Meta-analysis of multiple microarray datasets reveals a common gene signature of metastasis in solid tumors

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.Metastasis, the process involving the spread of cancer, accounts for greater than 90% of cancer deaths [1]. However, therapies to treat those patients with advanced disease are largely ineffective. It is, therefore, imperative that we improve the understanding of the metastatic process and detect patients at risk for developing met