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Construction of protein interaction network involved in lung adenocarcinomas using a novel algorithm

View Article: PubMed Central - PubMed

ABSTRACT

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.

No MeSH data available.


Combined gene interaction network based on the novel scores of each gene pairs across 4 methods. Genes were denoted as nodes and interactions between gene pairs were presented as edges (lines) in the image. A total of 280 nodes and 515 edges composed the combined network.
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f2-ol-0-0-4822: Combined gene interaction network based on the novel scores of each gene pairs across 4 methods. Genes were denoted as nodes and interactions between gene pairs were presented as edges (lines) in the image. A total of 280 nodes and 515 edges composed the combined network.

Mentions: The protein interaction networks of DE genes were constructed using EB, STRING, DCGL and WGCNA (Fig. 1), and the association between gene pairs was determined. Subsequently, a novel algorithm was implemented to combine the score values of all gene pairs obtained from the 4 existing approaches. A novel matrix with a combined score of each gene pair was produced and a simple rank-based permutation procedure was used. Next, the combined gene interaction network was also constructed, consisting of 280 nodes and 515 edges (Fig. 2).


Construction of protein interaction network involved in lung adenocarcinomas using a novel algorithm
Combined gene interaction network based on the novel scores of each gene pairs across 4 methods. Genes were denoted as nodes and interactions between gene pairs were presented as edges (lines) in the image. A total of 280 nodes and 515 edges composed the combined network.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4998145&req=5

f2-ol-0-0-4822: Combined gene interaction network based on the novel scores of each gene pairs across 4 methods. Genes were denoted as nodes and interactions between gene pairs were presented as edges (lines) in the image. A total of 280 nodes and 515 edges composed the combined network.
Mentions: The protein interaction networks of DE genes were constructed using EB, STRING, DCGL and WGCNA (Fig. 1), and the association between gene pairs was determined. Subsequently, a novel algorithm was implemented to combine the score values of all gene pairs obtained from the 4 existing approaches. A novel matrix with a combined score of each gene pair was produced and a simple rank-based permutation procedure was used. Next, the combined gene interaction network was also constructed, consisting of 280 nodes and 515 edges (Fig. 2).

View Article: PubMed Central - PubMed

ABSTRACT

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.

No MeSH data available.