Unveiling the Dynamics behind Glioblastoma Multiforme Single-Cell Data Heterogeneity

• Published in: International journal of molecular sciences Vol. 25; no. 9; p. 4894
• Main Authors: Junior, Marcos Guilherme Vieira, Côrtes, Adriano Maurício de Almeida, Carneiro, Flávia Raquel Gonçalves, Carels, Nicolas, Silva, Fabrício Alves Barbosa da
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.05.2024
• Subjects: Analysis; Brain Neoplasms - genetics; Brain Neoplasms - metabolism; Brain Neoplasms - pathology; Brain tumors; Cancer; cancer attractors; Cell cycle; Cluster Analysis; Computer software industry; Development and progression; epigenetic landscape; Epigenetics; Gene expression; Gene Expression Profiling - methods; Gene Expression Regulation, Neoplastic; Genes; Genetic Heterogeneity; Genomic Instability; Glioblastoma - genetics; Glioblastoma - metabolism; Glioblastoma - pathology; Glioblastoma Multiforme; Glioma; Health aspects; heterogeneity; Humans; Mutation; Parameter estimation; parameter sets estimation; Sequence Analysis, RNA - methods; Single-Cell Analysis - methods; single-cell RNA sequencing; Brazil; United States; single-cell RNA sequencing; gene regulatory network dynamics; Glioblastoma Multiforme; parameter sets estimation; cancer attractors; epigenetic landscape; heterogeneity
• DOI: 10.3390/ijms25094894
• ISSN: 1422-0067; 1661-6596

Glioblastoma Multiforme is a brain tumor distinguished by its aggressiveness. We suggested that this aggressiveness leads single-cell RNA-sequence data (scRNA-seq) to span a representative portion of the cancer attractors domain. This conjecture allowed us to interpret the scRNA-seq heterogeneity as reflecting a representative trajectory within the attractor's domain. We considered factors such as genomic instability to characterize the cancer dynamics through stochastic fixed points. The fixed points were derived from centroids obtained through various clustering methods to verify our method sensitivity. This methodological foundation is based upon sample and time average equivalence, assigning an interpretative value to the data cluster centroids and supporting parameters estimation. We used stochastic simulations to reproduce the dynamics, and our results showed an alignment between experimental and simulated dataset centroids. We also computed the Waddington landscape, which provided a visual framework for validating the centroids and standard deviations as characterizations of cancer attractors. Additionally, we examined the stability and transitions between attractors and revealed a potential interplay between subtypes. These transitions might be related to cancer recurrence and progression, connecting the molecular mechanisms of cancer heterogeneity with statistical properties of gene expression dynamics. Our work advances the modeling of gene expression dynamics and paves the way for personalized therapeutic interventions.