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Find video protocols related to scientific articles indexed in Pubmed.
HYPOTHESIS SETTING AND ORDER STATISTIC FOR ROBUST GENOMIC META-ANALYSIS.
Ann Appl Stat
PUBLISHED: 11-11-2014
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Meta-analysis techniques have been widely developed and applied in genomic applications, especially for combining multiple transcriptomic studies. In this paper, we propose an order statistic of p-values (rth ordered p-value, rOP) across combined studies as the test statistic. We illustrate different hypothesis settings that detect gene markers differentially expressed (DE) "in all studies", "in the majority of studies", or "in one or more studies", and specify rOP as a suitable method for detecting DE genes "in the majority of studies". We develop methods to estimate the parameter r in rOP for real applications. Statistical properties such as its asymptotic behavior and a one-sided testing correction for detecting markers of concordant expression changes are explored. Power calculation and simulation show better performance of rOP compared to classical Fisher's method, Stouffer's method, minimum p-value method and maximum p-value method under the focused hypothesis setting. Theoretically, rOP is found connected to the naïve vote counting method and can be viewed as a generalized form of vote counting with better statistical properties. The method is applied to three microarray meta-analysis examples including major depressive disorder, brain cancer and diabetes. The results demonstrate rOP as a more generalizable, robust and sensitive statistical framework to detect disease-related markers.
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Complement Pathway is Frequently Altered in Endometriosis and Endometriosis-Associated Ovarian Cancer.
Clin. Cancer Res.
PUBLISHED: 10-09-2014
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Purpose. Mechanisms of immune dysregulation associated with advanced tumors are relatively well understood. Much less is known about the role of immune effectors against cancer precursor lesions. Endometrioid and clear cell ovarian tumors partly derive from endometriosis, a commonly diagnosed chronic inflammatory disease. We performed here a comprehensive immune gene expression analysis of pelvic inflammation in endometriosis and endometriosis-associated ovarian cancer (EAOC). Experimental design: RNA was extracted from 120 paraffin tissue blocks comprising of normal endometrium (n=32), benign endometriosis (n=30), atypical endometriosis (n=15) and EAOC (n=43). Serous tumors (n=15) were included as non-endometriosis associated controls. The immune microenvironment was profiled using Nanostring and the nCounter® GX Human Immunology Kit, comprising probes for a total of 511 immune genes. Results: One third of the endometriosis patients revealed a tumor-like inflammation profile, suggesting that cancer -like immune signatures may develop earlier, in patients classified as clinically benign. Gene expression analyses revealed the complement pathway as most prominently involved in both endometriosis and EAOC. Complement proteins are abundantly present in epithelial cells in both benign and malignant lesions. Mechanistic studies in ovarian surface epithelial (OSE) cells from mice with conditional (Cre-loxP) mutations show intrinsic production of complement in epithelia and demonstrate an early link between Kras- and Pten-driven pathways and complement upregulation. Downregulation of complement in these cells interferes with cell proliferation. Conclusions: These findings reveal new characteristics of inflammation in precursor lesions and point to previously unknown roles of complement in endometriosis and EAOC.
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Testing the Predictive Value of Peripheral Gene Expression for Nonremission Following Citalopram Treatment for Major Depression.
Neuropsychopharmacology
PUBLISHED: 09-01-2014
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Major depressive disorder (MDD) in general, and anxious-depression in particular, are characterized by poor rates of remission with first-line treatments, contributing to the chronic illness burden suffered by many patients. Prospective research is needed to identify the biomarkers predicting nonremission prior to treatment initiation. We collected blood samples from a discovery cohort of 34 adult MDD patients with co-occurring anxiety and 33 matched, nondepressed controls at baseline and after 12 weeks (of citalopram plus psychotherapy treatment for the depressed cohort). Samples were processed on gene arrays and group differences in gene expression were investigated. Exploratory analyses suggest that at pretreatment baseline, nonremitting patients differ from controls with gene function and transcription factor analyses potentially related to elevated inflammation and immune activation. In a second phase, we applied an unbiased machine learning prediction model and corrected for model-selection bias. Results show that baseline gene expression predicted nonremission with 79.4% corrected accuracy with a 13-gene model. The same gene-only model predicted nonremission after 8 weeks of citalopram treatment with 76% corrected accuracy in an independent validation cohort of 63 MDD patients treated with citalopram at another institution. Together, these results demonstrate the potential, but also the limitations, of baseline peripheral blood-based gene expression to predict nonremission after citalopram treatment. These results not only support their use in future prediction tools but also suggest that increased accuracy may be obtained with the inclusion of additional predictors (eg, genetics and clinical scales).Neuropsychopharmacology advance online publication, 1 October 2014; doi:10.1038/npp.2014.226.
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Bias correction for selecting the minimal-error classifier from many machine learning models.
Bioinformatics
PUBLISHED: 08-01-2014
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Supervised machine learning is commonly applied in genomic research to construct a classifier from the training data that is generalizable to predict independent testing data. When test datasets are not available, cross-validation is commonly used to estimate the error rate. Many machine learning methods are available, and it is well known that no universally best method exists in general. It has been a common practice to apply many machine learning methods and report the method that produces the smallest cross-validation error rate. Theoretically, such a procedure produces a selection bias. Consequently, many clinical studies with moderate sample sizes (e.g. n = 30-60) risk reporting a falsely small cross-validation error rate that could not be validated later in independent cohorts.
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Novel fusion transcripts associate with progressive prostate cancer.
Am. J. Pathol.
PUBLISHED: 05-16-2014
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The mechanisms underlying the potential for aggressive behavior of prostate cancer (PCa) remain elusive. In this study, whole genome and/or transcriptome sequencing was performed on 19 specimens of PCa, matched adjacent benign prostate tissues, matched blood specimens, and organ donor prostates. A set of novel fusion transcripts was discovered in PCa. Eight of these fusion transcripts were validated through multiple approaches. The occurrence of these fusion transcripts was then analyzed in 289 prostate samples from three institutes, with clinical follow-up ranging from 1 to 15 years. The analyses indicated that most patients [69 (91%) of 76] positive for any of these fusion transcripts (TRMT11-GRIK2, SLC45A2-AMACR, MTOR-TP53BP1, LRRC59-FLJ60017, TMEM135-CCDC67, KDM4-AC011523.2, MAN2A1-FER, and CCNH-C5orf30) experienced PCa recurrence, metastases, and/or PCa-specific death after radical prostatectomy. These outcomes occurred in only 37% (58/157) of patients without carrying those fusion transcripts. Three fusion transcripts occurred exclusively in PCa samples from patients who experienced recurrence or PCaerelated death. The formation of these fusion transcripts may be the result of genome recombination. A combination of these fusion transcripts in PCa with Gleason's grading or with nomogram significantly improves the prediction rate of PCa recurrence. Our analyses suggest that formation of these fusion transcripts may underlie the aggressive behavior of PCa.
