A merged microarray meta-dataset for transcriptionally profiling colorectal neoplasm formation and progression.
A merged microarray meta-dataset for transcriptionally profiling colorectal neoplasm formation and progression.
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DOI：
10.1038/s41597-021-00998-5
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发表时间：
2021-08-11
期刊：
影响因子：
9.8
通讯作者：
Altomare D
中科院分区：
文献类型：
--
作者：
Rohr M;Beardsley J;Nakkina SP;Zhu X;Aljabban J;Hadley D;Altomare D
恶性结直肠癌（CRC）病变前和后的转录谱能够对肿瘤进展的分子事件进行时间监测。然而，最广泛使用的CRC转录组数据集TCGA-COAD缺乏腺瘤样本，这增加了对各种不同微阵列研究的依赖，并阻碍了共识的建立。为了解决这个问题，我们开发了一个微阵列元数据集，包括来自12个独立研究的231个健康，132个腺瘤和342个CRC组织样本。利用严格的分析框架，从Gene Expression Omnibus下载选择的数据集，通过冷冻稳健多阵列平均进行归一化，随后合并。然后通过经验贝叶斯估计（ComBat）确定并去除批次效应。最后，对元数据集进行低变异探针过滤，分别通过跨平台相关性和富集分析实现下游差异表达以及定量和功能验证。总的来说，我们的元数据集提供了一个强大的工具，用于在转录水平上研究结直肠腺瘤形成和恶性转化，其管道是模块化的，易于适应其他癌症类型的类似分析。描述报告数据的机器可访问元数据文件：10.6084/m9.figshare.14589006
Transcriptional profiling of pre- and post-malignant colorectal cancer (CRC) lesions enable temporal monitoring of molecular events underlying neoplastic progression. However, the most widely used transcriptomic dataset for CRC, TCGA-COAD, is devoid of adenoma samples, which increases reliance on an assortment of disparate microarray studies and hinders consensus building. To address this, we developed a microarray meta-dataset comprising 231 healthy, 132 adenoma, and 342 CRC tissue samples from twelve independent studies. Utilizing a stringent analytic framework, select datasets were downloaded from the Gene Expression Omnibus, normalized by frozen robust multiarray averaging and subsequently merged. Batch effects were then identified and removed by empirical Bayes estimation (ComBat). Finally, the meta-dataset was filtered for low variant probes, enabling downstream differential expression as well as quantitative and functional validation through cross-platform correlation and enrichment analyses, respectively. Overall, our meta-dataset provides a robust tool for investigating colorectal adenoma formation and malignant transformation at the transcriptional level with a pipeline that is modular and readily adaptable for similar analyses in other cancer types. Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.14589006
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