scholarly journals Comprehensive tissue-specific gene set enrichment analysis and transcription factor analysis of breast cancer by integrating 14 gene expression datasets

Oncotarget ◽  
2016 ◽  
Vol 8 (4) ◽  
pp. 6775-6786 ◽  
Author(s):  
Wen-Xing Li ◽  
Kan He ◽  
Ling Tang ◽  
Shao-Xing Dai ◽  
Gong-Hua Li ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Factor Analysis ◽  
Transcription Factor ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Specific Gene ◽  
Gene Set Enrichment ◽  
Tissue Specific ◽  
Tissue Specific Gene

Gene expression combined with gene set enrichment analysis to identify markers of vinorelbine efficacy in breast cancer patients.

2012 ◽  
Vol 30 (15_suppl) ◽  
pp. e21099-e21099
Author(s):  
Robert Audet ◽  
Changyu Shen ◽  
Scooter Willis ◽  
Renata Duchnowska ◽  
Krzysztof Adamowicz ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Transcription Factor ◽  
Chromosomal Location ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Molecular Function ◽  
Time To Progression ◽  
Gene Set Enrichment ◽  
Histone Binding

e21099 Background: Vinorelbine (V) induces mitotic arrest and apoptosis but there are limited data on its effect on gene expression in breast cancer clinical setting. Methods: 43 adult female patients with pathologically confirmed breast cancer and locally advanced or metastatic disease were treated with V 25 mg/m2 days 1, 8, 15 of a 28-day cycle. Gene expression was assessed in archival FFPE tissue using the microarray-based DASL assay (cDNA-mediated Annealing, Selection extension and Ligation) and correlated with time-to-progression (TTP). Using a Gene Set Enrichment Analysis (GSEA), groups of genes that share a common molecular function, chromosomal location, or regulation were identified in patients classified as having either a short (S) (n=25) or a long (L) (n=18) time to progression (TTP) divided by the median (72 days). The GSEA software ( http://www.broadinstitute.org/gsea/index.jsp ) was used for the analysis. Results: GSEA focusing on genes grouped according to similar a) molecular function: 16 out of a set of 43 genes involved in histone binding were enriched in group S (p = 0.002), consistent with higher expression in group S of HIST3H2BB and HIST1H3I as well as a nuclear transcription factor promoting their expression. b) transcription factors: 14 out of 47 genes were enriched in group S (p = 0.004) and corresponds to genes with promoter regions that match c-fos serum response element-binding transcription factor that modulates, for example, ABCC1 and ABCB1 (P-gp/MDR1) solute carriers. c) chromosomal location: in group S, genes were enriched on chromosome 11q21 (20 out of 45 genes p = 0.004) and on chromosome 12p12 (14 out of 22 genes p = 0.002). Conclusions: a) the up-regulation of histone binding genes is consonant with recent discovery of high affinity V binding to histones b) the role of P-gp/MDR1 in V transport is well known c) our observations on chromosome 11q21 and12p12 are novel. DASL expression combined with GSEA highlights gene sets that correlate with clinical outcome and may lead to predictive markers of V efficacy. Further confirmatory analysis is needed due to the limitation of small sample size and multiple comparisons.

scholarly journals A Comparative Study of Mouse Hepatic and Intestinal Gene Expression Profiles under PPARαKnockout by Gene Set Enrichment Analysis

PPAR Research ◽  
2011 ◽  
Vol 2011 ◽  
pp. 1-10 ◽  
Author(s):  
Kan He ◽  
Qishan Wang ◽  
Yumei Yang ◽  
Minghui Wang ◽  
Yuchun Pan
Keyword(s):  
Gene Expression ◽  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Enrichment Analysis ◽  
Small Cell ◽  
Gene Set Enrichment Analysis ◽  
Small Cell Lung ◽  
Gene Set Enrichment ◽  
Tissue Specific

Gene expression profiling of PPARαhas been used in several studies, but fewer studies went further to identify the tissue-specific pathways or genes involved in PPARαactivation in genome-wide. Here, we employed and applied gene set enrichment analysis to two microarray datasets both PPARαrelated respectively in mouse liver and intestine. We suggested that the regulatory mechanism of PPARαactivation by WY14643 in mouse small intestine is more complicated than in liver due to more involved pathways. Several pathways were cancer-related such as pancreatic cancer and small cell lung cancer, which indicated that PPARαmay have an important role in prevention of cancer development. 12 PPARαdependent pathways and 4 PPARαindependent pathways were identified highly common in both liver and intestine of mice. Most of them were metabolism related, such as fatty acid metabolism, tryptophan metabolism, pyruvate metabolism with regard to PPARαregulation but gluconeogenesis and propanoate metabolism independent of PPARαregulation. Keratan sulfate biosynthesis, the pathway of regulation of actin cytoskeleton, the pathways associated with prostate cancer and small cell lung cancer were not identified as hepatic PPARαindependent but as WY14643 dependent ones in intestinal study. We also provided some novel hepatic tissue-specific marker genes.

scholarly journals Understanding Tissue-specific Gene Regulation

2017 ◽  
Author(s):  
Abhijeet R. Sonawane ◽  
John Platig ◽  
Maud Fagny ◽  
Cho-Yi Chen ◽  
Joseph N. Paulson ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Transcriptional Control ◽  
Tissue Specificity ◽  
Enrichment Analysis ◽  
Tissue Expression ◽  
Gene Set Enrichment Analysis ◽  
Specific Gene ◽  
Tissue Specific ◽  
Network Nodes ◽  
Transcription Factor Expression