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Missing value imputation in high-dimensional phenomic data: imputable or not, and how?
BMC Bioinformatics
PUBLISHED: 03-06-2014
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BackgroundIn modern biomedical research of complex diseases, a large number of demographic and clinical variables, herein called phenomic data, are often collected and missing values (MVs) are inevitable in the data collection process. Since many downstream statistical and bioinformatics methods require complete data matrix, imputation is a common and practical solution. In high-throughput experiments such as microarray experiments, continuous intensities are measured and many mature missing value imputation methods have been developed and widely applied. Numerous methods for missing data imputation of microarray data have been developed. Large phenomic data, however, contain continuous, nominal, binary and ordinal data types, which void application of most methods. Though several methods have been developed in the past few years, not a single complete guideline is proposed with respect to phenomic missing data imputation.ResultsIn this paper, we investigated existing imputation methods for phenomic data, proposed a self-training selection (STS) scheme to select the best imputation method and provide a practical guideline for general applications. We introduced a novel concept of ¿imputability measure¿ (IM) to identify missing values that are fundamentally inadequate to impute. In addition, we also developed four variations of K-nearest-neighbor (KNN) methods and compared with two existing methods, multivariate imputation by chained equations (MICE) and missForest. The four variations are imputation by variables (KNN-V), by subjects (KNN-S), their weighted hybrid (KNN-H) and an adaptively weighted hybrid (KNN-A). We performed simulations and applied different imputation methods and the STS scheme to three lung disease phenomic datasets to evaluate the methods. An R package ¿phenomeImpute¿ is made publicly available.ConclusionsSimulations and applications to real datasets showed that MICE often did not perform well; KNN-A, KNN-H and random forest were among the top performers although no method universally performed the best. Imputation of missing values with low imputability measures increased imputation errors greatly and could potentially deteriorate downstream analyses. The STS scheme was accurate in selecting the optimal method by evaluating methods in a second layer of missingness simulation. All source files for the simulation and the real data analyses are available on the author¿s publication website.
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Endpoints of Resuscitation: What Are They Anyway?
Semin Cardiothorac Vasc Anesth
PUBLISHED: 02-03-2014
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Hemodynamic optimization of surgical patients during and after surgery in the Surgical Intensive Care Unit is meant to improve outcomes. These outcomes have been measured by Length Of Stay (LOS), rate of infection, days on ventilator, etc. Unfortunately, the adaptation of modern technology to accomplish this has been slow in coming. Ever since Shoemaker described in 1988 using a pulmonary artery catheter (PAC) to guide fluid and inotropic administration to deliver supranormal tissue oxygenation, many authors have written about different techniques to achieve this "hemodynamic optimization". Since the PAC and CVC have both gone out of favor for utilization to monitor and improve hemodynamics, many clinicians have resorted using the easy to use static measurements of blood pressure (BP), heart rate (HR), and urine output. In this paper, the authors will review why these static measurements are no longer adequate and review some of the newer technology that have been studied and proven useful. This review of newer technologies combined with laboratory measurements that have also proven to help guide the clinician, may provide the impetus to adopt new strategies in the operating rooms (OR) and SICU.
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High fidelity copy number analysis of formalin-fixed and paraffin-embedded tissues using Affymetrix Cytoscan HD chip.
PLoS ONE
PUBLISHED: 01-01-2014
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Detection of human genome copy number variation (CNV) is one of the most important analyses in diagnosing human malignancies. Genome CNV detection in formalin-fixed and paraffin-embedded (FFPE) tissues remains challenging due to suboptimal DNA quality and failure to use appropriate baseline controls for such tissues. Here, we report a modified method in analyzing CNV in FFPE tissues using microarray with Affymetrix Cytoscan HD chips. Gel purification was applied to select DNA with good quality and data of fresh frozen and FFPE tissues from healthy individuals were included as baseline controls in our data analysis. Our analysis showed a 91% overlap between CNV detection by microarray with FFPE tissues and chromosomal abnormality detection by karyotyping with fresh tissues on 8 cases of lymphoma samples. The CNV overlap between matched frozen and FFPE tissues reached 93.8%. When the analyses were restricted to regions containing genes, 87.1% concordance between FFPE and fresh frozen tissues was found. The analysis was further validated by Fluorescence In Situ Hybridization on these samples using probes specific for BRAF and CITED2. The results suggested that the modified method using Affymetrix Cytoscan HD chip gave rise to a significant improvement over most of the previous methods in terms of accuracy in detecting CNV in FFPE tissues. This FFPE microarray methodology may hold promise for broad application of CNV analysis on clinical samples.
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A conserved BDNF, glutamate- and GABA-enriched gene module related to human depression identified by coexpression meta-analysis and DNA variant genome-wide association studies.
PLoS ONE
PUBLISHED: 01-01-2014
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Large scale gene expression (transcriptome) analysis and genome-wide association studies (GWAS) for single nucleotide polymorphisms have generated a considerable amount of gene- and disease-related information, but heterogeneity and various sources of noise have limited the discovery of disease mechanisms. As systematic dataset integration is becoming essential, we developed methods and performed meta-clustering of gene coexpression links in 11 transcriptome studies from postmortem brains of human subjects with major depressive disorder (MDD) and non-psychiatric control subjects. We next sought enrichment in the top 50 meta-analyzed coexpression modules for genes otherwise identified by GWAS for various sets of disorders. One coexpression module of 88 genes was consistently and significantly associated with GWAS for MDD, other neuropsychiatric disorders and brain functions, and for medical illnesses with elevated clinical risk of depression, but not for other diseases. In support of the superior discriminative power of this novel approach, we observed no significant enrichment for GWAS-related genes in coexpression modules extracted from single studies or in meta-modules using gene expression data from non-psychiatric control subjects. Genes in the identified module encode proteins implicated in neuronal signaling and structure, including glutamate metabotropic receptors (GRM1, GRM7), GABA receptors (GABRA2, GABRA4), and neurotrophic and development-related proteins [BDNF, reelin (RELN), Ephrin receptors (EPHA3, EPHA5)]. These results are consistent with the current understanding of molecular mechanisms of MDD and provide a set of putative interacting molecular partners, potentially reflecting components of a functional module across cells and biological pathways that are synchronously recruited in MDD, other brain disorders and MDD-related illnesses. Collectively, this study demonstrates the importance of integrating transcriptome data, gene coexpression modules and GWAS results for providing novel and complementary approaches to investigate the molecular pathology of MDD and other complex brain disorders.
