Hua Lin, Xia Hong, Zheng Wei-Ying, An Li
School of Biomedical Engineering, Capital Medical University, Beijing, 100069, China.
Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, Beijing, 100069, China.
Iran J Biotechnol. 2017 Mar;15(1):1-9. doi: 10.15171/ijb.1308.
Transforming growth factor (TGF)-β is over-expressed in a wide variety of cancers such as lung adenocarcinoma. TGF-β plays a major role in cancer progression through regulating cancer cell proliferation and remodeling of the tumor micro-environment. However, it is still a great challenge to explain the phenotypic effects caused by TGF-β stimulation and the effect of TGF-β stimulation on tumor micro-environment.
To address this issue, in the present study we used two time-course microarray data in human lung adenocarcinoma cells and applied bioinformatics methods to explore the gene regulation network responding to TGF-β stimulation in lung adenocarcinoma cells.
The time-dependent reverse-engineering method, protein-protein interaction network analyses, and calculation of the similarity measures between the links were used to construct gene regulatory network and to extract gene clusters.
Utilizing the constructed gene regulation network, we predicted NEFL and LUC7A show the opposite and the same change with C21orf90 if HAND2 is knocked-out after treatment with TGF-β for 4 hours and for 12 hours respectively. FGG and HSPC009 are predicted to display the opposite change with NEFL if CSMD1 is knocked out after treatment with TGF-β for 12 hours. Additionally, by integrating two datasets, we specially identified several nested clusters which included those genes regulated by TGF-β stimulation in lung adenocarcinoma cells.
Our analysis can help a better understanding regarding how TGF-β stimulation causes the expression change of a number of the genes and provide a novel insight into TGF-β stimulation effect on lung adenocarcinoma cells.
转化生长因子（TGF）-β在多种癌症（如肺腺癌）中过度表达。TGF-β通过调节癌细胞增殖和肿瘤微环境重塑在癌症进展中起主要作用。然而，解释TGF-β刺激引起的表型效应以及TGF-β刺激对肿瘤微环境的影响仍然是一个巨大的挑战。
为了解决这个问题，在本研究中，我们使用了人肺腺癌细胞中的两个时间进程微阵列数据，并应用生物信息学方法来探索肺腺癌细胞中对TGF-β刺激作出反应的基因调控网络。
采用时间依赖性逆向工程方法、蛋白质-蛋白质相互作用网络分析以及链接之间相似性度量的计算来构建基因调控网络并提取基因簇。
利用构建的基因调控网络，我们预测分别在TGF-β处理4小时和12小时后敲除HAND2时，NEFL和LUC7A与C21orf90的变化相反和相同。预测在TGF-β处理12小时后敲除CSMD1时，FGG和HSPC009与NEFL的变化相反。此外，通过整合两个数据集，我们特别鉴定了几个嵌套簇，其中包括肺腺癌细胞中受TGF-β刺激调控的那些基因。
我们的分析有助于更好地理解TGF-β刺激如何导致许多基因的表达变化，并为TGF-β对肺腺癌细胞的刺激效应提供新的见解。
