Identification of high-quality cancer prognostic markers and metastasis network modules.
Identification of high-quality cancer prognostic markers and metastasis network modules.
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DOI：
10.1038/ncomms1033
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发表时间：
2010-07-13
影响因子：
16.6
通讯作者：
Wang, Edwin
中科院分区：
文献类型：
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作者：
Li, Jie;Lenferink, Anne E. G.;Deng, Yinghai;Collins, Catherine;Cui, Qinghua;Purisima, Enrico O.;O'Connor-McCourt, Maureen D.;Wang, Edwin
人们对试图鉴定预测癌症存活的基因表达特征有很大的兴趣。在这项研究中，开发了一种新的算法来分析基因表达数据集，可以将ER+和ER−乳腺癌准确地分类为低风险和高风险组。由于未能识别低风险癌症患者，癌症患者往往被过度治疗。到目前为止，还没有算法能够成功地生成具有高准确性和鲁棒性的癌症预后基因签名，以识别这些患者。在本文中，我们开发了一种算法，确定预后标志物使用肿瘤基因微阵列集中在转移驱动基因表达信号。将该算法应用于乳腺癌样本，确定了雌激素受体（ER）阴性（−）和阳性（+）亚型的预后基因特征集。这些特征的组合使用允许在训练集和包含1，375个样本的8个独立测试集中将患者分层为低风险，中等风险和高风险组。对低危组的预测准确率达到87- 100%。整合网络分析确定了模块，其中每个模块包含签名的基因及其直接相互作用的伴侣，即癌症驱动突变基因。这些模块在许多乳腺肿瘤中是复发性的，并有助于转移。
There has been great interest in attempting to identify gene expression signatures that predict cancer survival. In this study a new algorithm is developed to analyse gene expression datasets that accurately classify both ER+ and ER− breast cancers into low- and high-risk groups. Cancer patients are often overtreated because of a failure to identify low-risk cancer patients. Thus far, no algorithm has been able to successfully generate cancer prognostic gene signatures with high accuracy and robustness in order to identify these patients. In this paper, we developed an algorithm that identifies prognostic markers using tumour gene microarrays focusing on metastasis-driving gene expression signals. Application of the algorithm to breast cancer samples identified prognostic gene signature sets for both estrogen receptor (ER) negative (−) and positive (+) subtypes. A combinatorial use of the signatures allowed the stratification of patients into low-, intermediate- and high-risk groups in both the training set and in eight independent testing sets containing 1,375 samples. The predictive accuracy for the low-risk group reached 87–100%. Integrative network analysis identified modules in which each module contained the genes of a signature and their direct interacting partners that are cancer driver-mutating genes. These modules are recurrent in many breast tumours and contribute to metastasis.
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影响因子：
168.9
作者：
Wang, YX;Klijn, JGM;Foekens, JA
通讯作者：
Foekens, JA
影响因子：
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作者：
Cui Q;Purisima EO;Wang E
通讯作者：
Wang E
影响因子：
158.5
作者：
van de Vijver, MJ;He, YD;Bernards, R
通讯作者：
Bernards, R
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：
9.9
作者：
通讯作者：
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