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Peripheral blood mononuclear cell gene expression profiles predict poor outcome in idiopathic pulmonary fibrosis.
Sci Transl Med
PUBLISHED: 10-04-2013
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We aimed to identify peripheral blood mononuclear cell (PBMC) gene expression profiles predictive of poor outcomes in idiopathic pulmonary fibrosis (IPF) by performing microarray experiments of PBMCs in discovery and replication cohorts of IPF patients. Microarray analyses identified 52 genes associated with transplant-free survival (TFS) in the discovery cohort. Clustering the microarray samples of the replication cohort using the 52-gene outcome-predictive signature distinguished two patient groups with significant differences in TFS. We studied the pathways associated with TFS in each independent microarray cohort and identified decreased expression of "The costimulatory signal during T cell activation" Biocarta pathway and, in particular, the genes CD28, ICOS, LCK, and ITK, results confirmed by quantitative reverse transcription polymerase chain reaction (qRT-PCR). A proportional hazards model, including the qRT-PCR expression of CD28, ICOS, LCK, and ITK along with patients age, gender, and percent predicted forced vital capacity (FVC%), demonstrated an area under the receiver operating characteristic curve of 78.5% at 2.4 months for death and lung transplant prediction in the replication cohort. To evaluate the potential cellular source of CD28, ICOS, LCK, and ITK expression, we analyzed and found significant correlation of these genes with the PBMC percentage of CD4(+)CD28(+) T cells in the replication cohort. Our results suggest that CD28, ICOS, LCK, and ITK are potential outcome biomarkers in IPF and should be further evaluated for patient prioritization for lung transplantation and stratification in drug studies.
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Meta-analysis methods for combining multiple expression profiles: comparisons, statistical characterization and an application guideline.
BMC Bioinformatics
PUBLISHED: 08-17-2013
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As high-throughput genomic technologies become accurate and affordable, an increasing number of data sets have been accumulated in the public domain and genomic information integration and meta-analysis have become routine in biomedical research. In this paper, we focus on microarray meta-analysis, where multiple microarray studies with relevant biological hypotheses are combined in order to improve candidate marker detection. Many methods have been developed and applied in the literature, but their performance and properties have only been minimally investigated. There is currently no clear conclusion or guideline as to the proper choice of a meta-analysis method given an application; the decision essentially requires both statistical and biological considerations.
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Sex chromosome complement regulates expression of mood-related genes.
Biol Sex Differ
PUBLISHED: 07-31-2013
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Studies on major depressive and anxiety disorders suggest dysfunctions in brain corticolimbic circuits, including altered gamma-aminobutyric acid (GABA) and modulatory (serotonin and dopamine) neurotransmission. Interestingly, sexual dimorphisms in GABA, serotonin, and dopamine systems are also reported. Understanding the mechanisms behind these sexual dimorphisms may help unravel the biological bases of the heightened female vulnerability to mood disorders. Here, we investigate the contribution of sex-related factors (sex chromosome complement, developmental gonadal sex, or adult circulating hormones) to frontal cortex expression of selected GABA-, serotonin-, and dopamine-related genes.
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Whole-genome methylation sequencing reveals distinct impact of differential methylations on gene transcription in prostate cancer.
Am. J. Pathol.
PUBLISHED: 06-12-2013
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DNA methylation is one of the most important epigenetic mechanisms in regulating gene expression. Genome hypermethylation has been proposed as a critical mechanism in human malignancies. However, whole-genome quantification of DNA methylation of human malignancies has rarely been investigated, and the significance of the genome distribution of CpG methylation is unclear. We performed whole-genome methylation sequencing to investigate the methylation profiles of 13 prostate samples: 5 prostate cancers, 4 matched benign prostate tissues adjacent to tumor, and 4 age-matched organ-donor prostate tissues. Alterations of methylation patterns occurred in prostate cancer and in benign prostate tissues adjacent to tumor, in comparison with age-matched organ-donor prostates. More than 95% alterations of genome methylation occurred in sequences outside CpG islands. Only a small fraction of the methylated CpG islands had any effect on RNA expression. Both intragene and promoter CpG island methylations negatively affected gene expression. However, suppressions of RNA expression did not correlate with levels of CpG island methylation, suggesting that CpG island methylation alone might not be sufficient to shut down gene expression. Motif analysis revealed a consensus sequence containing Sp1 binding motif significantly enriched in the effective CpG islands.
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Considerations and pitfalls in phenotyping and reclassification of chronic obstructive pulmonary disease.
Transl Res
PUBLISHED: 04-20-2013
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As the clinical and research focus of chronic obstructive pulmonary disease (COPD) evolves from regarding obstructive lung disease as a single disease entity to recognizing the complexity of disease expression, the importance of COPD phenotyping rises to the forefront. The reclassification of COPD holds both prognostic and therapeutic implications but does not come without issues that may complicate classification efforts. In this review, we discuss the significance of refining the definition of the term phenotype, consider the impact of variations in cohort severity and attribute mix, account for the contrast of longitudinal vs cross-sectional cohort analysis, recognize the differing criteria used to define disease traits along with the nuances of combining cohorts, and identify the interaction of covariates as we advance in the field of COPD phenotyping.
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Metallothionein 1 h tumour suppressor activity in prostate cancer is mediated by euchromatin methyltransferase 1.
