Related Experiment Video
Updated: Oct 25, 2026
Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
Published on: May 17, 2019
Sleep patterns and breast cancer risk: a prospective cohort study integrating multi-omics and machine learning
Xiwen Chang1, Shuting Zuo1, Zhenyu Wang1
1Department of Breast Surgery, The Second Hospital of Jilin University, Changchun, Jilin Province, China.
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.