Dan L, Duncan Cancer Center, Baylor College of Medicine, One Baylor Plaza, Houston, Texas 77030, USA.
BMC Med Genomics. 2011 Jul 7;4:56. doi: 10.1186/1755-8794-4-56.
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.
转移是癌症死亡的首要原因。表达微阵列已广泛用于研究各种类型癌症的转移。我们假设，对各种肿瘤类型中公开可用的基因表达数据集进行荟萃分析，可以鉴定出一种对多种肿瘤类型都普遍存在的转移特征。这种共同的转移特征可能有助于我们了解转移过程中的共同步骤，并识别出可预测转移风险的有用生物标志物。
我们在 Oncomine 数据库中确定了 18 个公开的基因表达数据集，这些数据集比较了各种实体肿瘤中远处转移与原发性肿瘤之间的差异，符合我们的纳入标准。我们使用修改后的置换计数方法进行荟萃分析，以获得共同的转移基因特征。然后，我们使用 LS 统计量的基因集表达比较分析在独立数据集上验证了该特征。
在转移性病变与原发性肿瘤相比时，鉴定出了一个包含 79 个基因的共同转移性特征，其假发现率小于 0.1。有趣的是，特征中的所有基因除了一个之外都显著下调，这表明克服转移性抑制可能是所有转移性肿瘤的共同关键特征。对显著基因的通路分析表明，这些基因参与了已知的与转移相关的通路，如整合素信号、钙信号和 VEGF 信号。为了验证该特征，我们使用了另外六个未在发现研究中使用的表达数据集。我们的结果表明，该特征在四个验证集中显著富集，p 值小于 0.05。
我们修改了以前发表的荟萃分析方法，并通过比较各种肿瘤类型中的原发性肿瘤与转移瘤，鉴定出了一个共同的转移性特征。这种方法以及鉴定出的基因特征为共同的转移过程提供了重要的见解，并为未来的发现奠定了基础，这些发现可能具有广泛的应用，如药物发现、转移预测和机制研究。
