Abstract
In this study, we investigated cancer cellular networks in the context of gene interactions and their associated patterns in order to recognize the structural features underlying this disease. We aim to propose that the quest of understanding cancer takes us beyond pairwise interactions between genes to a higher-order construction. We characterize the most prominent network deviations in the gene interaction patterns between cancer and normal samples that contribute to the complexity of this disease. What we hope is that through understanding these interaction patterns we will notice a deeper structure in the cancer network. This study uncovers the significant deviations that topological features in cancerous cells show from the healthy one, where the last stage of filtration confirms the importance of one-dimensional holes (topological loops) in cancerous cells and two-dimensional holes (topological voids) in healthy cells. In the small threshold region, the drop in the number of connected components of the cancer network, along with the rise in the number of loops and voids, all occurring at some smaller weight values compared to the normal case, reveals the cancerous network tendency to certain pathways.
Highlights
In this study, we investigated cancer cellular networks in the context of gene interactions and their associated patterns in order to recognize the structural features underlying this disease
By analyzing the interaction networks from the topological point of view, we aim to uncover prominent insights into cellular gene interaction patterns. To this end, applying the Persistent Homology (PH) technique on the weighted complex networks of the normal and cancerous data sets, we analyze the evolution of the dimension of the k-homology group of the topological space; where these Betti numbers demonstrate the number of k-dimensional topological holes
In this study, according to our results, we propose Topological Data Analysis (TDA) can be employed to associate cancer cell proliferation to numbers and the evolution of topological features, so as to study this disease from the viewpoint of patterns of genes’ interaction in order to confirm how local topological modifications may contribute to global features and propose examining the patterns of interactions as a general and global picture as an alternative to studying single genes and their pairwise interactions
Summary
We investigated cancer cellular networks in the context of gene interactions and their associated patterns in order to recognize the structural features underlying this disease. The subsequent cellular phenotype is modulated by a dynamic network of interactions among genes Perturbations in these interactions affect the overall manifestation of genetically driven diseases such as cancer. Responses to driving forces on the structure formation of these networks cause the development of new features and subsequently lead to the identification of unique patterns in the observational data These patterns can arise from non-trivial connections that go beyond classical pairwise interactions, leading to a higher-order c onstruction[16]. These constructions can be described by simplices of different dimensions and can be studied in the framework of Balance Theory and Topological Data Analysis (TDA). Network construction from real data and the result of balance theory analysis of the interaction network
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