Workflow of microarray meta-analysis integrating GWAS data.

<p>A flow diagram depicting the process involved in the meta-analysis of the selected microarray datasets with integration of GWAS data.</p

Transcriptional regulatory subnetwork based on microarray meta-analysis.

<p>Regulatory network analysis was performed using RNEA R/package to determine the regulation complexes upstream of DEGs identified in the meta-analysis. Genes carrying in their promoter, a significant regulatory SNPs are marked by a yellow star. Each node represents a DEG or enriched transcription factor, depending on their shapes. The node size indicate greater significance of the enrichment. The edges reflect the relationships between the nodes.</p

Over-representation of pathways and GO categories in biological networks identified by the meta-analysis.

<p>Network representation of an enriched pathway integrating biological processes on the DEG list according to the ClueGO Cytoscape plugin. Hypergeometric (right-handed) enrichment distribution tests were conducted with a p value significance level of ≤0.05, followed by the Bonferroni adjustment for the terms, and thus leading term groups were selected based on the highest significance. Each node represents a biological process. The edges reflect the relationships between the terms based on the similarity of their associated genes. The node size and deeper color indicate greater significance of the enrichment.</p

Characteristics of included individual microarray dataset.

<p>Characteristics of included individual microarray dataset.</p

MetaQC quantitative quality control measures for gene expression data.

<p>MetaQC quantitative quality control measures for gene expression data.</p

A PRISMA flow diagram of a systematic database search.

<p>The selection process of eligible microarray datasets for meta-analysis of the shared transcriptomic signatures between Sickle cell disease patients, according to Prisma 2009 flow diagram.</p

Downregulated and upregulated DEGs involved in apoptosis pathways.

<p>The inner ring is a bar plot where the height of the bar indicates the significance of the term (−log₁₀ adjusted <i>p</i> value), and color corresponds to the <i>z</i>-score. The outer ring displays scatterplots of the expression levels (log2FC) for the genes in each term.</p

Network-based meta-analysis of hub genes.

<p><b>A:</b> Protein interaction network analysis indicates a central role for SKP1, NAPA, EPB42, and ARPC5 in SCD anemia. All 335 genes served as input for the STRING database with the high confidence interaction score 0.7, and a network was built by means of Cytoscape. The network topology was analyzed by the Cytoscape NetworkAnalyzer tool, and then network topology measures such as the degree (represented by the node size scale), betweenness (represented by police size scale), closeness centrality, and clustering coefficient were calculated. <b>B and C:</b> The top-ranked subnetwork identified by the OH-PIN algorithm (threshold: 2, overlapping score 0.5) using CytoCluster (a Cytoscape plugin).</p

A common molecular signature of patients with sickle cell disease revealed by microarray meta-analysis and a genome-wide association study

<div><p>A chronic inflammatory state to a large extent explains sickle cell disease (SCD) pathophysiology. Nonetheless, the principal dysregulated factors affecting this major pathway and their mechanisms of action still have to be fully identified and elucidated. Integrating gene expression and genome-wide association study (GWAS) data analysis represents a novel approach to refining the identification of key mediators and functions in complex diseases. Here, we performed gene expression meta-analysis of five independent publicly available microarray datasets related to homozygous SS patients with SCD to identify a consensus SCD transcriptomic profile. The meta-analysis conducted using the MetaDE R package based on combining p values (maxP approach) identified 335 differentially expressed genes (DEGs; 224 upregulated and 111 downregulated). Functional gene set enrichment revealed the importance of several metabolic pathways, of innate immune responses, erythrocyte development, and hemostasis pathways. Advanced analyses of GWAS data generated within the framework of this study by means of the atSNP R package and SIFT tool identified 60 regulatory single-nucleotide polymorphisms (rSNPs) occurring in the promoter of 20 DEGs and a deleterious SNP, affecting CAMKK2 protein function. This novel database of candidate genes, transcription factors, and rSNPs associated with SCD provides new markers that may help to identify new therapeutic targets.</p></div