scholarly journals Meta-analysis of prostate cancer gene expression data identifies a novel discriminatory signature enriched for glycosylating enzymes

2014 ◽  
Vol 7 (1) ◽  
Author(s):  
Stefan J Barfeld ◽  
Phil East ◽  
Verena Zuber ◽  
Ian G Mills
Keyword(s):  
Gene Expression ◽  
Prostate Cancer ◽  
Gene Expression Data ◽  
Meta Analysis ◽  
Cancer Gene ◽  
Expression Data

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.

scholarly journals Candidate pathways and genes for prostate cancer: a meta-analysis of gene expression data

2009 ◽  
Vol 2 (1) ◽  
Author(s):  
Ivan P Gorlov ◽  
Jinyoung Byun ◽  
Olga Y Gorlova ◽  
Ana M Aparicio ◽  
Eleni Efstathiou ◽  
...  
Keyword(s):  
Gene Expression ◽  
Prostate Cancer ◽  
Gene Expression Data ◽  
Meta Analysis ◽  
Expression Data

scholarly journals Site-Specific and Common Prostate Cancer Metastasis Genes as Suggested by Meta-Analysis of Gene Expression Data

Life ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 636
Author(s):  
Ivana Samaržija
Keyword(s):  
Gene Expression ◽  
Prostate Cancer ◽  
Gene Expression Data ◽  
Cancer Metastasis ◽  
Meta Analysis ◽  
Cancer Prognosis ◽  
Expression Data ◽  
Site Specific ◽  
Metastatic Cells ◽  
The Common

Anticancer therapies mainly target primary tumor growth and little attention is given to the events driving metastasis formation. Metastatic prostate cancer, in comparison to localized disease, has a much worse prognosis. In the work presented here, groups of genes that are common to prostate cancer metastatic cells from bones, lymph nodes, and liver and those that are site-specific were delineated. The purpose of the study was to dissect potential markers and targets of anticancer therapies considering the common characteristics and differences in transcriptional programs of metastatic cells from different secondary sites. To that end, a meta-analysis of gene expression data of prostate cancer datasets from the GEO database was conducted. Genes with differential expression in all metastatic sites analyzed belong to the class of filaments, focal adhesion, and androgen receptor signaling. Bone metastases undergo the largest transcriptional changes that are highly enriched for the term of the chemokine signaling pathway, while lymph node metastasis show perturbation in signaling cascades. Liver metastases change the expression of genes in a way that is reminiscent of processes that take place in the target organ. Survival analysis for the common hub genes revealed involvements in prostate cancer prognosis and suggested potential biomarkers.

scholarly journals Meta-analysis of Gene Expression Data Identifies Causal Genes for Prostate Cancer

2013 ◽  
Vol 14 (1) ◽  
pp. 457-461 ◽  
Author(s):  
Xiang-Yang Wang ◽  
Jian-Wei Hao ◽  
Rui-Jin Zhou ◽  
Xiang-Sheng Zhang ◽  
Tian-Zhong Yan ◽  
...  
Keyword(s):  
Gene Expression ◽  
Prostate Cancer ◽  
Gene Expression Data ◽  
Meta Analysis ◽  
Expression Data ◽  
Causal Genes

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.

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