-qPCR). Results: Statistical analyses revealed 3,327 (35.3%) differentially expressed probe sets. Expression of biomarkers typically associated with normal articular cartilage and fibrocartilage repair tissue corroborate earlier studies. Other changes in gene expression previously unassociated with cartilage repair were also revealed and validated by RT-qPCR. Conclusion: The magnitude of divergence in transcriptional profiles between normal chondrocytes and the cells that populate repair tissue reveal substantial functional differences between these two cell populations. At the four-month postoperative time point, the relative deficiency within repair tissue of gene transcripts which typically define articular cartilage indicate that while cells occupying the lesion might be of mesenchymal origin, they have not recapitulated differentiation to the chondrogenic phenotype of normal articular chondrocytes. http://www.biomedcentral.com/1755-8794/2/60 Michael Mienaltowski Liping Huang David Frisbie C. Wayne McIlwraith Arnold Stromberg Arne Bathke James MacLeod BMC Medical Genomics 2009, 2:60 2009-09-14T00:00:00Z doi:10.1186/1755-8794-2-60 BMC Medical Genomics 1755-8794 2 60 2009-09-14T00:00:00Z XML Background: Pathway discovery from gene expression data can provide important insight into the relationship between signaling networks and cancer biology. Oncogenic signaling pathways are commonly inferred by comparison with signatures derived from cell lines. We use the Molecular Apocrine subtype of breast cancer to demonstrate our ability to infer pathways directly from patients' gene expression data with pattern analysis algorithms. Methods: We combine data from two studies that propose the existence of the Molecular Apocrine phenotype. We use quantile normalization and XPN to minimize institutional bias in the data. We use hierarchical clustering, principal components analysis, and comparison of gene signatures derived from Significance Analysis of Microarrays to establish the existence of the Molecular Apocrine subtype and the equivalence of its molecular phenotype across both institutions. Statistical significance was computed using the Fasano & Franceschini test for separation of principal components and the hypergeometric probability formula for significance of overlap in gene signatures. We perform pathway analysis using LeFEminer and Backward Chaining Rule Induction to identify a signaling network that differentiates the subset. We identify a larger cohort of samples in the public domain, and use Gene Shaving and Robust Bayesian Network Analysis to detect pathways that interact with the defining signal. Results: We demonstrate that the two separately introduced ER- breast cancer subsets represent the same tumor type, called Molecular Apocrine breast cancer. LeFEminer and Backward Chaining Rule Induction support a role for AR signaling as a pathway that differentiates this subset from others. Gene Shaving and Robust Bayesian Network Analysis detect interactions between the AR pathway, EGFR trafficking signals, and ErbB2. Conclusion: We propose criteria for meta-analysis that are able to demonstrate statistical significance in establishing molecular equivalence of subsets across institutions. Data mining strategies used here provide an alternative method to comparison with cell lines for discovering seminal pathways and interactions between signaling networks. Analysis of Molecular Apocrine breast cancer implies that therapies targeting AR might be hampered if interactions with ErbB family members are not addressed. http://www.biomedcentral.com/1755-8794/2/59 Sandeep Sanga Bradley Broom Vittorio Cristini Mary Edgerton BMC Medical Genomics 2009, 2:59 2009-09-11T00:00:00Z doi:10.1186/1755-8794-2-59 BMC Medical Genomics 1755-8794 2 59 2009-09-11T00:00:00Z XML Background: In addition to their well-documented ocular therapeutic effects, glucocorticoids (GCs) can cause sight-threatening side-effects including ocular hypertension presumably via morphological and biochemical changes in trabecular meshwork (TM) cells. In the present study, we directly compared the glucocorticoid receptor (GR) potency for dexamethasone (DEX), fluocinolone acetonide (FA) and triamcinolone acetonide (TA), examined the expression of known GRα and GRβ isoforms, and used gene expression microarrays to compare the effects of DEX, FA, and TA on the complete transcriptome in two primary human TM cell lines. Methods: GR binding affinity for DEX, FA, and TA was measured by a cell-free competitive radio-labeled GR binding assay. GR-mediated transcriptional activity was assessed using the GeneBLAzer beta-lactamase reporter gene assay. Levels of GRα and GRβ isoforms were assessed by Western blot. Total RNA was extracted from TM 86 and TM 93 cells treated with 1 μM DEX, FA, or TA for 24 hr and used for microarray gene expression analysis. The microarray experiments were repeated three times. Differentially expressed genes were identified by Rosetta Resolver Gene Expression Analysis System. Results: The GR binding affinity (IC50) for DEX, FA, and TA was 5.4, 2.0, and 1.5 nM, respectively. These values are similar to the GR transactivation EC50 of 3.0, 0.7, and 1.5 nM for DEX, FA, and TA, respectively. All four GRα translational isoforms (A-D) were expressed in TM 86 and TM 93 total cell lysates, however, the C and D isoforms were more highly expressed relative to A and B. All four GRβ isoforms (A-D) were also detected in TM cells, although GRβ-D isoform expression was lower compared to that of the A, B, or C isoforms. Microarray analysis revealed 1,968 and 1,150 genes commonly regulated by DEX, FA, and TA in TM 86 and TM 93, respectively. These genes included RGC32, OCA2, ANGPTL7, MYOC, FKBP5, SAA1 and ZBTB16. In addition, each GC specifically regulated a unique set of genes in both TM cell lines. Using Ingenuity Pathway Analysis (IPA) software, analysis of the data from TM 86 cells showed that DEX significantly regulated transcripts associated with RNA post-transcriptional modifications, whereas FA and TA modulated genes involved in lipid metabolism and cell morphology, respectively. In TM 93 cells, DEX significantly regulated genes implicated in histone methylation, whereas FA and TA altered genes associated with cell cycle and cell adhesion, respectively. Conclusion: Human trabecular meshwork cells in culture express all known GRα and GRβ translational isoforms, and GCs with similar potency but subtly different chemical structure are capable of regulating common and unique gene subsets and presumably biologic responses in these cells. These GC structure-dependent effects appear to be TM cell-lineage dependent. http://www.biomedcentral.com/1755-8794/2/58 Alissar Nehme Edward Lobenhofer W Stamer Jeffrey Edelman BMC Medical Genomics 2009, 2:58 2009-09-10T00:00:00Z doi:10.1186/1755-8794-2-58 BMC Medical Genomics 1755-8794 2 58 2009-09-10T00:00:00Z XML