J. Pathol.
PUBLISHED: 01-29-2013
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Metallothioneins (MTs) are a group of metal binding proteins thought to play a role in the detoxification of heavy metals. Here we showed by microarray and validation analyses that MT1h, a member of MT, is down-regulated in many human malignancies. Low expression of MT1h was associated with poor clinical outcomes in both prostate and liver cancer. We found that the promoter region of MT1h was hypermethylated in cancer and that demethylation of the MT1h promoter reversed the suppression of MT1h expression. Forced expression of MT1h induced cell growth arrest, suppressed colony formation, retarded migration, and reduced invasion. SCID mice with tumour xenografts with inducible MT1h expression had lower tumour volumes as well as fewer metastases and deaths than uninduced controls. MT1h was found to interact with euchromatin histone methyltransferase 1 (EHMT1) and enhanced its methyltransferase activity on histone 3. Knocking down of EHMT1 or a mutation in MT1h that abrogates its interaction with EHMT1 abrogated MT1h tumour suppressor activity. This demonstrates tumour suppressor activity in a heavy metal binding protein that is dependent on activation of histone methylation.
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Genome-wide methylation analysis of prostate tissues reveals global methylation patterns of prostate cancer.
Am. J. Pathol.
PUBLISHED: 01-22-2013
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Altered genome methylation is a hallmark of human malignancies. In this study, high-throughput analyses of concordant gene methylation and expression events were performed for 91 human prostate specimens, including prostate tumor (T), matched normal adjacent to tumor (AT), and organ donor (OD). Methylated DNA in genomic DNA was immunoprecipitated with anti-methylcytidine antibodies and detected by Affymetrix human whole genome SNP 6.0 chips. Among the methylated CpG islands, 11,481 islands were found located in the promoter and exon 1 regions of 9295 genes. Genes (7641) were methylated frequently across OD, AT, and T samples, whereas 239 genes were differentially methylated in only T and 785 genes in both AT and T but not OD. Genes with promoter methylation and concordantly suppressed expression were identified. Pathway analysis suggested that many of the methylated genes in T and AT are involved in cell growth and mitogenesis. Classification analysis of the differentially methylated genes in T or OD produced a specificity of 89.4% and a sensitivity of 85.7%. The T and AT groups, however, were only slightly separated by the prediction analysis, indicating a strong field effect. A gene methylation prediction model was shown to predict prostate cancer relapse with sensitivity of 80.0% and specificity of 85.0%. These results suggest methylation patterns useful in predicting clinical outcomes of prostate cancer.
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The Role of Genetic Sex in Affect Regulation and Expression of GABA-Related Genes Across Species.
Front Psychiatry
PUBLISHED: 01-01-2013
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Although circulating hormones and inhibitory gamma-aminobutyric acid (GABA)-related factors are known to affect mood, considerable knowledge gaps persist for biological mechanisms underlying the female bias in mood disorders. Here, we combine human and mouse studies to investigate sexual dimorphism in the GABA system in the context of major depressive disorder (MDD) and then use a genetic model to dissect the role of sex-related factors in GABA-related gene expression and anxiety-/depressive-like behaviors in mice. First, using meta-analysis of gene array data in human postmortem brain (N?=?51 MDD subjects, 50 controls), we show that the previously reported down-regulation in MDD of somatostatin (SST), a marker of a GABA neuron subtype, is significantly greater in women with MDD. Second, using gene co-expression network analysis in control human subjects (N?=?214; two frontal cortex regions) and expression quantitative trait loci mapping (N?=?170 subjects), we show that expression of SST and the GABA-synthesizing enzymes glutamate decarboxylase 67 (GAD67) and GAD65 are tightly co-regulated and influenced by X-chromosome genetic polymorphisms. Third, using a rodent genetic model [Four Core Genotypes (FCG) mice], in which genetic and gonadal sex are artificially dissociated (N???12/group), we show that genetic sex (i.e., X/Y-chromosome) influences both gene expression (lower Sst, Gad67, Gad65 in XY mice) and anxiety-like behaviors (higher in XY mice). This suggests that in an intact male animal, the observed behavior represents the outcomes of male genetic sex increasing and male-like testosterone decreasing anxiety-like behaviors. Gonadal sex was the only factor influencing depressive-like behavior (gonadal males?
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Statistical analysis of electron transfer dissociation pairwise fragmentation patterns.
Anal. Chem.
PUBLISHED: 11-28-2011
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Electron transfer dissociation (ETD) is an alternative peptide dissociation method developed in recent years. Compared with the traditional collision induced dissociation (CID) b and y ion formation, ETD generates c and z ions and the backbone cleavage is believed to be less selective. We have reported previously the application of a statistical data mining strategy, K-means clustering, to discover fragmentation patterns for CID, and here we report application of this approach to ETD spectra. We use ETD data sets from digestions with three different proteases. Data analysis shows that selective cleavages do exist for ETD, with the fragmentation patterns affected by protease, charge states, and amino acid residue compositions. It is also noticed that the c(n-1) ion, corresponding to loss of the C-terminal amino acid residue, is statistically strong regardless of the residue at the C-terminus of the peptide, which suggests that the peptide gas phase conformation plays an important role in the dissociation pathways. These patterns provide a basis for mechanism elucidation, spectral prediction, and improvement of ETD peptide identification algorithms.
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MetaQC: objective quality control and inclusion/exclusion criteria for genomic meta-analysis.
Nucleic Acids Res.
PUBLISHED: 11-23-2011
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Genomic meta-analysis to combine relevant and homogeneous studies has been widely applied, but the quality control (QC) and objective inclusion/exclusion criteria have been largely overlooked. Currently, the inclusion/exclusion criteria mostly depend on ad-hoc expert opinion or naïve threshold by sample size or platform. There are pressing needs to develop a systematic QC methodology as the decision of study inclusion greatly impacts the final meta-analysis outcome. In this article, we propose six quantitative quality control measures, covering internal homogeneity of coexpression structure among studies, external consistency of coexpression pattern with pathway database, and accuracy and consistency of differentially expressed gene detection or enriched pathway identification. Each quality control index is defined as the minus log transformed P values from formal hypothesis testing. Principal component analysis biplots and a standardized mean rank are applied to assist visualization and decision. We applied the proposed method to 4 large-scale examples, combining 7 brain cancer, 9 prostate cancer, 8 idiopathic pulmonary fibrosis and 17 major depressive disorder studies, respectively. The identified problematic studies were further scrutinized for potential technical or biological causes of their lower quality to determine their exclusion from meta-analysis. The application and simulation results concluded a systematic quality assessment framework for genomic meta-analysis.
