Kan Takatsugu, Shimada Yutaka, Sato Fumiaki, Ito Tetsuo, Kondo Kan, Watanabe Go, Maeda Masato, Yamasaki Seiji, Meltzer Stephen J, Imamura Masayuki
Department of Surgery and Surgical Basic Science, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
Ann Surg Oncol. 2004 Dec;11(12):1070-8. doi: 10.1245/ASO.2004.03.007. Epub 2004 Nov 15.
The aim of the study was (1) to detect candidate genes involved in lymph node metastasis in esophageal cancers and (2) to investigate whether we can estimate and predict occurrence of lymph node metastasis by analyzing artificial neural networks (ANNs) using these gene subsets.
Twenty-eight primary esophageal squamous cell carcinomas were used. Gene expression profiles of all primary tumors were obtained by cDNA microarray. Lymph node metastasis-related genes were extracted with use of Significance Analysis of Microarrays (SAM). Predictive accuracy for lymph node metastasis was calculated by evaluation of 28 cases by ANNs with leave-one-out cross-n. The results were compared with those of other analyses such as clustering or predictive scoring (LMS).
Our ANN model could predict lymph node metastasis most accurately with 60 clones. The highest predictive accuracy for lymph node metastasis by ANN was 10 of 13 (77%) in newly added cases that were not used for gene selection by SAM and 24 of 28 (86%) in all cases (sensitivity: 15/17, 88%; specificity: 9/11, 82%). Predictive accuracy of LMS was 9 of 13 (69%) in newly added cases and 24 of 28 (86%) in all cases (sensitivity: 17/17, 100%; specificity: 7/11, 67%). It was difficult to extract useful information for the prediction of lymph node metastasis by clustering analysis.
ANN had superior potential in comparison with other methods of analysis for the prediction of lymph node metastasis. This systematic analysis combining SAM with ANN was very useful for the prediction of lymph node metastasis in esophageal cancers and could be applied clinically in the near future.
本研究的目的是（1）检测食管癌中参与淋巴结转移的候选基因，以及（2）研究我们是否能够通过使用这些基因子集分析人工神经网络（ANN）来估计和预测淋巴结转移的发生情况。
使用了28例原发性食管鳞状细胞癌。通过cDNA微阵列获得所有原发性肿瘤的基因表达谱。利用微阵列显著性分析（SAM）提取与淋巴结转移相关的基因。通过使用留一法交叉验证的人工神经网络对28例病例进行评估，计算淋巴结转移的预测准确性。将结果与其他分析方法（如聚类或预测评分（LMS））的结果进行比较。
我们的人工神经网络模型使用60个克隆能够最准确地预测淋巴结转移。在未用于SAM基因选择的新添加病例中，人工神经网络对淋巴结转移的最高预测准确率为13例中的10例（77%），在所有病例中为28例中的24例（86%）（敏感性：15/17，88%；特异性：9/11，82%）。LMS在新添加病例中的预测准确率为13例中的9例（69%），在所有病例中为28例中的24例（86%）（敏感性：17/17，100%；特异性：7/11，67%）。通过聚类分析难以提取用于预测淋巴结转移的有用信息。
与其他分析方法相比，人工神经网络在预测淋巴结转移方面具有更大的潜力。这种将SAM与人工神经网络相结合的系统分析对于预测食管癌中的淋巴结转移非常有用，并且在不久的将来可应用于临床。
