Gene Regulation Network Based Analysis Associated with TGF-beta Stimulation in Lung Adenocarcinoma Cells

Background: 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. Objectives: 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. Materials and Methods: 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. Results: 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-β1 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-β1 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. Conclusions: 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.