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首页> 美国卫生研究院文献>The American Journal of Pathology >Accurate Molecular Classification of Human Cancers Based on Gene Expression Using a Simple Classifier with a Pathological Tree-Based Framework
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Accurate Molecular Classification of Human Cancers Based on Gene Expression Using a Simple Classifier with a Pathological Tree-Based Framework

机译：基于具有病理树基框架的简单分类器基于基因表达的人类癌症精确分子分类

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摘要

Recent studies suggest accurate prediction of tissue of origin for human cancers can be achieved by applying sophisticated statistical learning procedures to gene expression data obtained from DNA microarrays. We have pursued the hypothesis that a more straightforward and equally accurate strategy for classifying human tumors is to use a simple algorithm that considers gene expression levels within a tree-based framework that encodes limited information about pathology and tissue ontogeny. By considering gene expression data within this framework, we found only a small number of genes were required to achieve a relatively high accuracy level in tumor classification. Using as few as 45 genes we were able to classify 157 of 190 human malignant tumors correctly, which is comparable to previous results obtained with sophisticated classifiers using thousands of genes. Our simple classifier accurately predicted the origin of metastatic tumors even when the classifier was trained using only primary tumors, and the classifier produced accurate predictions when trained and tested on expression data from different labs, and from different microarray platforms. Our findings suggest that accurate and robust cancer diagnosis from gene expression profiles can be achieved by mimicking the classification strategies routinely used by surgical pathologists.
机译：最近的研究表明，可以通过将复杂的统计学习程序应用于从DNA微阵列获得的基因表达数据，来实现对人类癌症起源组织的准确预测。我们已经提出了这样的假设：对人类肿瘤进行分类的更直接且同样准确的策略是使用一种简单的算法，该算法考虑基于树的框架内的基因表达水平，该框架编码有关病理学和组织个体学的有限信息。通过在此框架内考虑基因表达数据，我们发现仅需要少数基因即可实现肿瘤分类中相对较高的准确度。使用最少的45个基因，我们就能正确地分类190个人类恶性肿瘤中的157个，这与以前使用数千个基因的复杂分类器获得的结果相当。即使仅使用原发性肿瘤训练分类器，我们的简单分类器仍可准确预测转移性肿瘤的起源，并且当对来自不同实验室和不同微阵列平台的表达数据进行训练和测试时，分类器可产生准确的预测。我们的发现表明，通过模仿手术病理学家常规使用的分类策略，可以从基因表达谱进行准确而可靠的癌症诊断。

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