PDF(Pubmed)
Abstract：
Studies have identified genes and molecular pathways regulating cancer metastasis. However, it remains largely unknown whether metastatic potentials of cancer cells from different lineage types are driven by the same or different gene networks. Here, we aim to address this question through integrative analyses of 493 human cancer cells\' transcriptomic profiles and their metastatic potentials in vivo. Using an unsupervised approach and considering both gene coexpression and protein-protein interaction networks, we identify different gene networks associated with various biological pathways (i.e. inflammation, cell cycle, and RNA translation), the expression of which are correlated with metastatic potentials across subsets of lineage types. By developing a regularized random forest regression model, we show that the combination of the gene module features expressed in the native cancer cells can predict their metastatic potentials with an overall Pearson correlation coefficient of 0.90. By analyzing transcriptomic profile data from cancer patients, we show that these networks are conserved in vivo and contribute to cancer aggressiveness. The intrinsic expression levels of these networks are correlated with drug sensitivity. Altogether, our study provides novel comparative insights into cancer cells\' intrinsic gene networks mediating metastatic potentials across different lineage types, and our results can potentially be useful for designing personalized treatments for metastatic cancers.
摘要：
研究已经确定了调节癌症转移的基因和分子途径。然而,来自不同谱系类型的癌细胞的转移潜能是由相同的还是不同的基因网络驱动的，这在很大程度上是未知的。这里,我们的目标是通过对493个人类肿瘤细胞转录组谱及其体内转移潜能的综合分析来解决这个问题。使用无监督的方法，并考虑基因共表达和蛋白质-蛋白质相互作用网络，我们确定了与各种生物途径相关的不同基因网络(即炎症，细胞周期,和RNA翻译)，其表达与谱系类型亚群的转移潜力相关。通过建立正则化随机森林回归模型,我们表明,在天然癌细胞中表达的基因模块特征的组合可以预测其转移潜能,总体Pearson相关系数为0.90.通过分析癌症患者的转录组数据，我们表明，这些网络在体内是保守的，并有助于癌症侵袭性。这些网络的内在表达水平与药物敏感性相关。总之,我们的研究为介导不同谱系类型转移潜能的癌细胞内在基因网络提供了新的比较见解，我们的结果可能有助于设计针对转移性癌症的个性化治疗方法。
