Journal article
Combining machine learning with Cox models to identify predictors for incident post-menopausal breast cancer in the UK Biobank
- Abstract:
-
We aimed to identify potential novel predictors for breast cancer among post-menopausal women, with pre-specified interest in the role of polygenic risk scores (PRS) for risk prediction. We utilised an analysis pipeline where machine learning was used for feature selection, prior to risk prediction by classical statistical models. An “extreme gradient boosting” (XGBoost) machine with Shapley feature-importance measures were used for feature selection among ≈ 1.7 k features in 104,313 post-menopausal women from the UK Biobank. We constructed and compared the “augmented” Cox model (incorporating the two PRS, known and novel predictors) with a “baseline” Cox model (incorporating the two PRS and known predictors) for risk prediction. Both of the two PRS were significant in the augmented Cox model (p<0.001 ). XGBoost identified 10 novel features, among which five showed significant associations with post-menopausal breast cancer: plasma urea (HR = 0.95, 95% CI 0.92–0.98, p<0.001 ), plasma phosphate (HR = 0.68, 95% CI 0.53–0.88, p=0.003 ), basal metabolic rate (HR = 1.17, 95% CI 1.11–1.24, p<0.001 ), red blood cell count (HR = 1.21, 95% CI 1.08–1.35, p<0.001 ), and creatinine in urine (HR = 1.05, 95% CI 1.01–1.09, p=0.006 ). Risk discrimination was maintained in the augmented Cox model, yielding C-index 0.673 vs 0.667 (baseline Cox model) with the training data and 0.665 vs 0.664 with the test data. We identified blood/urine biomarkers as potential novel predictors for post-menopausal breast cancer. Our findings provide new insights to breast cancer risk. Future research should validate novel predictors, investigate using multiple PRS and more precise anthropometry measures for better breast cancer risk prediction.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
Actions
Access Document
- Files:
-
-
(Preview, Version of record, pdf, 1.9MB, Terms of use)
-
- Publisher copy:
- 10.1038/s41598-023-36214-0
Authors
- Publisher:
- Springer Nature
- Journal:
- Scientific Reports More from this journal
- Volume:
- 13
- Article number:
- 9221
- Publication date:
- 2023-06-07
- Acceptance date:
- 2023-05-31
- DOI:
- EISSN:
-
2045-2322
- Language:
-
English
- Keywords:
- Pubs id:
-
1350200
- Local pid:
-
pubs:1350200
- Deposit date:
-
2023-06-02
Terms of use
- Copyright holder:
- Liu et al.
- Copyright date:
- 2023
- Rights statement:
- © The Author(s) 2023. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
- Licence:
- CC Attribution (CC BY)
If you are the owner of this record, you can report an update to it here: Report update to this record