Limits...
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.


Scatter-gram of gene degree in the combined network. The combined network is a scale-free network of which the degree distribution followed a power law (y = axb, where a=121.0, b=−1.315) with the highest fitting coefficient (R2=0.977).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4998145&req=5

f3-ol-0-0-4822: Scatter-gram of gene degree in the combined network. The combined network is a scale-free network of which the degree distribution followed a power law (y = axb, where a=121.0, b=−1.315) with the highest fitting coefficient (R2=0.977).

Mentions: Network analysis showed that 4/5 networks exhibited the scale-free property, with a degree distribution that follows the power law with high fitting coefficients R2, with the exception of the network constructed using the WGCNA method (R2=0.264). The combined network showed the highest fitting coefficient (R2=0.977) compared with the other 4 networks (Fig. 3), which indicates the evident scale-free property and increased robustness against the random failure of the network, compared with the other networks. However, the network constructed by the WGCNA method was more likely to be a small-world network, with the smallest mean shortest path length (1.783) and the largest clustering coefficient (0.813). The detailed parameters of the 5 networks are shown in Table II.


Construction of protein interaction network involved in lung adenocarcinomas using a novel algorithm
Scatter-gram of gene degree in the combined network. The combined network is a scale-free network of which the degree distribution followed a power law (y = axb, where a=121.0, b=−1.315) with the highest fitting coefficient (R2=0.977).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f3-ol-0-0-4822: Scatter-gram of gene degree in the combined network. The combined network is a scale-free network of which the degree distribution followed a power law (y = axb, where a=121.0, b=−1.315) with the highest fitting coefficient (R2=0.977).
Mentions: Network analysis showed that 4/5 networks exhibited the scale-free property, with a degree distribution that follows the power law with high fitting coefficients R2, with the exception of the network constructed using the WGCNA method (R2=0.264). The combined network showed the highest fitting coefficient (R2=0.977) compared with the other 4 networks (Fig. 3), which indicates the evident scale-free property and increased robustness against the random failure of the network, compared with the other networks. However, the network constructed by the WGCNA method was more likely to be a small-world network, with the smallest mean shortest path length (1.783) and the largest clustering coefficient (0.813). The detailed parameters of the 5 networks are shown in Table II.

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.