BMC cancer · 2026
Sleep patterns and breast cancer risk: a prospective cohort study integrating multi-omics and machine learning.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Chang Xiwen, Zuo Shuting, Wang Zhenyu, Wang Zhen, Gao Yafang, Zhang Yan
Аннотация
BACKGROUND: Sleep is a potentially modifiable behavioural factor that may influence breast cancer risk. This study examined the association between sleep patterns and incident breast cancer in relation to genetic susceptibility and plasma proteomic profiles.
METHODS: This prospective cohort study included 159,452 UK Biobank women free of breast cancer at baseline. Sleep patterns were constructed from five sleep characteristics. Associations with incident breast cancer were assessed using Cox proportional hazards models and time-varying Cox proportional hazards models adjusted for demographic, socioeconomic, lifestyle, breast cancer-related, and clinical covariates. Kaplan-Meier curves, restricted cubic splines, and PRS-based joint and stratified analyses were performed. In the proteomic subcohort, differential expression, enrichment, and machine-learning-based feature prioritisation analyses were used to identify overlapping proteomic signatures related to sleep pattern and breast cancer status.
RESULTS: During follow-up, 7,291 incident breast cancer cases were identified. Compared with a healthy sleep pattern, an unhealthy sleep pattern was associated with a higher risk of breast cancer in the fully adjusted model (HR = 1.10, 95% CI = 1.05-1.15). This association was consistent in time-varying Cox proportional hazards models (HR = 1.34, 95% CI = 1.02-1.77). Joint analyses showed that participants with both high PRS and an unhealthy sleep pattern had the highest breast cancer risk. Proteomic analyses identified overlapping protein signatures related to both sleep pattern and breast cancer status, with enrichment mainly in immune regulation, inflammatory signalling, vascular function, and extracellular matrix-related biological processes. Machine-learning-based feature prioritisation identified CRISP3, PADI2, PLA2G2A, SUSD4, TNFRSF6B, and UPK3A as key candidate proteins.
CONCLUSIONS: Unhealthy sleep patterns were associated with increased incident breast cancer risk. Integrated genetic and proteomic analyses suggest that immune-inflammatory, vascular, and extracellular matrix-related processes may provide biological context for this association.
METHODS: This prospective cohort study included 159,452 UK Biobank women free of breast cancer at baseline. Sleep patterns were constructed from five sleep characteristics. Associations with incident breast cancer were assessed using Cox proportional hazards models and time-varying Cox proportional hazards models adjusted for demographic, socioeconomic, lifestyle, breast cancer-related, and clinical covariates. Kaplan-Meier curves, restricted cubic splines, and PRS-based joint and stratified analyses were performed. In the proteomic subcohort, differential expression, enrichment, and machine-learning-based feature prioritisation analyses were used to identify overlapping proteomic signatures related to sleep pattern and breast cancer status.
RESULTS: During follow-up, 7,291 incident breast cancer cases were identified. Compared with a healthy sleep pattern, an unhealthy sleep pattern was associated with a higher risk of breast cancer in the fully adjusted model (HR = 1.10, 95% CI = 1.05-1.15). This association was consistent in time-varying Cox proportional hazards models (HR = 1.34, 95% CI = 1.02-1.77). Joint analyses showed that participants with both high PRS and an unhealthy sleep pattern had the highest breast cancer risk. Proteomic analyses identified overlapping protein signatures related to both sleep pattern and breast cancer status, with enrichment mainly in immune regulation, inflammatory signalling, vascular function, and extracellular matrix-related biological processes. Machine-learning-based feature prioritisation identified CRISP3, PADI2, PLA2G2A, SUSD4, TNFRSF6B, and UPK3A as key candidate proteins.
CONCLUSIONS: Unhealthy sleep patterns were associated with increased incident breast cancer risk. Integrated genetic and proteomic analyses suggest that immune-inflammatory, vascular, and extracellular matrix-related processes may provide biological context for this association.