ArticleScientific reports2021
Topological analysis of interaction patterns in cancer-specific gene regulatory network: persistent homology approach.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- A Review of Topological Data Analysis and Topological Deep Learning in Molecular Sciences.Journal of chemical information and modeling · 2025Review
- Cancer and Aging Biomarkers: Classification, Early Detection Technologies and Emerging Research Trends.Biosensors · 2025Review
- Identifying key genes in cancer networks using persistent homology.Scientific reports · 2025Article
- Relational Persistent Homology for Multispecies Data with Application to the Tumor Microenvironment.Bulletin of mathematical biology · 2024Article
- Dissecting glial scar formation by spatial point pattern and topological data analysis.Scientific reports · 2024Article
- A universal null-distribution for topological data analysis.Scientific reports · 2023Article
- A novel approach to topological network analysis for the identification of metrics and signatures in non-small cell lung cancer.Scientific reports · 2023Article
- Persistent homology analysis of type 2 diabetes genome-wide association studies in protein-protein interaction networks.Frontiers in genetics · 2023Article
- Identification of Two Exosomal miRNAs in Circulating Blood of Cancer Patients by Using Integrative Transcriptome and Network Analysis.Non-coding RNA · 2022Article
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Authors and funding
5 authors.
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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.
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