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2016 ; 12
(3
): 1792-1800
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Construction of protein interaction network involved in lung adenocarcinomas
using a novel algorithm
#MMPMID27588126
Chen J
; Yang HT
; Li Z
; Xu N
; Yu B
; Xu JP
; Zhao PG
; Wang Y
; Zhang XJ
; Lin DJ
Oncol Lett
2016[Sep]; 12
(3
): 1792-1800
PMID27588126
show ga
Studies that only assess differentially-expressed (DE) genes do not contain the
information required to investigate the mechanisms of diseases. A complete
knowledge of all the direct and indirect interactions between proteins may act as
a significant benchmark in the process of forming a comprehensive description of
cellular mechanisms and functions. The results of protein interaction network
studies are often inconsistent and are based on various methods. In the present
study, a combined network was constructed using selected gene pairs, following
the conversion and combination of the scores of gene pairs that were obtained
across multiple approaches by a novel algorithm. Samples from patients with and
without lung adenocarcinoma were compared, and the RankProd package was used to
identify DE genes. The empirical Bayesian (EB) meta-analysis approach, the search
tool for the retrieval of interacting genes/proteins database (STRING), the
weighted gene coexpression network analysis (WGCNA) package and the
differentially-coexpressed genes and links package (DCGL) were used for network
construction. A combined network was also constructed with a novel rank-based
algorithm using a combined score. The topological features of the 5 networks were
analyzed and compared. A total of 941 DE genes were screened. The topological
analysis indicated that the gene interaction network constructed using the WGCNA
method was more likely to produce a small-world property, which has a small
average shortest path length and a large clustering coefficient, whereas the
combined network was confirmed to be a scale-free network. Gene pairs that were
identified using the novel combined method were mostly enriched in the cell cycle
and p53 signaling pathway. The present study provided a novel perspective to the
network-based analysis. Each method has advantages and disadvantages. Compared
with single methods, the combined algorithm used in the present study may provide
a novel method to analyze gene interactions, with increased credibility.
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