Supplementary Issue: Network and Pathway Analysis of Cancer Susceptibility (a)
Supplementary Issue: Network and Pathway Analysis of Cancer Susceptibility (a)
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全转录组鸟枪测序（RNA-Seq）是用于分析生物样品的转录组的有用工具。通过适当的统计和生物信息学处理，该平台能够识别转录组内基因表达的显着差异，并允许进行途径和网络分析，以确定这些基因如何在生物学上相互作用。在这项研究中，我们检测了两种肺腺癌细胞系（H358和A459）的基因表达，这些细胞系用转化生长因子-β（TGF-β）处理，作为诱导上皮向间质转化（EMT）的模型，EMT通常与疾病进展相关。我们进行这项研究，以说明一个工作流程，用于识别有趣的基因和过程，在EMT的早期调节，并确定其基因通路/网络的关系和调节。由此，我们鉴定了TGF-β处理后两种细胞系共有的137个上调和32个下调基因，这些基因代表了与诱导EMT相关的多种经典途径和生物网络的组成部分。这些发现还针对来自检查患者队列（总共n = 731）中的转移进展的多项试验的重新定位的Affyellow U133 a表达谱进行了验证，以进一步建立模型系统的临床相关性和转化意义。总之，这些发现有助于验证TGF-β模型对EMT研究的相关性，并为EMT的早期事件提供新的见解。
Whole transcriptome shotgun sequencing (RNA-Seq) is a useful tool for analyzing the transcriptome of a biological sample. With appropriate statistical and bioinformatic processing, this platform is capable of identifying significant differences in gene expression within the transcriptome and permits pathway and network analyses to determine how these genes interact biologically. In this study, we examined gene expression in two lung adenocarcinoma cell lines (H358 and A459) that were treated with transforming growth factor-β (TGF-β) as a model for induction of the epithelial-to-mesenchymal transition (EMT), commonly associated with disease progression. We performed this study in order to illustrate a workflow for identifying interesting genes and processes that are regulated early in EMT and to determine their gene pathway/network relationships and regulation. With this, we identified 137 upregulated and 32 downregulated genes common to both cell lines after TGF-β treatment that represent components of multiple canonical pathways and biological networks associated with the induction of EMT. These findings were also verified against reposited Affymetrix U133a expression profiles from multiple trials examining metastatic progression in patient cohorts (n = 731 total) to further establish the clinical relevance and translational significance of the model system. Together, these findings help validate the relevance of the TGF-β model for the study of EMT and provide new insights into early events in EMT.