ArticleBMC cancer2022
Development and validation of polygenic risk scores for prediction of breast cancer and breast cancer subtypes in Chinese women.
Article in BMC cancer, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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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.
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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.
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Who cites it
7 citing papers in PubMed, 13 citations in OpenAlex.
- Genetic and lifestyle factors for breast cancer risk assessment in Southeast China.Cancer medicine · 2023Trial
- Epidemiology, early detection, and management of breast cancer in China: A comprehensive review.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2025Article
- A survey on deep learning for polygenic risk scores.Briefings in bioinformatics · 2025Review
- Decoding core genes and intercellular communication in osteosarcoma: bioinformatic investigation and immune cell profiling for diagnostic and therapeutic insights.Discover oncology · 2024Article
- Benefits and harms of polygenic risk scores in organised cancer screening programmes: a cost-effectiveness analysis.The Lancet regional health. Western Pacific · 2024Article
- Deep learning captures the effect of epistasis in multifactorial diseases.Frontiers in medicine · 2024Article
- Review
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
backgroundStudies investigating breast cancer polygenic risk score (PRS) in Chinese women are scarce. The objectives of this study were to develop and validate PRSs that could be used to stratify risk for overall and subtype-specific breast cancer in Chinese women, and to evaluate the performance of a newly proposed Artificial Neural Network (ANN) based approach for PRS construction.
methodsThe PRSs were constructed using the dataset from a genome-wide association study (GWAS) and validated in an independent case-control study. Three approaches, including repeated logistic regression (RLR), logistic ridge regression (LRR) and ANN based approach, were used to build the PRSs for overall and subtype-specific breast cancer based on 24 selected single nucleotide polymorphisms (SNPs). Predictive performance and calibration of the PRSs were evaluated unadjusted and adjusted for Gail-2 model 5-year risk or classical breast cancer risk factors.
resultsThe primary PRS
conclusionsThe 24 SNPs based PRSs can provide additional risk information to help breast cancer risk stratification in the general population of China. The newly proposed ANN approach for PRS construction has potential to replace the traditional approaches, but more studies are needed to validate and investigate its performance.
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Registered trials
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