Meta-Analysis and Gene Set Analysis of Archived Microarrays Suggest Implication of the Spliceosome in Metastatic and Hypoxic Phenotypes

<div><p>We propose to make use of the wealth of underused DNA chip data available in public repositories to study the molecular mechanisms behind the adaptation of cancer cells to hypoxic conditions leading to the metastatic phenotype. We have developed new bioinformatics tools and adapted others to identify with maximum sensitivity those genes which are expressed differentially across several experiments. The comparison of two analytical approaches, based on either Over Representation Analysis or Functional Class Scoring, by a meta-analysis-based approach, led to the retrieval of known information about the biological situation – thus validating the model – but also more importantly to the discovery of the previously unknown implication of the spliceosome, the cellular machinery responsible for mRNA splicing, in the development of metastasis.</p></div

This table contains the genes identified as DEG in the spliceosome pathway, with the p-values for the respective datasets.

<p>The significant p-values are highlighted in bold. The last columns ‘count’ shows the number of times a given gene is detected as DEG through all 16 datasets.</p

Plot of the EASE score and number of hits in the spliceosome pathway for the 500 random selections of 1156 gene identifiers (in blue), compared with the actual result of the analysis (red).

<p>This graph plots the number of hits (X-axis) against the EASE score (Y-axis). The difference between the random selection scores and the actual result score supports the assumption that the spliceosome is over-represented in our list of genes.</p

Volcano plots for two datasets.

<p>Each volcano plot is related to a single data set, chosen among the different technologies and biological group tested. The green bars represent fold change log₂ values of +–2 and the blue bar represent a p-value threshold of 0.05. The red dots are the 1156 genes selected in the meta-analysis step.</p

List of pathways identified by DAVID with either a significant p-value or 14 or more genes of the 1156 DEG list detected in the map.

<p>The ‘count’ column shows the number of significant genes identified within a pathway. Significant p-values (either the EASE score, the Benjamini-corrected or the FDR-corrected p-values) are shown in bold.</p

Summary of FAERI results.

<p>The first column presents the name of the gene sets tested, the second and third columns show the number of times each gene set was detected as differentially expressed at a threshold of 5% for the p-values, for each biological group. The last column contains the discovery rate across all experiments (7 hypoxia datasets and 9 metastasis datasets).</p

Spliceosome units.

<p>The red stars mark the genes from our list mapped on this pathway.</p

Robustness analysis of the spliceosome pathway enrichment.

<p>The strictness of the meta-analysis step was increased (selection of genes DEGs in 2 datasets out of 16 available, then 4 datasets out of 16 and 6 datasets out of 16; the number between brackets represent the minimal number of datasets for which the genes have to be DEG to be selected per biological group, i.e. hypoxia or metastasis), the count of genes highlighted in the pathway (second column) and EASE score given by DAVID (third column).</p

Correlation between differential gene expressions obtained through real-time PCR and microarray approaches.

<p>Values were obtained from mean values of 6 replicates per conditions in the 4 conditions tested (the 4 concentrations; 0.01; 0.1; 1; 10 μgEE2/L). Significance threshold for n = 4 and p<0.01 is set to 0.72 [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0128598#pone.0128598.ref044" target="_blank">44</a>].</p