Singh Arun D, Sisley Karen, Xu Yaomin, Li Jianbo, Faber Pieter, Plummer Sarah J, Mudhar Hardeep S, Rennie Ian G, Kessler Patricia M, Casey Graham, Williams Bryan G
Department of Ophthalmic Oncology, Cole Eye Institute, Cleveland Clinic Foundation, 9500 Euclid Avenue, Cleveland, OH 44195, USA.
Br J Ophthalmol. 2007 Oct;91(10):1385-92. doi: 10.1136/bjo.2007.116947. Epub 2007 May 2.
In an effort to identify patients with uveal melanoma at high risk of metastasis, the authors undertook correlation of gene expression profiles with histopathology data and tumour-related mortality.
The RNA was isolated from 27 samples of uveal melanoma from patients who had consented to undergo enucleation, and transcripts profiled using a cDNA array comprised of sequence-verified cDNA clones representing approximately 4000 genes implicated in cancer development. Two multivariate data mining techniques--hierarchical cluster analysis and multidimensional scaling--were used to investigate the grouping structure in the gene expression data. Cluster analysis was performed with a subset of 10,000 randomly selected genes and the cumulative contribution of all the genes in making the correct grouping was recorded.
Hierarchical cluster analysis and multidimensional scaling revealed two distinct classes. When correlated with the data on metastasis, the two molecular classes corresponded very well to the survival data for the 27 patients. Thirty two discrete genes (corresponding to 44 probe sets) that correctly defined the molecular classes were selected. A single gene (ectonucleotide pyrophosphatase/phosphodiesterase 2; autotaxin) could classify the molecular types. The expression pattern was confirmed using real-time quantitative PCR.
Gene expression profiling identifies two distinct prognostic classes of uveal melanoma. Underexpression of autotaxin in class 2 uveal melanoma with a poor prognosis needs to be explored further.
为了识别有高转移风险的葡萄膜黑色素瘤患者，作者对基因表达谱与组织病理学数据及肿瘤相关死亡率进行了相关性研究。
从27例同意接受眼球摘除术的葡萄膜黑色素瘤患者的样本中提取RNA，并用由代表约4000个与癌症发展相关基因的经序列验证的cDNA克隆组成的cDNA阵列对转录本进行分析。使用两种多变量数据挖掘技术——层次聚类分析和多维标度分析——来研究基因表达数据中的分组结构。聚类分析使用随机选择的10000个基因的子集进行，并记录所有基因在正确分组中的累积贡献。
层次聚类分析和多维标度分析揭示了两个不同的类别。当与转移数据相关联时，这两个分子类别与27例患者的生存数据非常吻合。选择了32个能正确定义分子类别的离散基因（对应于44个探针组）。单个基因（外核苷酸焦磷酸酶/磷酸二酯酶2；自分泌运动因子）可以对分子类型进行分类。使用实时定量PCR确认了表达模式。
基因表达谱分析确定了葡萄膜黑色素瘤的两种不同的预后类别。2型葡萄膜黑色素瘤中自分泌运动因子表达不足且预后不良，这一情况需要进一步研究。
