System for predicting metastasis of cancer by statistical pattern recognition with microarrays.

2004年，我们研究了食管鳞癌(ESCCs)的基因表达谱与侵袭深度和淋巴转移程度的关系。我们使用高密度寡核苷酸芯片(Affymetrix)检测了54例食管鳞癌和11例非癌组织中22115个基因的表达。我们的结果表明，不同侵袭深度的食管癌表达不同的与淋巴结转移相关的基因，这表明在根据基因表达谱预测食管癌淋巴转移时，必须考虑侵袭深度的程度。2005年，我们对开发微阵列预测系统的研究进行了全面的综述。在人类基因组分析中，与要检测的基因相比，我们拥有的样本很少。样本数量少给预测系统带来了实际困难。根据在肝细胞癌方面取得的许多结果，我们研究了克服这些困难的策略。最重要的是，应该解决基因选择问题，而不是分类器设计问题。特别是，处理小样本量引起的变异性的方法是至关重要的。这导致了强大的基因选择。此外，我们还指出，在开发预测系统时，使用监督学习的方法是正确的。

In 2004, we examined gene expression profiles of esophageal squamous carcinomas (ESCCs) with respect to degree of invasive depth and lymph node involvement. We used high-density oligonucleotide microarrays (Affymetrix) to examine expression of 22115 genes in 54 ESCCs and 11 non-cancerous esophageal tissues. Our results show that ESCCs with distinct invasive depths express different genes linked to lymph node metastasis, suggesting that the degree of invasive depth must be considered when predicting lymph node metastasis of ESCCs from gene expression profiles.In 2005, we studied a comprehensive review for developing a predictive system with microarrays. In human genome analysis, we have few samples, compared with the genes to be examined. The small number of samples causes the practical difficulties in a predictive system. On the base of the many results obtained with hepatocellular carcinoma, we studied our strategy to overcome these difficulties. Most importantly, gene selection should be addressed, rather than classifier design. In particular, the way to cope with the variability due to the small sample size is critical. This leads to the robust gene selection. Moreover, we point out that the approach of using supervised learning is right when developing a predictive system.