Cancer Genetics and Epigenetics, 2025, Vol.13, No.1, 1-10 http://medscipublisher.com/index.php/cge 4 Which SNPS are selected and how to assign weights to them have a significant impact on the accuracy of PRS. For instance, the PRS calculated based on 140 known CRC-related gene variations has a less favorable predictive effect compared to the genome-wide PRS calculated based on nearly 1.2 million gene variations. This indicates that in order to accurately predict risks, more SNPS need to be taken into account and good modeling methods should also be used (Thomas et al., 2020; Duenas et al., 2023). 4.2 The predictive value and clinical application of PRS in CRC screening PRS has a certain role in predicting the risk of CRC, but it is not particularly strong. According to different models and populations, its prediction accuracy (measured by the area under the curve AUC) is between 0.61 and 0.71 (Thomas et al., 2020; Duenas et al., 2023; Kim and Chatterjee, 2025). People with a high PRS score have a 2 to 3 times higher risk of developing CRC than the general population. Even if no one in the family has had CRC, high-risk groups who need to have examinations earlier or more can still be identified through PRS (Figure 1) (Frampton et al., 2016; Thomas et al., 2020; Tamlander et al., 2024; Kim and Chatterjee, 2025). Figure 1 Description of three approaches to derive polygenic risk scores (PRS) for colorectal cancer (Adopted from Thomas et al., 2020) If PRS is combined with other traditional risk factors or protein test results, the population can be better classified according to the risk level, and it can also help doctors formulate personalized screening plans, such as deciding when to start the examination or how often to perform colonoscopy (Guo et al., 2020; Guo et al., 2022; Tamlander et al., 2024; Kim and Chatterjee, 2025). Formulating personalized screening plans with PRS can reduce unnecessary examinations for low-risk populations and help doctors detect the conditions of high-risk populations earlier (Frampton et al., 2016; Tamlander et al., 2024; Kim and Chatterjee, 2025). 4.3 Current challenges: population heterogeneity, model validation and generalability One major problem encountered in the practical application of PRS is that there are significant genetic differences among different groups of people. The PRS model developed in a certain population often has poor prediction effects when applied to other populations. This requires the development and validation of specialized models for different groups of people (Thomas et al., 2020; Duenas et al., 2023). For instance, a PRS model based on European population data may not be able to accurately predict the disease risk of East Asians or other non-European populations. Therefore, when developing and validating the model, it is essential to use data with a genetic background similar to that of the target population (Duenas et al., 2023; Zhao et al., 2024). In addition, due to differences in research design, selection criteria, and whether non-genetic risk factors were considered, the reliability and applicability of the PRS model have also become issues (Duenas et al., 2023; Jiang et al., 2024). Further research is needed to improve the PRS model, verify its accuracy in different populations, and combine PRS with factors such as clinical conditions and living habits. Only in this way can it play a better role in the actual CRC screening program (Duenas et al., 2023; Tamlander et al., 2024; Jiang et al., 2024; Kim and Chatterjee, 2025).
RkJQdWJsaXNoZXIy MjQ4ODYzNA==