title: Construction of protein interaction network involved in lung adenocarcinomas using a novel algorithm.
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.
authors: Department of Respiratory Medicine, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, Shandong 250014, P.R. China; Department of Respiratory Medicine, People's Hospital of Liaocheng, Liaocheng, Shandong 252000, P.R. China.
authors: Juan
authors: J
authors: Chen
authors: Department of Respiratory Medicine, People's Hospital of Liaocheng, Liaocheng, Shandong 252000, P.R. China.
authors: Hai-Tao
authors: HT
authors: Yang
authors: Department of Hepatobiliary Surgery, People's Hospital of Liaocheng, Liaocheng, Shandong 252000, P.R. China.
authors: Zhu
authors: Z
authors: Li
authors: Department of Respiratory Medicine, Weihai Municipal Hospital, Weihai, Shandong 264200, P.R. China.
authors: Ning
authors: N
authors: Xu
authors: Department of Respiratory Medicine, People's Hospital of Liaocheng, Liaocheng, Shandong 252000, P.R. China.
authors: Bo
authors: B
authors: Yu
authors: Department of Respiratory Medicine, People's Hospital of Liaocheng, Liaocheng, Shandong 252000, P.R. China.
authors: Jun-Ping
authors: JP
authors: Xu
authors: Department of Respiratory Medicine, People's Hospital of Liaocheng, Liaocheng, Shandong 252000, P.R. China.
authors: Pei-Ge
authors: PG
authors: Zhao
authors: Department of Respiratory Medicine, People's Hospital of Liaocheng, Liaocheng, Shandong 252000, P.R. China.
authors: Yan
authors: Y
authors: Wang
authors: Department of Respiratory Medicine, People's Hospital of Liaocheng, Liaocheng, Shandong 252000, P.R. China.
authors: Xiu-Juan
authors: XJ
authors: Zhang
authors: Department of Respiratory Medicine, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, Shandong 250014, P.R. China.
authors: Dian-Jie
authors: DJ
authors: Lin
keywords: empirical Bayesian; lung adenocarcinomas; protein interaction network; topological analysis; weighted gene coexpression network analysis