Application of Survival and Meta-analysis to Gene Expression Data Combined from Two Studies

2006 ◽  
pp. 67-80 ◽  
Author(s):  
Linda Warnock ◽  
Richard Stephens ◽  
JoAnn Coleman
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Meta Analysis ◽  
Expression Data

scholarly journals Multiple outcome meta-analysis of gene-expression data in inflammatory bowel disease

Genomics ◽  
2020 ◽  
Vol 112 (2) ◽  
pp. 1761-1767 ◽  
Author(s):  
Konstantina E. Vennou ◽  
Daniele Piovani ◽  
Panagiota I. Kontou ◽  
Stefanos Bonovas ◽  
Pantelis G. Bagos
Keyword(s):  
Gene Expression ◽  
Inflammatory Bowel Disease ◽  
Gene Expression Data ◽  
Bowel Disease ◽  
Meta Analysis ◽  
Expression Data ◽  
Multiple Outcome ◽  
Inflammatory Bowel

scholarly journals ExAtlas: An interactive online tool for meta-analysis of gene expression data

2015 ◽  
Vol 13 (06) ◽  
pp. 1550019 ◽  
Author(s):  
Alexei A. Sharov ◽  
David Schlessinger ◽  
Minoru S. H. Ko
Keyword(s):  
Gene Expression ◽  
Gene Ontology ◽  
Gene Expression Data ◽  
Fixed Effects ◽  
Expression Profiles ◽  
Meta Analysis ◽  
Data Sets ◽  
Expression Data ◽  
Gene Set ◽  
Public Data

We have developed ExAtlas, an on-line software tool for meta-analysis and visualization of gene expression data. In contrast to existing software tools, ExAtlas compares multi-component data sets and generates results for all combinations (e.g. all gene expression profiles versus all Gene Ontology annotations). ExAtlas handles both users’ own data and data extracted semi-automatically from the public repository (GEO/NCBI database). ExAtlas provides a variety of tools for meta-analyses: (1) standard meta-analysis (fixed effects, random effects, z-score, and Fisher’s methods); (2) analyses of global correlations between gene expression data sets; (3) gene set enrichment; (4) gene set overlap; (5) gene association by expression profile; (6) gene specificity; and (7) statistical analysis (ANOVA, pairwise comparison, and PCA). ExAtlas produces graphical outputs, including heatmaps, scatter-plots, bar-charts, and three-dimensional images. Some of the most widely used public data sets (e.g. GNF/BioGPS, Gene Ontology, KEGG, GAD phenotypes, BrainScan, ENCODE ChIP-seq, and protein–protein interaction) are pre-loaded and can be used for functional annotations.

scholarly journals A Meta-Analysis of Gene Expression Data Identifies a Molecular Signature Characteristic for Tumor-Stage Mycosis Fungoides

2012 ◽  
Vol 132 (8) ◽  
pp. 2050-2059 ◽  
Author(s):  
Marloes S. van Kester ◽  
Martin K. Borg ◽  
Willem H. Zoutman ◽  
Jacoba J. Out-Luiting ◽  
Patty M. Jansen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Mycosis Fungoides ◽  
Gene Expression Data ◽  
Meta Analysis ◽  
Tumor Stage ◽  
Molecular Signature ◽  
Expression Data

scholarly journals GWAS meta-analysis and gene expression data link reproductive tract development, immune response and cellular proliferation/apoptosis with cervical cancer and clarify overlap with other cervical phenotypes

2021 ◽  
Author(s):  
Mariann Koel ◽  
Urmo Võsa ◽  
Maarja Lepamets ◽  
Hannele Laivuori ◽  
Susanna Lemmelä ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cervical Cancer ◽  
Immune Response ◽  
Gene Expression Data ◽  
Cellular Proliferation ◽  
Reproductive Tract ◽  
Meta Analysis ◽  
Cervical Dysplasia ◽  
Expression Data ◽  
Causal Variants

