Breast cancer is a heterogeneous disease, and its development is closely associated with the underlying molecular regulatory network. In this paper, we propose a new way to measure the regulation strength between genes based on their expression values, and construct the dysregulated networks (DNs) for the four subtypes of breast cancer. Our results show that the key dysregulated networks (KDNs) are significantly enriched in critical breast cancer-related pathways and driver genes; closely related to drug targets; and have significant differences in survival analysis. Moreover, the key dysregulated genes could serve as potential driver genes, drug targets, and prognostic markers for each breast cancer subtype. Therefore, the KDN is expected to be an effective and novel way to understand the mechanisms of breast cancer.
Signalling pathway analysis is a popular approach that is used to identify significant cancer-related pathways based on differentially expressed genes (DEGs) from biological experiments. The main advantage of signalling pathway analysis lies in the fact that it assesses both the number of DEGs and the propagation of signal perturbation in signalling pathways. However, this method simplifies the interactions between genes by categorising them only as activation (+1) and suppression (-1), which does not encompass the range of interactions in real pathways, where interaction strength between genes may vary. In this study, the authors used newly developed signalling pathway impact analysis (SPIA) methods, SPIA based on Pearson correlation coefficient (PSPIA), and mutual information (MSPIA), to measure the interaction strength between pairs of genes. In analyses of a colorectal cancer dataset, a lung cancer dataset, and a pancreatic cancer dataset, PSPIA and MSPIA identified more candidate cancer-related pathways than were identified by SPIA. Generally, MSPIA performed better than PSPIA.
Gene set analysis using signaling pathway has become a popular downstream analysis following differential expression analysis. From a biological point of view, only some portions of a pathway are expected to be altered; however, a few approaches using the different importance of genes in signaling pathways, which encompass the constitutive functional nonequivalent roles of genes in real pathways, have been proposed and none of them tries to associate the importance of genes with the related disease. In this paper, we developed an extended method of signaling pathway impact analysis (SPIA), called gwSPIA, by incorporating three signaling pathway-based gene weight merits that reflect the importance of genes from different aspects and attempt to associate the importance of genes with the related diseases. By applying the gwSPIA to the gene expression data sets in comparison with other seven methods in three measures, sensitivity, prioritization, and specificity, we show that the gwSPIA ranks in the second place in both sensitivity and prioritization. Furthermore, the specificity of the gwSPIA is better than SPIA, which is lower than 25%. The results also suggest that the gene weight used in the gwSPIA can reflect the association between the genes and the related diseases. The R package of the gwSPIA can be accessed from https://github.com/sterding/gwSPIA. INDEX TERMS Differentially expressed genes, gene weights, gwSPIA, signaling pathways analysis.
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