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首页> 外文期刊>BMC Medical Genomics >Cell cycle correlated genes dictate the prognostic power of breast cancer gene lists
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Cell cycle correlated genes dictate the prognostic power of breast cancer gene lists

机译：与细胞周期相关的基因决定了乳腺癌基因表的预后能力

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Background Numerous gene lists or "classifiers" have been derived from global gene expression data that assign breast cancers to good and poor prognosis groups. A remarkable feature of these molecular signatures is that they have few genes in common, prompting speculation that they may use distinct genes to measure the same pathophysiological process(es), such as proliferation. However, this supposition has not been rigorously tested. If gene-based classifiers function by measuring a minimal number of cellular processes, we hypothesized that the informative genes for these processes could be identified and the data sets could be adjusted for the predictive contributions of those genes. Such adjustment would then attenuate the predictive function of any signature measuring that same process. Results We tested this hypothesis directly using a novel iterative-subtractive approach. We evaluated five gene expression data sets that sample a broad range of breast cancer subtypes. In all data sets, the dominant cluster capable of predicting metastasis was heavily populated by genes that fluctuate in concert with the cell cycle. When six well-characterized classifiers were examined, all contained a higher than expected proportion of genes that correlate with this cluster. Furthermore, when the data sets were globally adjusted for the cell cycle cluster, each classifier lost its ability to assign tumors to appropriate high and low risk groups. In contrast, adjusting for other predictive gene clusters did not impact their performance. Conclusion These data indicate that the discriminative ability of breast cancer classifiers is dependent upon genes that correlate with cell cycle progression.
机译：背景技术已经从全球基因表达数据中获得了许多基因列表或“分类器”，这些数据将乳腺癌分为好和坏预后组。这些分子标记的显着特征是它们几乎没有共同的基因，这促使人们推测它们可能使用不同的基因来测量相同的病理生理过程，例如增殖。但是，此假设尚未经过严格测试。如果基于基因的分类器通过测量最少数量的细胞过程而起作用，我们假设可以识别出这些过程的信息基因，并且可以针对这些基因的预测贡献来调整数据集。这样的调整将削弱测量同一过程的任何签名的预测功能。结果我们使用新颖的迭代减法方法直接检验了该假设。我们评估了五个样本广泛的乳腺癌亚型的基因表达数据集。在所有数据集中，能够预测转移的显性基因簇中大量存在与细胞周期一致波动的基因。当检查了六个表征良好的分类器时，所有分类器都包含与该簇相关的基因的比例高于预期。此外，当针对细胞周期群对数据集进行全局调整时，每个分类器都失去了将肿瘤分配给适当的高风险和低风险组的能力。相反，调整其他预测性基因簇不会影响其性能。结论这些数据表明，乳腺癌分类器的判别能力取决于与细胞周期进程相关的基因。

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