Background The uterine cervix has an important role in female reproductive health, but not much is known about the genetic determinants of cervical biology and pathology. Genome-wide association studies (GWAS) with increasing sample sizes have reported a few genetic associations for cervical cancer. However, GWAS is only the first step in mapping the genetic susceptibility and thus, the underlying biology in cervical cancer and other cervical phenotypes is still not entirely understood. Here, we use data from large biobanks to characterise the genetics of cervical phenotypes (including cervical cancer) and leverage latest computational methods and gene expression data to refine the association signals for cervical cancer. Methods Using Estonian Biobank and FinnGen data, we characterise the genetic signals associated with cervical ectropion (10,162 cases/151,347 controls), cervicitis (19,285/185,708) and cervical dysplasia (14,694/150,563). We present the results from the largest trans-ethnic GWAS meta-analysis of cervical cancer, including up to 9,229 cases and 490,304 controls from Estonian Biobank, the FinnGen study, the UK Biobank and Biobank Japan. We combine GWAS results with gene expression data and chromatin regulatory annotations in HeLa cervical carcinoma cells to propose the most likely candidate genes and causal variants for every locus associated with cervical cancer. We further dissect the HLA association with cervical pathology using imputed data on alleles and amino acid polymorphisms. Results We report a single associated locus on 2q13 for both cervical ectropion (rs3748916, p=5.1 x 10-16) and cervicitis (rs1049137, p=3.9 x 10-10), and five signals for cervical dysplasia - 6p21.32 (rs1053726, p=9.1 x 10-9; rs36214159, 1.6 x 10-22), 2q24.1 (rs12611652, p=3.2 x 10-9) near DAPL1, 2q13 ns1049137, p=6.4 x 10-9) near PAX8, and 5p15.33 (rs6866294, p=2.1 x 10-9), downstream of CLPTM1L. We identify five loci associated with cervical cancer in the trans-ethnic meta-analysis: 1p36.12 (rs2268177, p= 3.1 x 10-8), 2q13 (rs4849177, p=9.4 x 10-15), 5p15.33 (rs27069, p=1.3 x 10-14), 17q12 (rs12603332, p=1.2 x 10-9), and 6p21.32 (rs35508382, p=1.0 x 10-39). Joint analysis of dysplasia and cancer datasets revealed an association on chromosome 19 (rs425787, p=3.5 x 10-8), near CD70. Conclusions Our results map PAX8/PAX8-AS1, LINC00339, CDC42, CLPTM1L, HLA-DRB1, HLA-B, and GSDMB as the most likely candidate genes for cervical cancer, which provides novel insight into cervical cancer pathogenesis and supports the role of genes involved in reproductive tract development, immune response and cellular proliferation/apoptosis. We further show that PAX8/PAX8-AS1 has a central role in cervical biology and pathology, as it was associated with all analysed phenotypes. The detailed characterisation of association signals, together with mapping of causal variants and genes offers valuable leads for further functional studies.

scholarly journals Gene Expression Studies to Identify Significant Genes in AR, MTOR, MAPK Pathways and their Overlapping Regulatory Role in Prostate Cancer

2019 ◽  
Vol 16 (3) ◽  
Author(s):  
Nimisha Asati ◽  
Abhinav Mishra ◽  
Ankita Shukla ◽  
Tiratha Raj Singh
Keyword(s):  
Gene Expression ◽  
Prostate Cancer ◽  
Gene Expression Data ◽  
Meta Analysis ◽  
Expression Patterns ◽  
Mitogen Activated Protein Kinase ◽  
Expression Data ◽  
Mapk Pathways ◽  
Expression Studies ◽  
Gene Expression Studies

AbstractGene expression studies revealed a large degree of variability in gene expression patterns particularly in tissues even in genetically identical individuals. It helps to reveal the components majorly fluctuating during the disease condition. With the advent of gene expression studies many microarray studies have been conducted in prostate cancer, but the results have varied across different studies. To better understand the genetic and biological regulatory mechanisms of prostate cancer, we conducted a meta-analysis of three major pathways i.e. androgen receptor (AR), mechanistic target of rapamycin (mTOR) and Mitogen-Activated Protein Kinase (MAPK) on prostate cancer. Meta-analysis has been performed for the gene expression data for the human species that are exposed to prostate cancer. Twelve datasets comprising AR, mTOR, and MAPK pathways were taken for analysis, out of which thirteen potential biomarkers were identified through meta-analysis. These findings were compiled based upon the quantitative data analysis by using different tools. Also, various interconnections were found amongst the pathways in study. Our study suggests that the microarray analysis of the gene expression data and their pathway level connections allows detection of the potential predictors that can prove to be putative therapeutic targets with biological and functional significance in progression of prostate cancer.