Although all human tissues carry out common processes, tissues are distinguished by gene expres-sion patterns, implying that distinct regulatory programs control tissue-specificity. In this study, we investigate gene expression and regulation across 38 tissues profiled in the Genotype-Tissue Expression project. We find that network edges (transcription factor to target gene connections) have higher tissue-specificity than network nodes (genes) and that regulating nodes (transcription factors) are less likely to be expressed in a tissue-specific manner as compared to their targets (genes). Gene set enrichment analysis of network targeting also indicates that regulation of tissue-specific function is largely independent of transcription factor expression. In addition, tissue-specific genes are not highly targeted in their corresponding tissue-network. However, they do assume bottleneck positions due to variability in transcription factor targeting and the influence of non-canonical regulatory interactions. These results suggest that tissue-specificity is driven by context-dependent regulatory paths, providing transcriptional control of tissue-specific processes.

scholarly journals A hypothesis-driven approach to assessing significance of differences in RNA expression levels among specific groups of genes

2017 ◽  
Author(s):  
Mingze He ◽  
Peng Liu ◽  
Carolyn J. Lawrence-Dill
Keyword(s):  
Gene Expression ◽  
Transcription Factor ◽  
Heat Shock ◽  
Expression Patterns ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Specific Gene ◽  
Simple Method ◽  
Expression Levels ◽  
Gene Set

AbstractGenome-wide molecular gene expression studies generally compare expression values for each gene across multiple conditions followed by cluster and gene set enrichment analysis to determine whether differentially expressed genes are enriched in specific biochemical pathways, cellular components, biological processes, and/or molecular functions, etc. This approach to analyzing differences in gene expression enables discovery of gene function, but is not useful to determine whether pre-defined groups of genes share or diverge in their expression patterns in response to treatments nor to assess the correctness of pre-defined gene set groupings. Here we present a simple method that changes the dimension of comparison by treating genes as variable traits to directly assess significance of differences in expression levels among pre-defined gene groups. Because expression distributions are typically skewed (thus unfit for direct assessment using Gaussian statistical methods) our method involves transforming expression data to approximate a normal distribution followed by dividing the genes into groups, then applying Gaussian parametric methods to assess significance of observed differences. This method enables the assessment of differences in gene expression distributions within and across samples, enabling hypothesis-based comparison among groups of genes. We demonstrate this method by assessing the significance of specific gene groups’ differential response to heat stress conditions in maize.AbbreviationsGO– gene ontology HSP – heat shock proteinKEGG– Kyoto Encyclopedia of Genes and GenomesHSF TF– heat shock factor transcription factorHSBP– heat shock binding proteinRNA– ribonucleic acidTE– transposable elementTF– transcription factorTPM– transcripts per kilobase millions

Histones: genetic diversity and tissue-specific gene expression

1997 ◽  
Vol 107 (1) ◽  
pp. 1-10 ◽  
Author(s):  
D. Doenecke ◽  
W. Albig ◽  
C. Bode ◽  
B. Drabent ◽  
K. Franke ◽  
...  
Keyword(s):  
Gene Expression ◽  
Genetic Diversity ◽  
Specific Gene ◽  
Tissue Specific ◽  
Tissue Specific Gene ◽  
Specific Gene Expression

scholarly journals Blood—Brain Barrier Genomics

2001 ◽  
Vol 21 (1) ◽  
pp. 61-68 ◽  
Author(s):  
Jian Yi Li ◽  
Ruben J. Boado ◽  
William M. Pardridge
Keyword(s):  
Gene Expression ◽  
Blood Brain Barrier ◽  
Brain Barrier ◽  
Specific Gene ◽  
Microvascular Endothelium ◽  
Tissue Specific ◽  
Tissue Specific Gene ◽  
Specific Gene Expression ◽  
Blood Brain ◽  
Tester Cdna

The blood–brain barrier (BBB) is formed by the brain microvascular endothelium, and the unique transport properties of the BBB are derived from tissue-specific gene expression within this cell. The current studies developed a gene microarray approach specific for the BBB by purifying the initial mRNA from isolated rat brain capillaries to generate tester cDNA. A polymerase chain reaction–based subtraction cloning method, suppression subtractive hybridization (SSH), was used, and the BBB cDNA was subtracted with driver cDNA produced from mRNA isolated from rat liver and kidney. Screening 5% of the subtracted tester cDNA resulted in identification of 50 gene products and more than 80% of those were selectively expressed at the BBB; these included novel gene sequences not found in existing databases, ESTs, and known genes that were not known to be selectively expressed at the BBB. Genes in the latter category include tissue plasminogen activator, insulin-like growth factor-2, PC-3 gene product, myelin basic protein, regulator of G protein signaling 5, utrophin, IκB, connexin-45, the class I major histocompatibility complex, the rat homologue of the transcription factors hbrm or EZH1, and organic anion transporting polypeptide type 2. Knowledge of tissue-specific gene expression at the BBB could lead to new targets for brain drug delivery and could elucidate mechanisms of brain pathology at the microvascular level.

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