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Transcriptomic response of murine liver to severe injury and hemorrhagic shock: a dual-platform microarray analysis.
Physiol. Genomics
PUBLISHED: 08-09-2011
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Trauma-hemorrhagic shock (HS/T) is a complex process that elicits numerous molecular pathways. We hypothesized that a dual-platform microarray analysis of the liver, an organ that integrates immunology and metabolism, would reveal key pathways engaged following HS/T. C57BL/6 mice were divided into five groups (n = 4/group), anesthetized, and surgically treated to simulate a time course and trauma severity model: 1) nonmanipulated animals, 2) minor trauma, 3) 1.5 h of hemorrhagic shock and severe trauma (HS/T), 4) 1.5 h HS/T followed by 1 h resuscitation (HS/T+1.0R), 5) 1.5 h HS/T followed by 4.5 h resuscitation (HS/T+4.5R). Liver RNA was hybridized to CodeLink and Affymetrix mouse whole genome microarray chips. Common genes with a cross-platform correlation >0.6 (2,353 genes in total) were clustered using k-means clustering, and clusters were analyzed using Ingenuity Pathways Analysis. Genes involved in the stress response and immunoregulation were upregulated early and remained upregulated throughout the course of the experiment. Genes involved in cell death and inflammatory pathways were upregulated in a linear fashion with elapsed time and in severe injury compared with minor trauma. Three of the six clusters contained genes involved in metabolic function; these were downregulated with elapsed time. Transcripts involved in amino acid metabolism as well as signaling pathways associated with glucocorticoid receptors, IL-6, IL-10, and the acute phase response were elevated in a severity-dependent manner. This is the first study to examine the postinjury response using dual-platform microarray analysis, revealing responses that may enable novel therapies or diagnostics.
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An immunohistochemical panel to differentiate metastatic breast carcinoma to skin from primary sweat gland carcinomas with a review of the literature.
Arch. Pathol. Lab. Med.
PUBLISHED: 08-04-2011
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Approximately 25% of patients with breast cancer develop cutaneous metastases. Sweat gland carcinomas (SGCs) account for about 0.05% of all cutaneous neoplasms. Cutaneous metastases of breast carcinoma (CMBCs) (especially the ductal type) can be difficult to distinguish from SGCs. Treatment and prognoses for these 2 types of tumors differ radically, making accurate histologic diagnosis crucial. Although a few studies attempt to differentiate these entities employing immunohistochemical (IHC) studies (some of which we review here), to date, no panel of IHC stains exists, to our knowledge, to distinguish these entities.
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A combined molecular-pathologic score improves risk stratification of thyroid papillary microcarcinoma.
Cancer
PUBLISHED: 05-04-2011
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Thyroid papillary microcarcinoma (TPMC) is an incidentally discovered papillary carcinoma that measures ?1.0 cm in size. Most TPMCs are indolent, whereas some behave aggressively. The objective of the study was to evaluate whether the combination of v-raf murine sarcoma viral oncogene homolog B1 (BRAF) mutation and specific histopathologic features allows risk stratification of TPMC.
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Biological impact of missing-value imputation on downstream analyses of gene expression profiles.
Bioinformatics
PUBLISHED: 11-02-2010
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Microarray experiments frequently produce multiple missing values (MVs) due to flaws such as dust, scratches, insufficient resolution or hybridization errors on the chips. Unfortunately, many downstream algorithms require a complete data matrix. The motivation of this work is to determine the impact of MV imputation on downstream analysis, and whether ranking of imputation methods by imputation accuracy correlates well with the biological impact of the imputation.
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Module-based prediction approach for robust inter-study predictions in microarray data.
Bioinformatics
PUBLISHED: 08-17-2010
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Traditional genomic prediction models based on individual genes suffer from low reproducibility across microarray studies due to the lack of robustness to expression measurement noise and gene missingness when they are matched across platforms. It is common that some of the genes in the prediction model established in a training study cannot be matched to another test study because a different platform is applied. The failure of inter-study predictions has severely hindered the clinical applications of microarray. To overcome the drawbacks of traditional gene-based prediction (GBP) models, we propose a module-based prediction (MBP) strategy via unsupervised gene clustering.
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Meta-analysis for pathway enrichment analysis when combining multiple genomic studies.
Bioinformatics
PUBLISHED: 04-21-2010
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Many pathway analysis (or gene set enrichment analysis) methods have been developed to identify enriched pathways under different biological states within a genomic study. As more and more microarray datasets accumulate, meta-analysis methods have also been developed to integrate information among multiple studies. Currently, most meta-analysis methods for combining genomic studies focus on biomarker detection and meta-analysis for pathway analysis has not been systematically pursued.
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Clinical importance of histologic grading of lobular carcinoma in situ in breast core needle biopsy specimens: current issues and controversies.
Am. J. Clin. Pathol.
PUBLISHED: 04-17-2010
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Lobular carcinoma in situ (LCIS) is considered a risk factor for development of invasive carcinoma (IC). Many variants of LCIS have been described based on pathologic features such as nuclear grade, pleomorphism, and necrosis, but little is known about the biology of these variants. The proposed 3-tier grading system for LCIS has not been validated or endorsed across laboratories. We found significant upstaging of pure pleomorphic LCIS (LCIS with nuclear grade [NG] 3), up to 25% in core needle biopsy (CNB) specimens, in an earlier study. The aim of the current study was to address the importance of pure classical LCIS (NGs 1 and 2) in CNB specimens along with clinicopathologic follow-up. In follow-up resection specimens, IC or ductal carcinoma in situ was seen in 18% (7/39), a high incidence of residual LCIS was seen in 69% (27/39), and other high-risk lesions, such as atypical ductal hyperplasia, were seen in 36% (14/39) of LCIS NG 2 cases. Our study illustrates the importance of grading LCIS; we recommend follow-up excision in LCIS NG 2 cases owing to a high incidence of residual LCIS and the likelihood of identifying other high-risk lesions.
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RNA interference against hepatic epidermal growth factor receptor has suppressive effects on liver regeneration in rats.
Am. J. Pathol.
