No abstract

The development of sequencing technology has promoted the expansion of cancer genome data. It is necessary to identify the pathogenesis of cancer at the molecular level and explore reliable treatment methods and precise drug targets in cancer by identifying carcinogenic functional modules in massive multi‐omics data. However, there are still limitations to identifying carcinogenic driver modules by utilising genetic characteristics simply. Therefore, this study proposes a computational method, NetAP, to identify driver modules in prostate cancer. Firstly, high mutual exclusivity, high coverage, and high topological similarity between genes are integrated to construct a weight function, which calculates the weight of gene pairs in a biological network. Secondly, the random walk method is utilised to reevaluate the strength of interaction among genes. Finally, the optimal driver modules are identified by utilising the affinity propagation algorithm. According to the results, the authors’ method identifies more validated driver genes and driver modules compared with the other previous methods. Thus, the proposed NetAP method can identify carcinogenic driver modules effectively and reliably, and the experimental results provide a powerful basis for cancer diagnosis, treatment and drug targets.

Cancer is generally caused by genetic mutations, and differentially expressed genes are closely associated with genetic mutations. Therefore, mutated genes and differentially expressed genes can be used to study the dysregulated modules in cancer. However, it has become a big challenge in cancer research how to accurately and effectively detect dysregulated modules that promote cancer in massive data. In this study, we propose a network-based method for identifying dysregulated modules (Netkmeans). Firstly, the study constructs an undirected-weighted gene network based on the characteristics of high mutual exclusivity, high coverage and complex network topology among genes widely existed in the genome. Secondly, the study constructs a comprehensive evaluation function to select the number of clusters scientifically and effectively. Finally, the K-means clustering method is applied to detect the dysregulated modules. Compared with the results detected by IBA and CCEN methods, the results of Netkmeans proposed in this study have higher statistical significance and biological relevance. Besides, compared with the dysregulated modules detected by MCODE, CFinder and ClusterONE, the results of Netkmeans have higher accuracy, precision and F-measure. The experimental results show that the multiple dysregulated modules detected by Netkmeans are essential in the generation, development and progression of cancer, and thus they play a vital role in the precise diagnosis, treatment and development of new medications for cancer patients.