Sample size calculation for microarray experiments with blocked one-way design

<p>Abstract</p> <p>Background</p> <p>One of the main objectives of microarray analysis is to identify differentially expressed genes for different types of cells or treatments. Many statistical methods have been proposed to assess the treatment effects in microarray experiments.</p> <p>Results</p> <p>In this paper, we consider discovery of the genes that are differentially expressed among <it>K </it>(> 2) treatments when each set of <it>K </it>arrays consists of a block. In this case, the array data among <it>K </it>treatments tend to be correlated because of block effect. We propose to use the blocked one-way ANOVA <it>F</it>-statistic to test if each gene is differentially expressed among <it>K </it>treatments. The marginal p-values are calculated using a permutation method accounting for the block effect, adjusting for the multiplicity of the testing procedure by controlling the false discovery rate (FDR). We propose a sample size calculation method for microarray experiments with a blocked one-way design. With FDR level and effect sizes of genes specified, our formula provides a sample size for a given number of true discoveries.</p> <p>Conclusion</p> <p>The calculated sample size is shown via simulations to provide an accurate number of true discoveries while controlling the FDR at the desired level.</p

Gene-Expression Signatures Can Distinguish Gastric Cancer Grades and Stages

Microarray gene-expression data of 54 paired gastric cancer and adjacent noncancerous gastric tissues were analyzed, with the aim to establish gene signatures for cancer grades (well-, moderately-, poorly- or un-differentiated) and stages (I, II, III and IV), which have been determined by pathologists. Our statistical analysis led to the identification of a number of gene combinations whose expression patterns serve well as signatures of different grades and different stages of gastric cancer. A 19-gene signature was found to have discerning power between high- and low-grade gastric cancers in general, with overall classification accuracy at 79.6%. An expanded 198-gene panel allows the stratification of cancers into four grades and control, giving rise to an overall classification agreement of 74.2% between each grade designated by the pathologists and our prediction. Two signatures for cancer staging, consisting of 10 genes and 9 genes, respectively, provide high classification accuracies at 90.0% and 84.0%, among early-, advanced-stage cancer and control. Functional and pathway analyses on these signature genes reveal the significant relevance of the derived signatures to cancer grades and progression. To the best of our knowledge, this represents the first study on identification of genes whose expression patterns can serve as markers for cancer grades and stages

Global metabolomic profiling of uterine leiomyomas

Background: Uterine leiomyomas can be classified into molecularly distinct subtypes according to their genetic triggers: MED12 mutations, HMGA2 upregulation, or inactivation of FH. The aim of this study was to identify metabolites and metabolic pathways that are dysregulated in different subtypes of leiomyomas. Methods: We performed global metabolomic profiling of 25 uterine leiomyomas and 17 corresponding myometrium specimens using liquid chromatography-tandem mass spectroscopy. Results: A total of 641 metabolites were detected. All leiomyomas displayed reduced homocarnosine and haeme metabolite levels. We identified a clearly distinct metabolomic profile for leiomyomas of the FH subtype, characterised by metabolic alterations in the tricarboxylic acid cycle and pentose phosphate pathways, and increased levels of multiple lipids and amino acids. Several metabolites were uniquely elevated in leiomyomas of the FH subtype, including N6-succinyladenosine and argininosuccinate, serving as potential biomarkers for FH deficiency. In contrast, leiomyomas of the MED12 subtype displayed reduced levels of vitamin A, multiple membrane lipids and amino acids, and dysregulation of vitamin C metabolism, a finding which was also compatible with gene expression data. Conclusions: The study reveals the metabolomic heterogeneity of leiomyomas and provides the requisite framework for strategies designed to target metabolic alterations promoting the growth of these prevalent tumours.Peer reviewe

Profiling and Functional Analyses of MicroRNAs and Their Target Gene Products in Human Uterine Leiomyomas

Human uterine leiomyomas (ULM) are characterized by dysregulation of a large number of genes and non-coding regulatory microRNAs. In order to identify microRNA::mRNA associations relevant to ULM pathogenesis, we examined global correlation patterns between the altered microRNA expression and the predicted target genes in ULMs and matched myometria.Patterns of inverse association of microRNA with mRNA expression in ULMs revealed an involvement of multiple candidate pathways, including extensive transcriptional reprogramming, cell proliferation control, MAP kinase, TGF-beta, WNT, JAK/STAT signaling, remodeling of cell adhesion, and cell-cell and cell-matrix contacts. We further examined the correlation between the expression of the selected target gene protein products and microRNAs in thirty-six paired sets of leiomyomas and matched myometria. We found that a number of dysregulated microRNAs were inversely correlated with their targets at the protein level. The comparative genomic hybridization (CGH) in eight ULM patients revealed that partially shared deletions of two distinct chromosomal regions might be responsible for loss of cancer-associated microRNA expression and could thus contribute to the ULM pathogenesis via deregulation of target mRNAs. Last, we functionally tested the repressor effects of selected cancer-related microRNAs on their predicted target genes in vitro.We found that some but not all of the predicted and inversely correlated target genes in ULMs can be directly regulated by microRNAs in vitro. Our findings provide a broad overview of molecular events underlying the tumorigenesis of uterine ULMs and identify select genetic and regulatory events that alter microRNA expression and may play important roles in ULM pathobiology by positively regulating tumor growth while maintaining the non-invasive character of ULMs

Molecular classification of selective oestrogen receptor modulators on the basis of gene expression profiles of breast cancer cells expressing oestrogen receptor α

The purpose of this study was to classify selective oestrogen receptor modulators based on gene expression profiles produced in breast cancer cells expressing either wtERα or mutant351ERα. In total, 54 microarray experiments were carried out by using a commercially available Atlas cDNA Expression Arrays (Clontech), containing 588 cancer-related genes. Nine sets of data were generated for each cell line following 24 h of treatment: expression data were obtained for cells treated with vehicle EtOH (Control); with 10−9 or 10−8 M oestradiol; with 10−6 M 4-hydroxytamoxifen; with 10−6 M raloxifene; with 10−6 M idoxifene, with 10−6 M EM 652, with 10−6 M GW 7604; with 5×10−5 M resveratrol and with 10−6 M ICI 182,780. We developed a new algorithm ‘Expression Signatures’ to classify compounds on the basis of differential gene expression profiles. We created dendrograms for each cell line, in which branches represent relationships between compounds. Additionally, clustering analysis was performed using different subsets of genes to assess the robustness of the analysis. In general, only small differences between gene expression profiles treated with compounds were observed with correlation coefficients ranged from 0.83 to 0.98. This observation may be explained by the use of the same cell context for treatments with compounds that essentially belong to the same class of drugs with oestrogen receptors related mechanisms. The most surprising observation was that ICI 182,780 clustered together with oestrodiol and raloxifene for cells expressing wtERα and clustered together with EM 652 for cells expressing mutant351ERα. These data provide a rationale for a more precise and elaborate study in which custom made oligonucleotide arrays can be used with comprehensive sets of genes known to have consensus and putative oestrogen response elements in their promoter regions