Curie Institute, INSERM U830, Translational Research Department, PSL Research University, Paris, 75005, France.
School of Biological Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran.
BMC Med Genomics. 2019 Nov 8;12(1):160. doi: 10.1186/s12920-019-0609-1.
In 2012, Venet et al. proposed that at least in the case of breast cancer, most published signatures are not significantly more associated with outcome than randomly generated signatures. They suggested that nominal p-value is not a good estimator to show the significance of a signature. Therefore, one can reasonably postulate that some information might be present in such significant random signatures.
In this research, first we show that, using an empirical p-value, these published signatures are more significant than their nominal p-values. In other words, the proposed empirical p-value can be considered as a complimentary criterion for nominal p-value to distinguish random signatures from significant ones. Secondly, we develop a novel computational method to extract information that are embedded within significant random signatures. In our method, a score is assigned to each gene based on the number of times it appears in significant random signatures. Then, these scores are diffused through a protein-protein interaction network and a permutation procedure is used to determine the genes with significant scores. The genes with significant scores are considered as the set of significant genes.
First, we applied our method on the breast cancer dataset NKI to achieve a set of significant genes in breast cancer considering significant random signatures. Secondly, prognostic performance of the computed set of significant genes is evaluated using DMFS and RFS datasets. We have observed that the top ranked genes from this set can successfully separate patients with poor prognosis from those with good prognosis. Finally, we investigated the expression pattern of TAT, the first gene reported in our set, in malignant breast cancer vs. adjacent normal tissue and mammospheres.
Applying the method, we found a set of significant genes in breast cancer, including TAT, a gene that has never been reported as an important gene in breast cancer. Our results show that the expression of TAT is repressed in tumors suggesting that this gene could act as a tumor suppressor in breast cancer and could be used as a new biomarker.
2012 年，Venet 等人提出，至少在乳腺癌的情况下，大多数已发表的特征与结局的相关性并不比随机生成的特征强。他们认为，名义 p 值不是显示特征显著性的良好估计值。因此，可以合理地假设，在这些显著的随机特征中可能存在一些信息。
在这项研究中，首先我们表明，使用经验 p 值，这些已发表的特征比它们的名义 p 值更显著。换句话说，所提出的经验 p 值可以被视为名义 p 值的补充标准，以区分随机特征和显著特征。其次，我们开发了一种新的计算方法来提取嵌入在显著随机特征中的信息。在我们的方法中，根据基因出现在显著随机特征中的次数，为每个基因分配一个分数。然后，这些分数通过蛋白质-蛋白质相互作用网络传播，并且使用置换过程来确定具有显著分数的基因。具有显著分数的基因被认为是显著基因的集合。
首先，我们在乳腺癌数据集 NKI 上应用我们的方法，考虑到显著的随机特征，在乳腺癌中获得了一组显著基因。其次，使用 DMFS 和 RFS 数据集评估计算得到的显著基因集的预后性能。我们观察到，从该集合中排名最高的基因可以成功地将预后不良的患者与预后良好的患者区分开来。最后，我们研究了 TAT 的表达模式，TAT 是我们集合中报告的第一个基因，在恶性乳腺癌与相邻正常组织和乳腺球体中。
应用该方法，我们在乳腺癌中发现了一组显著基因，包括 TAT，这是一个从未被报道为乳腺癌重要基因的基因。我们的结果表明，TAT 的表达在肿瘤中受到抑制，这表明该基因可能在乳腺癌中作为肿瘤抑制基因发挥作用，并可作为新的生物标志物。
