SynthesisBMC cancer2022
Polygenic risk prediction models for colorectal cancer: a systematic review.
Synthesis in BMC cancer, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- AGA Clinical Practice Update on Risk Stratification for Colorectal Cancer Screening and Post-Polypectomy Surveillance: Expert Review.Gastroenterology · 2023Guideline
- Validation of a Colorectal Cancer Risk Prediction Model in US Black Women.Cancer prevention research (Philadelphia, Pa.) · 2026Article
- TGF-β Pathway-Based Polygenic Risk Score Modifies the Association between Red Meat Intake and Colorectal Cancer Risk: Application of a Novel Pathway-Based PRS Method.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2026Article
- Red meat intake interacts with a TGF-β-pathway-based polygenic risk score to impact colorectal cancer risk: Application of a novel approach for polygenic risk score construction.medRxiv : the preprint server for health sciences · 2025Article
- Stratified/risk-based screening for colorectal cancer in the UK: an overview.Colorectal cancer · 2025Review
- Article
- Colorectal cancer risk stratification using a polygenic risk score in symptomatic primary care patients-a UK Biobank retrospective cohort study.European journal of human genetics : EJHG · 2024Article
- Enhancing infectious disease prediction model selection with multi-objective optimization: an empirical study.PeerJ. Computer science · 2024Article
- Lifestyle predictors of colorectal cancer in European populations: a systematic review.BMJ nutrition, prevention & health · 2024Article
- Genetic Variants in the Mitochondrial Thymidylate Biosynthesis Pathway Increase Colorectal Cancer Risk.Current oncology (Toronto, Ont.) · 2023Article
- Symptoms and early-onset colorectal cancer: red flags are common flags!Journal of the National Cancer Institute · 2023Article
- Development and Validation of Risk Prediction Models for Colorectal Cancer in Patients with Symptoms.Journal of personalized medicine · 2023Article
- Detection of germline variants with pathogenic potential in 48 patients with familial colorectal cancer by using whole exome sequencing.BMC medical genomics · 2023Article
- Colorectal Cancer Risk Assessment and Precision Approaches to Screening: Brave New World or Worlds Apart?Gastroenterology · 2023Review
- Evaluation of polygenic risk score for risk prediction of gastric cancer.World journal of gastrointestinal oncology · 2023Review
- Article
- Editorial: Clinically prediction models for gastrointestinal cancer diagnosis and prognosis in the era of precision oncology.Frontiers in oncology · 2023Article
- Using the Prediction Model Risk of Bias Assessment Tool (PROBAST) to Evaluate Melanoma Prediction Studies.Cancers · 2022Article
- Polygenic risk, population structure and ongoing difficulties with race in human genetics.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2022Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundRisk prediction models incorporating single nucleotide polymorphisms (SNPs) could lead to individualized prevention of colorectal cancer (CRC). However, the added value of incorporating SNPs into models with only traditional risk factors is still not clear. Hence, our primary aim was to summarize literature on risk prediction models including genetic variants for CRC, while our secondary aim was to evaluate the improvement of discriminatory accuracy when adding SNPs to a prediction model with only traditional risk factors.
methodsWe conducted a systematic review on prediction models incorporating multiple SNPs for CRC risk prediction. We tested whether a significant trend in the increase of Area Under Curve (AUC) according to the number of SNPs could be observed, and estimated the correlation between AUC improvement and number of SNPs. We estimated pooled AUC improvement for SNP-enhanced models compared with non-SNP-enhanced models using random effects meta-analysis, and conducted meta-regression to investigate the association of specific factors with AUC improvement.
resultsWe included 33 studies, 78.79% using genetic risk scores to combine genetic data. We found no significant trend in AUC improvement according to the number of SNPs (p for trend = 0.774), and no correlation between the number of SNPs and AUC improvement (p = 0.695). Pooled AUC improvement was 0.040 (95% CI: 0.035, 0.045), and the number of cases in the study and the AUC of the starting model were inversely associated with AUC improvement obtained when adding SNPs to a prediction model. In addition, models constructed in Asian individuals achieved better AUC improvement with the incorporation of SNPs compared with those developed among individuals of European ancestry.
conclusionsThough not conclusive, our results provide insights on factors influencing discriminatory accuracy of SNP-enhanced models. Genetic variants might be useful to inform stratified CRC screening in the future, but further research is needed.
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