scholarly journals FARO server: Meta-analysis of gene expression by matching gene expression signatures to a compendium of public gene expression data

2011 ◽  
Vol 4 (1) ◽  
Author(s):  
Mieszko P Manijak ◽  
Henrik B Nielsen
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Meta Analysis ◽  
Expression Data ◽  
Gene Expression Signatures

scholarly journals Topological Properties of Co-Occurrence Networks in Published Gene Expression Signatures

2008 ◽  
Vol 2 ◽  
pp. BBI.S518 ◽  
Author(s):  
Heiko Muller ◽  
Francesco Acquati
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Human Cancer ◽  
Meta Analysis ◽  
Expression Data ◽  
Topological Properties ◽  
Scale Free ◽  
Protein Protein Interaction ◽  
Gene Expression Signatures ◽  
Cancer Related Gene

Meta-analysis of high-throughput gene expression data is often used for the interpretation of proprietary gene expression data sets. We have recently shown that co-occurrence patterns of gene expression in published cancer-related gene expression signatures are reminiscent of several cancer signaling pathways. Indeed, significant co-occurrence of up to ten genes in published gene expression signatures can be exploited to build a co-occurrence network from the sets of co-occurring genes (“co-occurrence modules”). Such co-occurrence network is represented by an undirected graph, where single genes are assigned to vertices and edges indicate that two genes are significantly co-occurring. Thus, graph-cut methods can be used to identify groups of highly interconnected vertices (“network communities”) that correspond to sets of genes that are significantly co-regulated in human cancer. Here, we investigate the topological properties of co-occurrence networks derived from published gene expression signatures and show that co-occurrence networks are characterized by scale-free topology and hierarchical modularity. Furthermore, we report that genes with a “promiscuous” or a “faithful” co-occurrence pattern can be distinguished. This behavior is reminiscent of date and party hubs that have been identified in protein-protein interaction networks.

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 Unraveling the Genomic-Epigenomic Interaction Landscape in Triple Negative and Non-Triple Negative Breast Cancer

Cancers ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1559
Author(s):  
Jiande Wu ◽  
Tarun Karthik Kumar Mamidi ◽  
Lu Zhang ◽  
Chindo Hicks
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Signaling Pathways ◽  
Somatic Mutation ◽  
Gene Expression Data ◽  
Triple Negative Breast Cancer ◽  
Somatic Mutations ◽  
Triple Negative ◽  
Expression Data ◽  
Gene Expression Signatures

Background: The recent surge of next generation sequencing of breast cancer genomes has enabled development of comprehensive catalogues of somatic mutations and expanded the molecular classification of subtypes of breast cancer. However, somatic mutations and gene expression data have not been leveraged and integrated with epigenomic data to unravel the genomic-epigenomic interaction landscape of triple negative breast cancer (TNBC) and non-triple negative breast cancer (non-TNBC). Methods: We performed integrative data analysis combining somatic mutation, epigenomic and gene expression data from The Cancer Genome Atlas (TCGA) to unravel the possible oncogenic interactions between genomic and epigenomic variation in TNBC and non-TNBC. We hypothesized that within breast cancers, there are differences in somatic mutation, DNA methylation and gene expression signatures between TNBC and non-TNBC. We further hypothesized that genomic and epigenomic alterations affect gene regulatory networks and signaling pathways driving the two types of breast cancer. Results: The investigation revealed somatic mutated, epigenomic and gene expression signatures unique to TNBC and non-TNBC and signatures distinguishing the two types of breast cancer. In addition, the investigation revealed molecular networks and signaling pathways enriched for somatic mutations and epigenomic changes unique to each type of breast cancer. The most significant pathways for TNBC were: retinal biosynthesis, BAG2, LXR/RXR, EIF2 and P2Y purigenic receptor signaling pathways. The most significant pathways for non-TNBC were: UVB-induced MAPK, PCP, Apelin endothelial, Endoplasmatic reticulum stress and mechanisms of viral exit from host signaling Pathways. Conclusion: The investigation revealed integrated genomic, epigenomic and gene expression signatures and signing pathways unique to TNBC and non-TNBC, and a gene signature distinguishing the two types of breast cancer. The study demonstrates that integrative analysis of multi-omics data is a powerful approach for unravelling the genomic-epigenomic interaction landscape in TNBC and non-TNBC.

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.

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