PUBLISHED: 04-15-2010
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Liver regeneration after a two-thirds partial hepatectomy (PHx) is a complex process requiring interaction and cooperation of many growth factors and cytokines and cross talk between multiple pathways. Along with hepatocyte growth factor and its receptor MET (HGF-MET), the epidermal growth factor receptor (EGFR) signaling pathway is activated within 60 minutes after PHx. To investigate the role of EGFR in liver regeneration, we used two EGFR-specific short hairpin silencing RNAs to inhibit EGFR expression in regenerating normal rat liver. Suppression of EGFR mRNA and protein was evident in treated rats. There was also a demonstrable decrease but not complete elimination of bromo-deoxyuridine incorporation and mitoses at 24 hours after PHx. In addition, we observed up-regulation of MET and Src as well as activation of the ErbB-3-ErbB-2-PI3K-Akt pathway and down-regulation of STAT 3, cyclin D1, cyclin E1, p21, and C/EBP beta. The decrease in the ratio of C/EBP alpha to C/EBP beta known to occur after PHx was offset in shEGFR-treated rats. Despite suppression of hepatocyte proliferation lasting into day 3 after PHx, liver weight restoration occurred. Interestingly, hepatocytes in shEGFR-treated rats were considerably larger when compared with ScrRNA-treated controls. The data indicate that although the MET and EGFR pathways are similar, the contributions made by MET and EGFR are unique and are not compensated by each other or other cytokines.
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Quantile map: simultaneous visualization of patterns in many distributions with application to tandem mass spectrometry.
Comput Stat Data Anal
PUBLISHED: 02-18-2010
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High-throughput experiments have become more and more prevalent in biomedical research. The high-dimensional data have brought new challenges. Effective data reduction, summarization and visualization are important keys to initial exploration in the data mining. In this paper, we introduce a visualization tool, namely quantile map, to present information contained in a probabilistic distribution. We demonstrate its use as an effective visual analysis tool through the application of a tandem mass spectrometry data set. Information of quantiles of a distribution is presented in gradient colors by concentric doughnuts. The width of the doughnuts is proportional to the Fisher information of the distribution to present unbiased visualization effect. A parametric empirical Bayes (PEB) approach is shown to improve the simple maximum likelihood estimate (MLE) approach when estimating the Fisher information. In the motivating example from tandem mass spectrometry data, multiple probabilistic distributions are to be displayed in two-dimensional grids. A hierarchical clustering to reorder rows and columns and a gradient color selection from a Hue-Chroma-Luminance model, similar to that commonly applied in heatmaps of microarray analysis, are adopted to improve the visualization. Both simulations and the motivating example show superior performance of quantile map in summarization and visualization of such high-throughput data sets.
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Biomarker detection in the integration of multiple multi-class genomic studies.
Bioinformatics
PUBLISHED: 12-04-2009
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Systematic information integration of multiple-related microarray studies has become an important issue as the technology becomes mature and prevalent in the past decade. The aggregated information provides more robust and accurate biomarker detection. So far, published meta-analysis methods for this purpose mostly consider two-class comparison. Methods for combining multi-class studies and considering expression pattern concordance are rarely explored.
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Biomarker profile in breast carcinomas presenting with bone metastasis.
Int J Clin Exp Pathol
PUBLISHED: 08-12-2009
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Bone is the most preferred site for metastatic dissemination in breast cancer. The purpose of this study was to examine the expression of a set of antibodies that could serve as predictive biomarkers associated with breast cancer metastasis in a subset of sixteen (16) breast cancer patients who developed bone metastasis. The clinical and pathologic data were obtained, and tissue microarrays were constructed. Tissue microarray slides were stained for TFF-1, CXRC4, MMP1, PTHrP, HER2, CD44, FGFR3 and IL-11. The expression rates were compared between the metastatic breast cancer to bone (MBC-B) group and a group of sixty-four (64) primary breast cancer (PBC). The results demonstrated that MBC-B group patients were more likely to be HER2 positive (P = 0.016). There was no significant difference on estrogen receptor or progesterone receptor expression between MBC-B group and PBC group (P > 0.05). There was a high expression of CXCR4, MMP-1, CD44, TFF-1, PTHrP, FGFR3 and IL-11, in both, PBC and MBC-B, and no significant differences between the groups were identified. We found that tumors associated with bone metastasis tended to be larger than 2 cm. The high morbidity associated to metastatic breast cancer prompts the identification of predictive biomarkers of relapse of breast tumors to categorize patients at high risk of bone metastasis and serve as targeted therapy.
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A molecular signature of depression in the amygdala.
Am J Psychiatry
PUBLISHED: 07-15-2009
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Major depressive disorder is a heterogeneous illness with a mostly uncharacterized pathology. Recent gene array attempts to identify the molecular underpinnings of the illness in human postmortem subjects have not yielded a consensus. The authors hypothesized that controlling several sources of clinical and technical variability and supporting their analysis with array results from a parallel study in the unpredictable chronic mild stress (UCMS) rodent model of depression would facilitate identification of the molecular pathology of major depression.
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Clinicopathologic implications of "flat epithelial atypia" in core needle biopsy specimens of the breast.
Am. J. Clin. Pathol.
PUBLISHED: 05-23-2009
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Flat epithelial atypia (FEA) is an emerging entity of uncertain clinical significance, and outcome data are sparse. The aim of this study was to evaluate the clinicopathologic significance of this entity for proper management. All core needle biopsy (CNB) specimens diagnosed as atypical ductal hyperplasia (ADH) from January 2006 to April 2008 were retrieved. H&E-stained slides of 5 levels on each case were reviewed. The differences in upstaging in subsequent excisions in the FEA and ADH group (31/189 [16.4%]) vs the pure FEA group (5/35 [14%]) and pure FEA (5/35 [14%]) vs pure ADH (5/45 [11%]) were not statistically significant. We observed that FEA evolved into ADH at the same site at an average of 3 to 4 levels. Our study concludes that there is an association of FEA with ADH on multiple levels of CNB specimens, and follow-up surgical excision findings for FEA are clinically significant.
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Ratio adjustment and calibration scheme for gene-wise normalization to enhance microarray inter-study prediction.
Bioinformatics
PUBLISHED: 05-04-2009
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Reproducibility analyses of biologically relevant microarray studies have mostly focused on overlap of detected biomarkers or correlation of differential expression evidences across studies. For clinical utility, direct inter-study prediction (i.e. to establish a prediction model in one study and apply to another) for disease diagnosis or prognosis prediction is more important. Normalization plays a key role for such a task. Traditionally, sample-wise normalization has been a standard for inter-array and inter-study normalization. For gene-wise normalization, it has been implemented for intra-study or inter-study predictions in a few papers while its rationale, strategy and effect remain unexplored.
