Investigation Into the Association Between Neurotransmitters, Immune Features, and Lung Adenocarcinoma: Identifying GABA‐Related Features Using Machine Learning Methods
Randomized trial reveals GABA-related features that predict outcomes in lung adenocarcinoma, suggesting potential for personalized treatment strategies.
Key Points
This research aims to explore the role of GABA-related features in lung adenocarcinoma prognosis and treatment.
Developed a machine learning framework with 10 algorithms to identify GABA-related genes (GABARgenes).
Conducted multiomics analyses including genomics and single-cell transcriptomics to define GABA-related features (GABARF).
Established a nomogram for clinical prognostic tool based on GABARF.
38 of 124 GABARgenes showed significant correlation with overall survival (OS).
GABARF demonstrated high performance predicting prognosis, serving as an independent prognostic factor for OS.
Risk stratification based on GABARF highlighted varying responses to immunotherapy and conventional treatments.