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首页> 美国卫生研究院文献>Disease Markers >Gene Expression Profiling of Colorectal Tumors and Normal Mucosa by Microarrays Meta-Analysis Using Prediction Analysis of Microarray Artificial Neural Network Classification and Regression Trees
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Gene Expression Profiling of Colorectal Tumors and Normal Mucosa by Microarrays Meta-Analysis Using Prediction Analysis of Microarray Artificial Neural Network Classification and Regression Trees

机译：基于微阵列人工神经网络分类和回归树预测分析的微阵列Meta分析对大肠肿瘤和正常黏膜的基因表达谱

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Background. Microarray technology shows great potential but previous studies were limited by small number of samples in the colorectal cancer (CRC) research. The aims of this study are to investigate gene expression profile of CRCs by pooling cDNA microarrays using PAM, ANN, and decision trees (CART and C5.0). Methods. Pooled 16 datasets contained 88 normal mucosal tissues and 1186 CRCs. PAM was performed to identify significant expressed genes in CRCs and models of PAM, ANN, CART, and C5.0 were constructed for screening candidate genes via ranking gene order of significances. Results. The first screening identified 55 genes. The test accuracy of each model was over 0.97 averagely. Less than eight genes achieve excellent classification accuracy. Combining the results of four models, we found the top eight differential genes in CRCs; suppressor genes, CA7, SPIB, GUCA2B, AQP8, IL6R and CWH43; oncogenes, SPP1 and TCN1. Genes of higher significances showed lower variation in rank ordering by different methods. Conclusion. We adopted a two-tier genetic screen, which not only reduced the number of candidate genes but also yielded good accuracy (nearly 100%). This method can be applied to future studies. Among the top eight genes, CA7, TCN1, and CWH43 have not been reported to be related to CRC.
机译：背景。微阵列技术显示出巨大的潜力，但以前的研究受到结直肠癌（CRC）研究中少量样品的限制。这项研究的目的是通过使用PAM，ANN和决策树（CART和C5.0）合并cDNA微阵列来研究CRC的基因表达谱。方法。汇集的16个数据集包含88个正常粘膜组织和1186个CRC。进行PAM以鉴定CRC中重要的表达基因，并构建PAM，ANN，CART和C5.0模型以通过对基因的重要性进行排序来筛选候选基因。结果。第一次筛选鉴定出55个基因。每个模型的测试准确性平均超过0.97。少于八个基因可实现出色的分类精度。结合四个模型的结果，我们发现了CRC中排名前8位的差异基因。抑制基因，CA7，SPIB，GUCA2B，AQP8，IL6R和CWH43；癌基因，SPP1和TCN1。具有较高重要性的基因通过不同的方法显示出较低的等级排序差异。结论。我们采用了两层遗传筛选，不仅减少了候选基因的数量，而且产生了良好的准确性（接近100％）。该方法可以应用于未来的研究。在前八位基因中，尚未报道CA7，TCN1和CWH43与CRC相关。

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