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首页> 外文期刊>BMC Genomics >Meta-analytical biomarker search of EST expression data reveals three differentially expressed candidates
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Meta-analytical biomarker search of EST expression data reveals three differentially expressed candidates

机译：EST表达数据的元分析生物标志物搜索揭示了三个差异表达的候选对象

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Background Researches have been conducted for the identification of differentially expressed genes (DEGs) by generating and mining of cDNA expressed sequence tags (ESTs) for more than a decade. Although the availability of public databases make possible the comprehensive mining of DEGs among the ESTs from multiple tissue types, existing studies usually employed statistics suitable only for two categories. Multi-class test has been developed to enable the finding of tissue specific genes, but subsequent search for cancer genes involves separate two-category test only on the ESTs of the tissue of interest. This constricts the amount of data used. On the other hand, simple pooling of cancer and normal genes from multiple tissue types runs the risk of Simpson's paradox. Here we presented a different approach which searched for multi-cancer DEG candidates by analyzing all pertinent ESTs in all categories and narrowing down the cancer biomarker candidates via integrative analysis with microarray data and selection of secretory and membrane protein genes as well as incorporation of network analysis. Finally, the differential expression patterns of three selected cancer biomarker candidates were confirmed by real-time qPCR analysis. Results Seven hundred and twenty three primary DEG candidates (p-value Conclusions Searching digitized transcriptome using CMH enabled us to identify multi-cancer differentially expressed gene candidates. Our methodology demonstrated simultaneously analysis for cancer biomarkers of multiple tissue types with the EST data. With the revived interest in digitizing the transcriptomes by NGS, cancer biomarkers could be more precisely detected from the ESTs. The three candidates identified in this study, COL3A1, DLG3, and RNF43, are valuable targets for further evaluation with a larger sample size of normal and cancer tissue or serum samples.
机译：背景技术已经进行了十多年的研究，以通过产生和挖掘cDNA表达的序列标签（EST）来鉴定差异表达的基因（DEG）。尽管公共数据库的可用性使从多种组织类型的EST中全面提取DEG成为可能，但现有研究通常采用仅适用于两种类别的统计数据。已经开发了多类测试以能够找到组织特异性基因，但是随后的癌症基因搜索仅涉及目标组织的EST上的单独的两类测试。这限制了使用的数据量。另一方面，癌症和来自多种组织类型的正常基因的简单合并会带来辛普森悖论的风险。在这里，我们提出了一种不同的方法，该方法通过分析所有类别中的所有相关EST来搜索多癌DEG候选物，并通过与微阵列数据进行整合分析，选择分泌和膜蛋白基因以及结合网络分析来缩小癌症生物标志物候选物的范围。 。最后，通过实时qPCR分析证实了三种选择的癌症生物标志物候选物的差异表达模式。结果723个主要DEG候选对象（p值）结论使用CMH搜索数字化转录组可以使我们识别多癌差异表达基因候选者。我们的方法论证明了利用EST数据同时分析多种组织类型的癌症生物标志物。再次引起人们对通过NGS对转录组数字化的兴趣，可以从EST中更精确地检测出癌症生物标记物。本研究中确定的三个候选物COL3A1，DLG3和RNF43是有价值的靶标，可用于评估更大样本的正常和癌症组织或血清样品。

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