Multiple polygenic score approach in colorectal cancer risk prediction
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68378041%3A_____%2F25%3A00641282" target="_blank" >RIV/68378041:_____/25:00641282 - isvavai.cz</a>
Alternative codes found
RIV/00216208:11110/25:10505055
Result on the web
<a href="https://www.nature.com/articles/s41598-025-21956-w" target="_blank" >https://www.nature.com/articles/s41598-025-21956-w</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1038/s41598-025-21956-w" target="_blank" >10.1038/s41598-025-21956-w</a>
Alternative languages
Result language
angličtina
Original language name
Multiple polygenic score approach in colorectal cancer risk prediction
Original language description
Recent studies have demonstrated that for various diseases, incorporating polygenic risk scores (PRSs) for other traits and diseases into the PRS-based risk prediction model may improve predictive performance - known as Multiple Polygenic Score (MPS) approach. We aimed to examine whether the MPS approach improves colorectal cancer (CRC) risk prediction. We included 2,187 non-CRC PRSs from the polygenic Score (PGS) Catalog and used machine learning (ML) models to select the most predictive non-CRC PRSs, utilizing individual-level data from 31,257 CRC cases and 33,408 controls. An independent dataset from the Genetic Epidemiology Research in Adult Health and Aging (GERA) cohort (4,852 cases and 67,939 controls) was randomly split into subsets for model estimation and validation. The model combined MPS with two existing CRC-PRSs based on known loci and genome-wide genotyping. We then assessed model performance by calculating the area under the receiver operating curve (AUC) in the validation set and performed 1,000 bootstrapped iterations to evaluate AUC improvements. The ML model selected 337 non-CRC PRSs predictive of CRC risk. Adding MPS to the CRC-PRSs significantly improved AUC by 0.017 (95% CI: 0.011-0.022, p < 0.0001) when combined with known-loci CRC-PRS, 0.005 (95% CI: 0.002-0.007, p = 0.0005) with genome-wide CRC-PRS, and 0.004 (95% CI: 0.002-0.006, p = 0.0005) with both the known loci and genome-wide CRC-PRSs. These findings demonstrate MPS's potential to refine CRC risk prediction models and highlight opportunities for further advancements in risk prediction.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
30204 - Oncology
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Scientific Reports
ISSN
2045-2322
e-ISSN
2045-2322
Volume of the periodical
15
Issue of the periodical within the volume
38006
Country of publishing house
GB - UNITED KINGDOM
Number of pages
14
Pages from-to
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UT code for WoS article
001606739500005
EID of the result in the Scopus database
2-s2.0-105020397713