Evidence map›Paper›PMID 41851747›Full record

ArticleGenome medicine2026

Optimizing colorectal cancer screening through polygenic risk score-based risk stratification: evidence from a population-based cohort and screening trial.

Hongda Chen, Jiahui Si, Chenyu Luo, Jianbo Tian, Junyi Xin, Canqing Yu, Dianjianyi Sun, Pei Pei, Ling Yang, Iona Y Millwood and 11 more

Abstract read
In one paragraph

Article in Genome medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

21 authors.

Hongda Chen *Center for Prevention and Early Intervention, National Infrastructures for Translational Medicine, Institute of Clinical Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China.
Jiahui Si *CAS Key Laboratory of Pathogenic Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences, Beijing, China.
Chenyu Luo *Center for Prevention and Early Intervention, National Infrastructures for Translational Medicine, Institute of Clinical Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China.
Jianbo Tian *Department of Epidemiology and Biostatistics, School of Public Health; State Key Laboratory of Metabolism and Regulation in Complex Organisms, TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, China.
Junyi Xin *Department of Bioinformatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China.
Canqing YuDepartment of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, China.
Dianjianyi SunDepartment of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, China.
Pei PeiKey Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China.
Ling YangMedical Research Council Population Health Research Unit at the University of Oxford, Oxford, UK.
Iona Y MillwoodMedical Research Council Population Health Research Unit at the University of Oxford, Oxford, UK.
Robin G WaltersMedical Research Council Population Health Research Unit at the University of Oxford, Oxford, UK.
Yiping ChenMedical Research Council Population Health Research Unit at the University of Oxford, Oxford, UK.
Huaidong DuMedical Research Council Population Health Research Unit at the University of Oxford, Oxford, UK.
Zhengming ChenClinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK.
Liming LiDepartment of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, China.
Masaru KoidoLaboratory of Complex Trait Genomics, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan.
Yoichiro KamataniLaboratory of Complex Trait Genomics, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan.
Meilin WangDepartment of Environmental Genomics, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, School of Public Health, Nanjing Medical University, Nanjing, China. mwang@njmu.edu.cn.
Xiaoping MiaoDepartment of Epidemiology and Biostatistics, School of Public Health; State Key Laboratory of Metabolism and Regulation in Complex Organisms, TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, China. xpmiao@whu.edu.cn.
Jun LvDepartment of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, China. lvjun@bjmu.edu.cn.
Min DaiDepartment of Cancer Epidemiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China. daimin2002@hotmail.com.

Funding

Beijing Research Ward Excellence Program BRWEP2024W034010101Chinese Academy of Medical Science Innovation Fund for Medical Science 2022-I2M-1-003Chinese Ministry of Science and Technology 2011BAI09B01Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education 2024101National High Level Hospital Clinical Research Funding 2025-LYZX-C-B03National Key R&D Program of China 2016YFC0900500Noncommunicable Chronic Diseases-National Science and Technology Major Project 2023ZD0510100PUMCH Talent Development and Support Program, Category B UGG06641Science and Technology Planning Project of Tibet Autonomous Region XZ202501JD0021Wellcome grants to Oxford University 212946/Z/18/Z, 202922/Z/16/Z, 104085/Z/14/Z, 088158/Z/09/Z
6 · The paper itself

Abstract

backgroundAccurate risk stratification of colorectal cancer (CRC) is essential for precise screening. Polygenic risk scores (PRS) hold promise for improving predictive efficacy in CRC. However, the real-world applicability of a risk-adapted CRC screening strategy based on the PRS remains underexplored. Therefore, we aimed to evaluate the optimized PRS in a large prospective cohort in China and assess its utility for risk-adapted CRC screening.

methodsWe evaluated multiple PRS construction strategies using East Asian genome-wide association study data and well-established PRSs to select an optimal score, which was then assessed in 100,639 eligible participants from the China Kadoorie Biobank. The risk-adapted screening strategy assigns high-risk individuals to colonoscopy and low-risk individuals to fecal immunochemical testing (FIT), with FIT-positive cases referred for colonoscopy. We assessed the screening performance of the PRS, Asia–Pacific Colorectal Screening score, and their combination-based risk-adapted screening strategies against a standard FIT-based strategy among 2,821 participants in the TARGET-C CRC screening trial.

resultsThe combined PRS (i.e., PRS121) demonstrated the best predictive performance (C-index = 0.602) for CRC. Individuals in the highest PRS quintile (top 20%) exhibited a 2.69-fold increased CRC risk compared with those in the lowest quintile. The high PRS and unfavorable lifestyle group conferred the highest risk (hazard ratio = 3.32, 95% confidence interval: 1.80–6.11). In the TARGET-C trial, the PRS121 effectively distinguished patients with advanced neoplasia from controls (top 20%, odds ratio = 3.66). The PRS-based risk-adapted screening strategy improved AN detection compared with FIT-only screening, with higher sensitivity (69.4% vs. 54.1%, P = 7.6 × 10–4) and detection rate (16.7% vs. 13.1%, P = 0.024). Notably, PRS-based screening identified 21.1% of AN cases that were missed by FIT, and integrated risk stratification using PRS and APCS increased this proportion to 37.9%, demonstrating substantial improvement in detecting FIT-negative lesions.

conclusionsPRS enables effective risk stratification for colorectal cancer and improves detection of advanced neoplasia by identifying high-risk individuals missed by FIT, supporting its utility in precision risk-adapted screening.

trial registrationChinese Clinical Trial Registry: ChiCTR1800015506. Registered on 4 April 2018.

Indexed as

Colorectal NeoplasmsEarly Detection of CancerAgedChinaColonoscopyFemaleGenetic Predisposition to DiseaseGenetic Risk ScoreGenome-Wide Association StudyHumansMaleMiddle AgedRisk AssessmentRisk FactorsColorectal cancerPolygenic risk scorePrecise screeningRisk prediction

Identifiers

PMID41851747
PMCPMC13123215

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.