2019/10/04 by Pramod B. Shinde, Loïc Marrec, Shinde, Pramod +12
Biochemistry, Genetics and Molecular Biology · Computer Science · Physics and Astronomy · #Bioinformatics and Genomic Networks #Complex Network Analysis Techniques #Computational Drug Discovery Methods #FOS: Biological sciences #Molecular Networks (q-bio.MN)
paper · pdf · doi:10.48550/arxiv.1910.01801
openalex publication_date 2019/10/04 · openalex created_date 2022/07/28 · openalex updated_date 2026/07/28
One of the most challenging problems in biomedicine and genomics is the\nidentification of disease biomarkers. In this study, proteomics data from seven\nmajor cancers were used to construct two weighted protein-protein interaction\n(PPI) networks i.e., one for the normal and another for the cancer conditions.\nWe developed rigorous, yet mathematically simple, methodology based on the\ndegeneracy at -1 eigenvalues to identify structural symmetry or motif\nstructures in network. Utilising eigenvectors corresponding to degenerate\neigenvalues in the weighted adjacency matrix, we identified structural symmetry\nin underlying weighted PPI networks constructed using seven cancer data.\nFunctional assessment of proteins forming these structural symmetry exhibited\nthe property of cancer hallmarks. Survival analysis refined further this\nprotein list proposing BMI, MAPK11, DDIT4, CDKN2A, and FYN as putative\nmulti-cancer biomarkers. The combined framework of networks and spectral graph\ntheory developed here can be applied to identify symmetrical patterns in other\ndisease networks to predict proteins as potential disease biomarkers.\n