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2017 ; 15
(1
): 1-9
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Gene Regulation Network Based Analysis Associated with TGF-?eta Stimulation in
Lung Adenocarcinoma Cells
#MMPMID28959347
Hua L
; Xia H
; Zheng WY
; An L
Iran J Biotechnol
2017[Mar]; 15
(1
): 1-9
PMID28959347
show ga
BACKGROUND: Transforming growth factor (TGF)-? is over-expressed in a wide
variety of cancers such as lung adenocarcinoma. TGF-? 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-? stimulation and the effect of TGF-?
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-? 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-?(1) 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-?(1) for 12
hours. Additionally, by integrating two datasets, we specially identified several
nested clusters which included those genes regulated by TGF-? stimulation in lung
adenocarcinoma cells. CONCLUSIONS: Our analysis can help a better understanding
regarding how TGF-? stimulation causes the expression change of a number of the
genes and provide a novel insight into TGF-? stimulation effect on lung
adenocarcinoma cells.