Development of a Predictor for Human Brain Tumors Based on Gene Expression Values Obtained from Two Types of Microarray Technologies
Development of a Predictor for Human Brain Tumors Based on Gene Expression Values Obtained from Two Types of Microarray Technologies
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DOI：
10.1089/omi.2009.0093
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发表时间：
2010-04-01
影响因子：
3.3
通讯作者：
Arus, Carles
中科院分区：
文献类型：
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作者：
Castells, Xavier;Jose Acebes, Juan;Arus, Carles
开发能够可靠区分脑肿瘤不同亚型的分子诊断是后基因组医学和临床肿瘤学中重要的未满足的临床需求。一个简单的线性公式，从基因表达值的四个基因（GFAP，PTPRZ 1，GPM6B，PRELP）测量的cDNA微阵列（n = 35）区分胶质母细胞瘤和脑膜瘤的情况下，在以前的研究。我们在此进一步扩展了这项工作，并报告说，上述预测公式显示出其鲁棒性时，应用于Affytron微阵列数据在我们的实验室（n = 80）以及公开获得的数据（n = 98）。重要的是，在使用我们实验室获得的Affyssin数据后，GFAP和GPM6B在预测模型中均被保留为显著的，而其他两个预测基因是SFRP 2和SLC 6A2。这些结果共同表明了从所测试的两种类型的微阵列技术中取样的GFAP和GPM6B基因的表达值的重要性。在这些情况下获得的高预测准确性证明了所使用的微阵列平台上的预测因子的稳健性。这一结果需要更多的脑膜瘤和胶质母细胞瘤病例进一步验证。无论如何，这项研究为基因签名进一步应用于更严格的活检鉴别挑战铺平了道路。
Development of molecular diagnostics that can reliably differentiate amongst different subtypes of brain tumors is an important unmet clinical need in postgenomics medicine and clinical oncology. A simple linear formula derived from gene expression values of four genes (GFAP, PTPRZ1, GPM6B, and PRELP) measured from cDNA microarrays (n = 35) have distinguished glioblastoma and meningioma cases in a previous study. We herein extend this work further and report that the above predictor formula showed its robustness when applied to Affymetrix microarray data acquired prospectively in our laboratory (n = 80) as well as publicly available data (n = 98). Importantly, GFAP and GPM6B were both retained as being significant in the predictive model upon using the Affymetrix data obtained in our laboratory, whereas the other two predictor genes were SFRP2 and SLC6A2. These results collectively indicate the importance of the expression values of GFAP and GPM6B genes sampled from the two types of microarray technologies tested. The high prediction accuracy obtained in these instances demonstrates the robustness of the predictors across microarray platforms used. This result would require further validation with a larger population of meningioma and glioblastoma cases. At any rate, this study paves the way for further application of gene signatures to more stringent biopsy discrimination challenges.