Dataset Information

Peripheral Biomarkers in Schizophrenia: A Meta-Analysis of Microarray Gene Expression Datasets.

ABSTRACT: BACKGROUND:Schizophrenia is a severe psychiatric disorder with a complex pathophysiology. Given its prevalence, high risk of mortality, early onset, and high levels of disability, researchers have attempted to develop early detection strategies for facilitating timely pharmacological and/or nonpharmacological interventions. Here, we performed a meta-analysis of publicly available gene expression datasets in peripheral tissues in schizophrenia and healthy controls to detect consistent patterns of illness-associated gene expression. We also tested whether our earlier finding of a downregulation of NPTX2 expression in the brain of schizophrenia patients replicated in peripheral tissues. METHODS:We conducted a systematic search in the Gene Expression Omnibus repository (https://www.ncbi.nlm.nih.gov/gds/) and identified 3 datasets matching our inclusion criteria: GSE62333, GSE18312, and GSE27383. After quality controls, the total sample size was: schizophrenia (n?=?71) and healthy controls (n?=?57) (schizophrenia range: n?=?12-40; healthy controls range: n?=?8-29). RESULTS:The results of the meta-analysis conducted with the GeneMeta package revealed 2 genes with a false discovery rate ?

SUBMITTER: Piras IS

PROVIDER: S-EPMC6403089 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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Peripheral Biomarkers in Schizophrenia: A Meta-Analysis of Microarray Gene Expression Datasets.

Piras Ignazio S IS Manchia Mirko M Huentelman Matthew J MJ Pinna Federica F Zai Clement C CC Kennedy James L JL Carpiniello Bernardo B

The international journal of neuropsychopharmacology 20190301 3

<h4>Background</h4>Schizophrenia is a severe psychiatric disorder with a complex pathophysiology. Given its prevalence, high risk of mortality, early onset, and high levels of disability, researchers have attempted to develop early detection strategies for facilitating timely pharmacological and/or nonpharmacological interventions. Here, we performed a meta-analysis of publicly available gene expression datasets in peripheral tissues in schizophrenia and healthy controls to detect consistent pat ...[more]

PMID: 30576541

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Project description:Affymetrix high density oligonucleotide expression arrays are widely used across all fields of biological research for measuring genome-wide gene expression. An important step in processing oligonucleotide microarray data is to produce a single value for the gene expression level of an RNA transcript using one of a growing number of statistical methods. The challenge for the researcher is to decide on the most appropriate method to use to address a specific biological question with a given dataset. Although several research efforts have focused on assessing performance of a few methods in evaluating gene expression from RNA hybridization experiments with different datasets, the relative merits of the methods currently available in the literature for evaluating genome-wide gene expression from Affymetrix microarray data collected from real biological experiments remain actively debated.The present study reports a comprehensive survey of the performance of all seven commonly used methods in evaluating genome-wide gene expression from a well-designed experiment using Affymetrix microarrays. The experiment profiled eight genetically divergent barley cultivars each with three biological replicates. The dataset so obtained confers a balanced and idealized structure for the present analysis. The methods were evaluated on their sensitivity for detecting differentially expressed genes, reproducibility of expression values across replicates, and consistency in calling differentially expressed genes. The number of genes detected as differentially expressed among methods differed by a factor of two or more at a given false discovery rate (FDR) level. Moreover, we propose the use of genes containing single feature polymorphisms (SFPs) as an empirical test for comparison among methods for the ability to detect true differential gene expression on the basis that SFPs largely correspond to cis-acting expression regulators. The PDNN method demonstrated superiority over all other methods in every comparison, whilst the default Affymetrix MAS5.0 method was clearly inferior.A comprehensive assessment of seven commonly used data extraction methods based on an extensive barley Affymetrix gene expression dataset has shown that the PDNN method has superior performance for the detection of differentially expressed genes.

Project description:Metastasis is the number one cause of cancer deaths. Expression microarrays have been widely used to study metastasis in various types of cancer. We hypothesize that a meta-analysis of publicly available gene expression datasets in various tumor types can identify a signature of metastasis that is common to multiple tumor types. This common signature of metastasis may help us to understand the shared steps in the metastatic process and identify useful biomarkers that could predict metastatic risk.We identified 18 publicly available gene expression datasets in the Oncomine database comparing distant metastases to primary tumors in various solid tumors which met our eligibility criteria. We performed a meta-analysis using a modified permutation counting method in order to obtain a common gene signature of metastasis. We then validated this signature in independent datasets using gene set expression comparison analysis with the LS-statistic.A common metastatic signature of 79 genes was identified in the metastatic lesions compared with primaries with a False Discovery Proportion of less than 0.1. Interestingly, all the genes in the signature, except one, were significantly down-regulated, suggesting that overcoming metastatic suppression may be a key feature common to all metastatic tumors. Pathway analysis of the significant genes showed that the genes were involved in known metastasis-associated pathways, such as integrin signaling, calcium signaling, and VEGF signaling. To validate the signature, we used an additional six expression datasets that were not used in the discovery study. Our results showed that the signature was significantly enriched in four validation sets with p-values less than 0.05.We have modified a previously published meta-analysis method and identified a common metastatic signature by comparing primary tumors versus metastases in various tumor types. This approach, as well as the gene signature identified, provides important insights to the common metastatic process and a foundation for future discoveries that could have broad application, such as drug discovery, metastasis prediction, and mechanistic studies.

Project description:BACKGROUND:Pulpitis is an inflammatory disease, the grade of which is classified according to the level of inflammation. Traditional methods of evaluating the status of dental pulp tissue in clinical practice have limitations. The rapid and accurate diagnosis of pulpitis is essential for determining the appropriate treatment. By integrating different datasets from the Gene Expression Omnibus (GEO) database, we analysed a merged expression matrix of pulpitis, aiming to identify biological pathways and diagnostic biomarkers of pulpitis. METHODS:By integrating two datasets (GSE77459 and GSE92681) in the GEO database using the sva and limma packages of R, differentially expressed genes (DEGs) of pulpitis were identified. Then, the DEGs were analysed to identify biological pathways of dental pulp inflammation with Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis and Gene Set Enrichment Analysis (GSEA). Protein-protein interaction (PPI) networks and modules were constructed to identify hub genes with the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and Cytoscape. RESULTS:A total of 470 DEGs comprising 394 upregulated and 76 downregulated genes were found in pulpitis tissue. GO analysis revealed that the DEGs were enriched in biological processes related to inflammation, and the enriched pathways in the KEGG pathway analysis were cytokine-cytokine receptor interaction, chemokine signalling pathway and NF-?B signalling pathway. The GSEA results provided further functional annotations, including complement system, IL6/JAK/STAT3 signalling pathway and inflammatory response pathways. According to the degrees of nodes in the PPI network, 10 hub genes were identified, and 8 diagnostic biomarker candidates were screened: PTPRC, CD86, CCL2, IL6, TLR8, MMP9, CXCL8 and ICAM1. CONCLUSIONS:With bioinformatics analysis of merged datasets, biomarker candidates of pulpitis were screened and the findings may be as reference to develop a new method of pulpitis diagnosis.

Dataset Information

Peripheral Biomarkers in Schizophrenia: A Meta-Analysis of Microarray Gene Expression Datasets.

Publications

Peripheral Biomarkers in Schizophrenia: A Meta-Analysis of Microarray Gene Expression Datasets.

Similar Datasets

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