Meta-analysis of archived DNA microarrays identifies genes regulated by hypoxia and involved in a metastatic phenotype in cancer cells

Abstract

BackgroundMetastasis is a major cancer-related cause of death. Recent studies have described metastasis pathways. However, the exact contribution of each pathway remains unclear. Another key feature of a tumor is the presence of hypoxic areas caused by a lack of oxygen at the center of the tumor. Hypoxia leads to the expression of pro-metastatic genes as well as the repression of anti-metastatic genes. As many Affymetrix datasets about metastasis and hypoxia are publicly available and not fully exploited, this study proposes to re-analyze these datasets to extract new information about the metastatic phenotype induced by hypoxia in different cancer cell lines.MethodsAffymetrix datasets about metastasis and/or hypoxia were downloaded from GEO and ArrayExpress. AffyProbeMiner and GCRMA packages were used for pre-processing and the Window Welch t test was used for processing. Three approaches of meta-analysis were eventually used for the selection of genes of interest.ResultsThree complementary approaches were used, that eventually selected 183 genes of interest. Out of these 183 genes, 99, among which the well known JUNB, FOS and TP63, have already been described in the literature to be involved in cancer. Moreover, 39 genes of those, such as SERPINE1 and MMP7, are known to regulate metastasis. Twenty-one genes including VEGFA and ID2 have also been described to be involved in the response to hypoxia. Lastly, DAVID classified those 183 genes in 24 different pathways, among which 8 are directly related to cancer while 5 others are related to proliferation and cell motility. A negative control composed of 183 random genes failed to provide such results. Interestingly, 6 pathways retrieved by DAVID with the 183 genes of interest concern pathogen recognition and phagocytosis.ConclusionThe proposed methodology was able to find genes actually known to be involved in cancer, metastasis and hypoxia and, thus, we propose that the other genes selected based on the same methodology are of prime interest in the metastatic phenotype induced by hypoxia.

Enhanced Meta-analysis Highlights Genes Involved in Metastasis from Several Microarray Datasets

“…These pathways are "toll-like receptor signaling pathway", "fMLP induced chemokine gene expression in HMC-1 cells", "T cell receptor signaling pathway", "Fc epsilon RI signaling pathway" and "Fc gamma R-mediated phagocytosis". The first four were already discussed previously in Pierre et al [15]. However, the Fc gamma R-mediated phagocytosis pathway was not.…”

“…To obtain n, we multiplied this number by two to take into account the genes involved in metastasis and/or hypoxia still not demonstrated as such. The choice of multiplier was motivated by our observation in a previous work about cancer using a meta-analysis methodology in which we retrieved 183 genes of interest, among which 99 were already known to be involved in cancer [15]. This showed that the number of genes known to be involved in a cancer-related phenomenon can be multiplied by two to take into account those genes still not known to be involved.…”

“…All the datasets and procedures used to run the individual analyses, union intersections and meta-analyses were described in Pierre et al [15]. The data was pre-processed with GCRMA (GeneChip Robust Multi-array Analysis) [16] and the Window Welch t test [17] was used for the processing, according to the benchmark performed by De Hertogh et al [18].…”

“…33 groups of individual analyses were designed as described in Pierre et al [15]. A threshold rank was calculated in each group with the equation (1):…”

Strategies to Explore Functional Genomics Data Sets in NCBI’s GEO Database

“…It is clear that researchers use these data to address questions far beyond those for which the original studies were designed to address. Examples include using GEO data to test new algorithms (18), functionally characterize genes (19), create new added-value targeted databases (20), perform massive meta-analyses across thousands of independently-generated assays (21), and identify diagnostic protein biomarkers for disease (22). …”