Bayesian Modeling of Gene Regulatory Networks in Colorectal Cancer Organoids.

Colorectal cancer remains a pressing challenge in global health, necessitating advanced biological models and analytical methodologies. Tumor organoids (tumoroids) have emerged as a compelling platform for cancer research, owing to their capacity to replicate the genetic and structural complexity of human tissues. However, extracting meaningful gene regulatory insights from bulk ribonucleic acid (RNA) sequencing data derived from tumoroids remains nontrivial due to cellular heterogeneity and temporal variation. We propose, for the first time, a comprehensive Bayesian framework to model gene expression dynamics throughout the developmental trajectory of colorectal tumoroids. We introduce a nonparametric Dirichlet process mixture model (DPMM) to cluster genes based on temporal expression patterns and a sparse regression scheme, incorporating Horseshoe+ priors, to construct gene regulatory networks (GRNs) among identified clusters. The proposed approach demonstrates robust performance in capturing high-dimensional relationships, enabling elucidation of key regulatory mechanisms in tumor progression. Our results offer valuable insights for personalized treatment and underscore the utility of Bayesian methods in complex biological systems.