Integrated transcriptomic, machine-learning and structural analyses prioritize NR3C1 for experimental follow-up in colorectal cancer
This study integrates transcriptomic, machine learning, and structural analyses to identify and prioritize the glucocorticoid receptor NR3C1 as the most stable and promising candidate for experimental follow-up in resveratrol-mediated colorectal cancer research, while explicitly noting that these findings are hypothesis-generating rather than proof of direct binding.