DNA walk of specific fused oncogenes exhibit distinct fractal geometric characteristics in nucleotide patterns

Abstract

Background/ObjectivesThe complex system of cancer has led to an emphasis on understanding the more general causal relationship within the disease. In this context, concepts of symmetry and symmetry-breaking in distinct biological cell features or components have been examined as an approach to cancer investigation. However, there can be possible limitations in directly interpreting the symmetry-based approach from a physical viewpoint due to the lack of understanding of physical laws governing symmetry in complex systems like cancer. MethodsFractal geometry and DNA walk representation were employed to investigate the geometric features i.e., self-similarity and heterogeneity in DNA nucleotide coding sequences of wild-type and mutated oncogenes, tumour-suppressor, and other unclassified genes. The mutation-facilitated self-similar and heterogenous features were quantified by the fractal dimension and lacunarity coefficient measures, respectively. Additionally, the geometrical orderedness and disorderedness in the analyzed sequences were interpreted from the combination of the fractal measures. ResultsThe findings showed distinct fractal geometric features in the case of fusion mutations. It also highlights the possible interpretation of the observed fractal features as geometric analogues concerning explicit observations corresponding to specific cancer types. In addition, the two-dimensional multi-fractal analysis highlighted the presence of a single exponent in the scaling of mutation-mediated gene sequence self-similarity/complexity and heterogeneity. ConclusionsThe approach identified mutation-induced geometric features in gene sequences, demonstrating the potential of DNA walks and fractal analysis in translational research regarding cancer. The findings suggest that investigating fractal parameters can capture unique geometric features in nucleotide sequences, contributing to the understanding of cancers molecular complexity.