Study of integrated heterogeneous data reveals prognostic power of gene expression for breast cancer survival.
Study of integrated heterogeneous data reveals prognostic power of gene expression for breast cancer survival.
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DOI：
10.1371/journal.pone.0117658
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发表时间：
2015
期刊：
影响因子：
3.7
通讯作者：
Jiang X
中科院分区：
文献类型：
--
作者：
Neapolitan RE;Jiang X
研究表明，数以千计的基因与乳腺癌的预后有关。为了利用现有的遗传数据，已经努力利用基因表达数据来预测结果，并且已经开发了许多商业产品。这些产品存在以下缺点：1)使用Cox模型进行预测。然而，RSF模型已被证明明显优于Cox模型。2)没有进行测试，看看一套完整的临床预测指标是否可以预测以及基因表达特征。我们解决了这些缺点。METABRIC数据集涉及1981年乳腺癌肿瘤。特征包括21个临床特征、16384个基因的表达水平和生存率。我们将使用临床数据和基因表达数据的Cox模型和RSF模型的生存预测性能与仅使用临床数据的性能进行了比较。当我们同时使用临床数据和基因表达数据进行5年、10年和15年的生存预测时，我们获得了明显更好的结果。当我们用PAM50亚型代替基因表达数据时，我们的结果仅对5年和15年的预测有意义。我们使用RSF模型比Cox模型获得了明显更好的结果。最后，我们的结果表明，基因表达数据单独可以预测长期生存。我们的研究结果表明，与仅使用临床数据进行预测相比，使用临床数据和基因表达数据可以获得更好的生存预测。我们进一步得出结论，我们可以使用RSF模型而不是Cox模型来获得更好的生存预测。这些结果具有重要意义，因为通过将更多的基因表达数据与临床特征结合起来，并使用RSF模型，我们可以开发决策支持系统，更好地利用异构信息来改进结果预测和决策。
Studies show that thousands of genes are associated with prognosis of breast cancer. Towards utilizing available genetic data, efforts have been made to predict outcomes using gene expression data, and a number of commercial products have been developed. These products have the following shortcomings: 1) They use the Cox model for prediction. However, the RSF model has been shown to significantly outperform the Cox model. 2) Testing was not done to see if a complete set of clinical predictors could predict as well as the gene expression signatures. We address these shortcomings. The METABRIC data set concerns 1981 breast cancer tumors. Features include 21 clinical features, expression levels for 16,384 genes, and survival. We compare the survival prediction performance of the Cox model and the RSF model using the clinical data and the gene expression data to their performance using only the clinical data. We obtain significantly better results when we used both clinical data and gene expression data for 5 year, 10 year, and 15 year survival prediction. When we replace the gene expression data by PAM50 subtype, our results are significant only for 5 year and 15 year prediction. We obtain significantly better results using the RSF model over the Cox model. Finally, our results indicate that gene expression data alone may predict long-term survival. Our results indicate that we can obtain improved survival prediction using clinical data and gene expression data compared to prediction using only clinical data. We further conclude that we can obtain improved survival prediction using the RSF model instead of the Cox model. These results are significant because by incorporating more gene expression data with clinical features and using the RSF model, we could develop decision support systems that better utilize heterogeneous information to improve outcome prediction and decision making.
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影响因子：
50.3
作者：
Neve, Richard M.;Chin, Koei;Gray, Joe W.
通讯作者：
Gray, Joe W.
影响因子：
2
作者：
Lowsky, D. J.;Ding, Y.;Zenios, S. A.
通讯作者：
Zenios, S. A.
DOI：
10.1073/pnas.0932692100
发表时间：
2003-07-08
影响因子：
11.1
作者：
Sorlie, T;Tibshirani, R;Botstein, D
通讯作者：
Botstein, D
影响因子：
7.5
作者：
Robnik-Sikonja, M;Kononenko, I
通讯作者：
Kononenko, I
影响因子：
4.4
作者：
Hu, Zhiyuan;Fan, Cheng;Perou, Charles M.
通讯作者：
Perou, Charles M.