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Characterization of high-grade ductal carcinoma in situ with and without regressive changes: diagnostic and biologic implications.
Appl. Immunohistochem. Mol. Morphol.
PUBLISHED: 05-02-2009
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Regressive changes (RC) have been described in malignant melanoma, carcinomas of the prostate and cervix. The presence of RC in these neoplasms may signify some degree of host response to tumor and seems to be a sign of poor prognosis for some neoplasms. RC in breast cancer is vaguely defined in the older literature. We have observed periodically similar RC in a subset of high-grade ductal carcinoma in situ (HGDCIS) in breast specimens. The aim of our study is to demonstrate how to recognize RC in the diagnostic setting and an attempt to understand the biologic behavior in this subset of HGDCIS cases. Fifty-nine cases of HG-DCIS (35 cases with RC and 24 cases without RC) were included. We defined RC in our study as demonstrating thick periductal fibrosis, dense lymphocytic infiltrate, and a thin rim of intact neoplastic cells. A short panel of immunomarkers to study this entity included myoepithelial markers. Reduced expression of myoepithelial markers (p63 and smooth muscle heavy chain myosin) were seen more frequently in the HGDCIS group with RC than without RC cases. Invasion as well as metastatic disease was seen in association with HGDCIS with RC nearly 4 times as often. It is also critically important to recognize HGDCIS-RC for diagnostic purposes, as the differential diagnosis of RC includes, benign associations such as papilloma, fibrocystic changes and periductal mastitis. HGDCIS-RC may also be a sign of an aggressive phenotype than other HGDCIS subtypes. Further outcome studies are necessary to determine if it has a clinical impact akin to other tumors with RC.
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Investigating Multi-cancer Biomarkers and Their Cross-predictability in the Expression Profiles of Multiple Cancer Types.
Biomark Insights
PUBLISHED: 05-01-2009
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Microarray technology has been widely applied to the analysis of many malignancies, however, integrative analyses across multiple studies are rarely investigated. In this study we performed a meta-analysis on the expression profiles of four published studies analyzing organ donor, benign tissues adjacent to tumor and tumor tissues from liver, prostate, lung and bladder samples. We identified 99 distinct multi-cancer biomarkers in the comparison of all three tissues in liver and prostate and 44 in the comparison of normal versus tumor in liver, prostate and lung. The bladder samples appeared to have a different list of biomarkers from the other three cancer types. The identified multi-cancer biomarkers achieved high accuracy similar to using whole genome in the within-cancer-type prediction. They also performed superior than the one using whole genome in inter-cancer-type prediction. To test the validity of the multi-cancer biomarkers, 23 independent prostate cancer samples were evaluated and 96% accuracy was achieved in inter-study prediction from the original prostate, liver and lung cancer data sets respectively. The result suggests that the compact lists of multi-cancer biomarkers are important in cancer development and represent the common signatures of malignancies of multiple cancer types. Pathway analysis revealed important tumorogenesis functional categories.
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Reciprocal phylogenetic conservation of molecular aging in mouse and human brain.
Neurobiol. Aging
PUBLISHED: 02-10-2009
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Studies of age-related molecular profiles have separately focused on the human and rodent brains, but the extent to which each organism predicts molecular events across species for the global signature of aging and for specific biological functions has only begun to be characterized. We previously showed that the molecular correlates of aging in the mouse cortex moderately, but significantly, predicted transcript changes in human frontal cortex. Using orthologous gene links between large-scale gene expression datasets, we now report a similar reciprocal human-to-mouse prediction of molecular aging in frontal cortex, but a limited and variable conservation of age-effects across a wide spectrum of biological functions. Thus, the moderate transcriptome correlations and partial functional concordance between late-life human and rodent cohorts (13-77 years in humans and 3-24 months in mice) suggest limitations of the mouse to model normal aging of the human brain cortex.
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An R package suite for microarray meta-analysis in quality control, differentially expressed gene analysis and pathway enrichment detection.
Bioinformatics
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With the rapid advances and prevalence of high-throughput genomic technologies, integrating information of multiple relevant genomic studies has brought new challenges. Microarray meta-analysis has become a frequently used tool in biomedical research. Little effort, however, has been made to develop a systematic pipeline and user-friendly software. In this article, we present MetaOmics, a suite of three R packages MetaQC, MetaDE and MetaPath, for quality control, differentially expressed gene identification and enriched pathway detection for microarray meta-analysis. MetaQC provides a quantitative and objective tool to assist study inclusion/exclusion criteria for meta-analysis. MetaDE and MetaPath were developed for candidate marker and pathway detection, which provide choices of marker detection, meta-analysis and pathway analysis methods. The system allows flexible input of experimental data, clinical outcome (case-control, multi-class, continuous or survival) and pathway databases. It allows missing values in experimental data and utilizes multi-core parallel computing for fast implementation. It generates informative summary output and visualization plots, operates on different operation systems and can be expanded to include new algorithms or combine different types of genomic data. This software suite provides a comprehensive tool to conveniently implement and compare various genomic meta-analysis pipelines.
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Distinct genes related to drug response identified in ER positive and ER negative breast cancer cell lines.
PLoS ONE
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Breast cancer patients have different responses to chemotherapeutic treatments. Genes associated with drug response can provide insight to understand the mechanisms of drug resistance, identify promising therapeutic opportunities, and facilitate personalized treatment. Estrogen receptor (ER) positive and ER negative breast cancer have distinct clinical behavior and molecular properties. However, to date, few studies have rigorously assessed drug response genes in them. In this study, our goal was to systematically identify genes associated with multidrug response in ER positive and ER negative breast cancer cell lines. We tested 27 human breast cell lines for response to seven chemotherapeutic agents (cyclophosphamide, docetaxel, doxorubicin, epirubicin, fluorouracil, gemcitabine, and paclitaxel). We integrated publicly available gene expression profiles of these cell lines with their in vitro drug response patterns, then applied meta-analysis to identify genes related to multidrug response in ER positive and ER negative cells separately. One hundred eighty-eight genes were identified as related to multidrug response in ER positive and 32 genes in ER negative breast cell lines. Of these, only three genes (DBI, TOP2A, and PMVK) were common to both cell types. TOP2A was positively associated with drug response, and DBI was negatively associated with drug response. Interestingly, PMVK was positively associated with drug response in ER positive cells and negatively in ER negative cells. Functional analysis showed that while cell cycle affects drug response in both ER positive and negative cells, most biological processes that are involved in drug response are distinct. A number of signaling pathways that are uniquely enriched in ER positive cells have complex cross talk with ER signaling, while in ER negative cells, enriched pathways are related to metabolic functions. Taken together, our analysis indicates that distinct mechanisms are involved in multidrug response in ER positive and ER negative breast cells.
