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

Lin, Hua; Xia, Hong; Wei-ying, Zheng; An, Li

doi:10.15171/ijb.1308

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

Document Type : Research Paper

Authors

¹ Biomedical Engineering Institute of Capital Medical University, Beijing, China

² School of Biomedical Engineering, Capital Medical University, Beijing, 100069, China

³ Beijing Institute of Respiratory Medicine, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, 100028, China

10.15171/ijb.1308

Abstract

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.

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