Fuller Gregory N, Mircean Cristian, Tabus Ioan, Taylor Ellen, Sawaya Raymond, Bruner Janet M, Shmulevich Ilya, Zhang Wei
Department of Pathology, University of Texas M.D. Anderson Cancer Center, Houston, TX 77030, USA.
Oncol Rep. 2005 Sep;14(3):651-6.
Gliomas, the most common brain tumors, are generally categorized into two lineages (astrocytic and oligodendrocytic) and further classified as low-grade (astrocytoma and oligodendroglioma), mid-grade (anaplastic astrocytoma and anaplastic oligodendroglioma), and high-grade (glioblastoma multiforme) based on morphological features. A strict classification scheme has limitations because a specific glioma can be at any stage of the continuum of cancer progression and may contain mixed features. Thus, a more comprehensive classification based on molecular signatures may reflect the biological nature of specific tumors more accurately. In this study, we used microarray technology to profile the gene expression of 49 human brain tumors and applied the k-nearest neighbor algorithm for classification. We first trained the classification gene set with 19 of the most typical glioma cases and selected a set of genes that provide the lowest cross-validation classification error with k=5. We then applied this gene set to the 30 remaining cases, including several that do not belong to gliomas such as atypical meningioma. The results showed that not only does the algorithm correctly classify most of the gliomas, but the detailed voting results also provide more subtle information regarding the molecular similarities to neighboring classes. For atypical meningioma, the voting was equally split among the four classes, indicating a difficulty in placement of meningioma into the four classes of gliomas. Thus, the actual voting results, which are typically used only to decide the winning class label in k-nearest neighbor algorithms, provide a useful method for gaining deeper insight into the stage of a tumor in the continuum of cancer development.
胶质瘤是最常见的脑肿瘤，通常分为两个谱系（星形细胞谱系和少突胶质细胞谱系），并根据形态学特征进一步分为低级别（星形细胞瘤和少突胶质细胞瘤）、中级别（间变性星形细胞瘤和间变性少突胶质细胞瘤）和高级别（多形性胶质母细胞瘤）。严格的分类方案存在局限性，因为特定的胶质瘤可能处于癌症进展连续体的任何阶段，并且可能包含混合特征。因此，基于分子特征的更全面分类可能更准确地反映特定肿瘤的生物学性质。在本研究中，我们使用微阵列技术对49例人脑肿瘤的基因表达进行分析，并应用k近邻算法进行分类。我们首先用19例最典型的胶质瘤病例训练分类基因集，并选择一组在k = 5时提供最低交叉验证分类误差的基因。然后我们将这个基因集应用于其余30例病例，包括一些不属于胶质瘤的病例，如非典型脑膜瘤。结果表明，该算法不仅能正确分类大多数胶质瘤，而且详细的投票结果还提供了有关与相邻类别分子相似性的更细微信息。对于非典型脑膜瘤，在四个类别中的投票平分，这表明将脑膜瘤归入四类胶质瘤存在困难。因此，实际的投票结果，通常仅用于在k近邻算法中决定获胜的类别标签，为深入了解肿瘤在癌症发展连续体中的阶段提供了一种有用的方法。
