Molecular subtyping of bladder cancer using Kohonen self-organizing maps.
Molecular subtyping of bladder cancer using Kohonen self-organizing maps.
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DOI：
10.1002/cam4.217
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发表时间：
2014-10
期刊：
影响因子：
4
通讯作者：
Kaluzewski, Bogdan
中科院分区：
文献类型：
--
作者：
Borkowska, Edyta M.;Kruk, Andrzej;Jedrzejczyk, Adam;Rozniecki, Marek;Jablonowski, Zbigniew;Traczyk, Magdalena;Constantinou, Maria;Banaszkiewicz, Monika;Pietrusinski, Michal;Sosnowski, Marek;Hamdy, Freddie C.;Peter, Stefan;Catto, James W. F.;Kaluzewski, Bogdan
Kohonen自组织映射（SOM）是无监督的人工神经网络（ANN），适用于低密度数据可视化。它们很容易处理变量之间复杂的非线性关系。我们评估了104例患者肿瘤中高级别和低级别BC通路的分子事件。我们比较了统计聚类与SOM的能力，以根据进展到更晚期疾病的风险对肿瘤进行分层。单因素分析显示，肿瘤分期（log rank P = 0.006）和分级（P < 0.001）、HPV DNA（P < 0.004）、9号染色体缺失（P = 0.04）和CDKN 2A基因A148 T多态性（rs 3731249）（P = 0.02）与疾病进展相关。对这些参数的多变量分析表明，肿瘤分级（考克斯回归，P = 0.001，OR 2.9（95% CI 1.6-5.2））和HPV DNA的存在（P = 0.017，OR 3.8（95% CI 1.3-11.4））是进展的唯一独立预测因素。无监督分层聚类将肿瘤分组为离散分支，但未根据无进展生存期进行分层（对数秩P = 0.39）。这些遗传变量被提交给SOM输入神经元。SOM适用于复杂的数据集成，允许结果的轻松可视化，并且可以比分层聚类更稳健地对BC进展进行分层。
Kohonen self-organizing maps (SOMs) are unsupervised Artificial Neural Networks (ANNs) that are good for low-density data visualization. They easily deal with complex and nonlinear relationships between variables. We evaluated molecular events that characterize high- and low-grade BC pathways in the tumors from 104 patients. We compared the ability of statistical clustering with a SOM to stratify tumors according to the risk of progression to more advanced disease. In univariable analysis, tumor stage (log rank P = 0.006) and grade (P < 0.001), HPV DNA (P < 0.004), Chromosome 9 loss (P = 0.04) and the A148T polymorphism (rs 3731249) in CDKN2A (P = 0.02) were associated with progression. Multivariable analysis of these parameters identified that tumor grade (Cox regression, P = 0.001, OR.2.9 (95% CI 1.6–5.2)) and the presence of HPV DNA (P = 0.017, OR 3.8 (95% CI 1.3–11.4)) were the only independent predictors of progression. Unsupervised hierarchical clustering grouped the tumors into discreet branches but did not stratify according to progression free survival (log rank P = 0.39). These genetic variables were presented to SOM input neurons. SOMs are suitable for complex data integration, allow easy visualization of outcomes, and may stratify BC progression more robustly than hierarchical clustering.
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影响因子：
6.6
作者：
Catto, JWF;Abbod, MF;Hamdy, FC
通讯作者：
Hamdy, FC
影响因子：
8.5
作者：
Budayan, Cenk;Dikmen, Irem;Birgonul, M. Talat
通讯作者：
Birgonul, M. Talat
影响因子：
37.3
作者：
Kim WJ;Kim EJ;Kim SK;Kim YJ;Ha YS;Jeong P;Kim MJ;Yun SJ;Lee KM;Moon SK;Lee SC;Cha EJ;Bae SC
通讯作者：
Bae SC
影响因子：
1.2
作者：
Borkowska E;Jędrzejczyk A;Kruk A;Pietrusiński M;Traczyk M;Rożniecki M;Kałużewski B
通讯作者：
Kałużewski B
影响因子：
7.5
作者：
Markey, MK;Lo, JY;Floyd, CE
通讯作者：
Floyd, CE