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Genome abnormalities precede prostate cancer and predict clinical relapse.
Am. J. Pathol.
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The prediction of prostate cancer clinical outcome remains a major challenge after the diagnosis, even with improved early detection by prostate-specific antigen (PSA) monitoring. To evaluate whether copy number variation (CNV) of the genomes in prostate cancer tumor, in benign prostate tissues adjacent to the tumor (AT), and in the blood of patients with prostate cancer predicts biochemical (PSA) relapse and the kinetics of relapse, 241 samples (104 tumor, 49 matched AT, 85 matched blood, and 3 cell lines) were analyzed using Affymetrix SNP 6.0 chips. By using gene-specific CNV from tumor, the genome model correctly predicted 73% (receiver operating characteristic P = 0.003) cases for relapse and 75% (P < 0.001) cases for short PSA doubling time (PSADT, <4 months). The gene-specific CNV model from AT correctly predicted 67% (P = 0.041) cases for relapse and 77% (P = 0.015) cases for short PSADT. By using median-sized CNV from blood, the genome model correctly predicted 81% (P < 0.001) cases for relapse and 69% (P = 0.001) cases for short PSADT. By using median-sized CNV from tumor, the genome model correctly predicted 75% (P < 0.001) cases for relapse and 80% (P < 0.001) cases for short PSADT. For the first time, our analysis indicates that genomic abnormalities in either benign or malignant tissues are predictive of the clinical outcome of a malignancy.
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Detecting disease-associated genes with confounding variable adjustment and the impact on genomic meta-analysis: with application to major depressive disorder.
BMC Bioinformatics
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Detecting candidate markers in transcriptomic studies often encounters difficulties in complex diseases, particularly when overall signals are weak and sample size is small. Covariates including demographic, clinical and technical variables are often confounded with the underlying disease effects, which further hampers accurate biomarker detection. Our motivating example came from an analysis of five microarray studies in major depressive disorder (MDD), a heterogeneous psychiatric illness with mostly uncharacterized genetic mechanisms.
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Gene deletions and amplifications in human hepatocellular carcinomas: correlation with hepatocyte growth regulation.
Am. J. Pathol.
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Tissues from 98 human hepatocellular carcinomas (HCCs) obtained from hepatic resections were subjected to somatic copy number variation (CNV) analysis. Most of these HCCs were discovered in livers resected for orthotopic transplantation, although in a few cases, the tumors themselves were the reason for the hepatectomies. Genomic analysis revealed deletions and amplifications in several genes, and clustering analysis based on CNV revealed five clusters. The LSP1 gene had the most cases with CNV (46 deletions and 5 amplifications). High frequencies of CNV were also seen in PTPRD (21/98), GNB1L (18/98), KIAA1217 (18/98), RP1-1777G6.2 (17/98), ETS1 (11/98), RSU1 (10/98), TBC1D22A (10/98), BAHCC1 (9/98), MAML2 (9/98), RAB1B (9/98), and YIF1A (9/98). The existing literature regarding hepatocytes or other cell types has connected many of these genes to regulation of cytoskeletal architecture, signaling cascades related to growth regulation, and transcription factors directly interacting with nuclear signaling complexes. Correlations with existing literature indicate that genomic lesions associated with HCC at the level of resolution of CNV occur on many genes associated directly or indirectly with signaling pathways operating in liver regeneration and hepatocyte growth regulation.
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Comprehensive literature review and statistical considerations for microarray meta-analysis.
Nucleic Acids Res.
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With the rapid advances of various high-throughput technologies, generation of -omics data is commonplace in almost every biomedical field. Effective data management and analytical approaches are essential to fully decipher the biological knowledge contained in the tremendous amount of experimental data. Meta-analysis, a set of statistical tools for combining multiple studies of a related hypothesis, has become popular in genomic research. Here, we perform a systematic search from PubMed and manual collection to obtain 620 genomic meta-analysis papers, of which 333 microarray meta-analysis papers are summarized as the basis of this paper and the other 249 GWAS meta-analysis papers are discussed in the next companion paper. The review in the present paper focuses on various biological purposes of microarray meta-analysis, databases and software and related statistical procedures. Statistical considerations of such an analysis are further scrutinized and illustrated by a case study. Finally, several open questions are listed and discussed.
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Comprehensive literature review and statistical considerations for GWAS meta-analysis.
Nucleic Acids Res.
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Over the last decade, genome-wide association studies (GWAS) have become the standard tool for gene discovery in human disease research. While debate continues about how to get the most out of these studies and on occasion about how much value these studies really provide, it is clear that many of the strongest results have come from large-scale mega-consortia and/or meta-analyses that combine data from up to dozens of studies and tens of thousands of subjects. While such analyses are becoming more and more common, statistical methods have lagged somewhat behind. There are good meta-analysis methods available, but even when they are carefully and optimally applied there remain some unresolved statistical issues. This article systematically reviews the GWAS meta-analysis literature, highlighting methodology and software options and reviewing methods that have been used in real studies. We illustrate differences among methods using a case study. We also discuss some of the unresolved issues and potential future directions.
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What is Visualize?

JoVE Visualize is a tool created to match the last 5 years of PubMed publications to methods in JoVE's video library.

How does it work?

We use abstracts found on PubMed and match them to JoVE videos to create a list of 10 to 30 related methods videos.

Video X seems to be unrelated to Abstract Y...

In developing our video relationships, we compare around 5 million PubMed articles to our library of over 4,500 methods videos. In some cases the language used in the PubMed abstracts makes matching that content to a JoVE video difficult. In other cases, there happens not to be any content in our video library that is relevant to the topic of a given abstract. In these cases, our algorithms are trying their best to display videos with relevant content, which can sometimes result in matched videos with only a slight relation.