Kourou Konstantina, Papaloukas Costas, Fotiadis Dimitrios I
Annu Int Conf IEEE Eng Med Biol Soc. 2017 Jul;2017:3876-3879. doi: 10.1109/EMBC.2017.8037703.
We propose a meta-analysis scheme for identifying differentially expressed genes in Oral Squamous Cell Carcinoma (OSCC) from different microarray studies. We detect a subset of relevant features and further classify samples under two experimental conditions (i.e healthy and cancer samples) for better patient stratification. A well-established meta-analysis method is adopted and gene expression data sets are derived from a public functional genomics data repository. Our primary aim is the accurate identification of up- and down-regulated genes in order to extract valuable biological information concerning the changes in expression between healthy and cancer samples. According to our results and the extracted informative gene list, a high classification accuracy of healthy and OSCC tumors is achieved with as few genes as possible. Furthermore, the proposed scheme implies that the combination of datasets from different origins may reduce the estimated percentage of false predictions, while the power of gene identification and disease classification is increased.
我们提出了一种荟萃分析方案，用于从不同的微阵列研究中识别口腔鳞状细胞癌（OSCC）中差异表达的基因。我们检测了相关特征的一个子集，并在两种实验条件下（即健康样本和癌症样本）对样本进行进一步分类，以实现更好的患者分层。我们采用了一种成熟的荟萃分析方法，基因表达数据集来自一个公共功能基因组学数据存储库。我们的主要目标是准确识别上调和下调基因，以便提取有关健康样本和癌症样本之间表达变化的有价值的生物学信息。根据我们的结果和提取的信息基因列表，使用尽可能少的基因就能实现健康肿瘤和OSCC肿瘤的高分类准确率。此外，所提出的方案表明，来自不同来源的数据集的组合可能会降低估计的错误预测百分比，同时提高基因识别和疾病分类的能力。
