BACKGROUND/OBJECTIVES: Circadian rhythm disruption is increasingly implicated in tumor progression and therapy resistance. However, its prognostic value and impact on the tumor immune microenvironment in lung adenocarcinoma (LUAD) remain unclear. This study aimed to develop a robust circadian rhythm-based gene signature to improve risk stratification and inform personalized therapeutic strategies in LUAD. METHODS: We integrated multi-omics data from over 900 LUAD patients across TCGA and GEO databases. A circadian rhythm-related gene prognostic signature (CRGPS) was constructed from candidate genes using ten machine learning algorithms and validated externally. The tumor immune microenvironment, mutation landscape, and therapy response were analyzed using bioinformatics algorithms. Single-cell RNA sequencing data were utilized to explore gene expression at cellular resolution. RESULTS: A 10-gene CRGPS was developed, which effectively stratified patients into high- and low-risk groups with significantly divergent overall survival in both training and validation cohorts. Unsupervised clustering based on these genes revealed two molecular subtypes (C1 and C2) with distinct characteristics. The two subgroups exhibited significant differences in terms of the tumor immune microenvironment, clinical prognosis, and therapeutic sensitivity. Single-cell analysis localized key signature genes to endothelial and epithelial cells and revealed enhanced endothelial-endothelial communication. CONCLUSIONS: The CRGPS serves as a robust prognostic framework for risk stratification and provides hypothesis-generating insights into therapeutic vulnerabilities based on in silico predictions of immune profiles and drug sensitivity, pending prospective validation.
Lu et al. (Mon,) studied this question.