Aberrant Co-Expression of Micrornas and Host Genes in Leukemia: Evidence for a Common Transcript from Meta-Analysis and Microarray Data.

Blood ◽  
2006 ◽  
Vol 108 (11) ◽  
pp. 2232-2232
Author(s):  
Serban San-Marina ◽  
Fernando Suarez Saiz ◽  
Haytham Khoury ◽  
Mark D. Minden
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Gene Expression Data ◽  
Microarray Data ◽  
Meta Analysis ◽  
Host Gene ◽  
Expression Data ◽  
Host Gene Expression ◽  
Intronic Mirnas ◽  
Host Genes

Abstract In leukemia, the integrity of the transcriptome is altered by chromosomal translocations, deletions, duplications, as well as by epigenetic changes in chromatin structure. By targeting mRNAs for translational repression or RNase-dependent hydrolysis (AU-rich miRNAs or shRNA-like effects), the micro RNA (miRNA) component of the transcriptome is estimated to regulate expression of up to 30% of all proteins. Yet the causes and role of deregulated miRNA expression in malignancy are largely unknown, in part because promoter events are not characterized. Since more than one-third of all known mammalian miRNA genes are encoded in the introns of protein-coding genes they may be regulated by the same promoter events that regulate host-gene mRNA expression. To provide experimental validation for coordinated expression of miRNAs and their host genes we compared Affymetrix U133A gene expression data for the promyelocytic NB4 and acute myelogenous leukemia AML2 cell lines with the expression of miRNA precursors. We found similar patterns of host gene expression in the two cell lines and a good correlation with the expression of miRNA precursors in NB4 cells (r=0.464, N=30 miRNAs, p<0.016). To further demonstrate that host gene mRNAs and miRNAs are expressed from common transcripts, we activated promoter events by enforcing the expression of Lyl1 a basic helix-loop-helix transcription factor that is often over-expressed in AML. This resulted in a greater than 2-fold increase in hsa-mir-126-1, 032-2, 107-1, 026a, -023b, -103-2, and 009-3-1 intronic miRNA precursors and a corresponding increase in host gene expression. Meta-analysis of microarray data across many experiments and platforms (available through Oncomine.org) has been used to study the cancer transcriptome. To help determine if intronic miRNAs play a substantial role in malignancy, we correlated host gene expression data with the expression of predicted miRNA targets. Less than 20% of all differentially expressed genes in leukemia and lymphoma were predicted targets, compared to 68% in breast cancer. Since the Gene Ontology term “ion binding” is most commonly associated with miRNA host genes, the data suggest that this cancer module is relatively inactive in leukemia and lymphoma, compared to breast cancer. Gene cluster analysis of a leukemia data set using only miRNA host gene expression was able to classify patients into similar (but not identical) subsets as did an analysis based on over 20,000 transcripts. To further demonstrate that miRNAs and their host genes are expressed from the same transcription unit, we correlated the expression of miRNA targets with that of genes that are either hosts for miRNAs or are situated several kilobases downstream of a miRNA, and thus belong to different transcription units. We applied this analysis to a subset of 81 AML patients that presented a normal karyotype and found significant negative correlations (p<0.01) between the levels of host genes for hsa-mir-15b, -103-1, and -128 and the expression ranks of their predicted gene targets, but no statistically significant correlation between non-host genes and targets for up-stream miRNAs. These data demonstrate co-regulated expression of host genes and intronic miRNAs and the usefulness of intronic miRNAs in cancer profiling.

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