Predicting Lung Cancer in Korean Never-Smokers With Polygenic Risk Scores.

Autor: Kim J; Department of Public Health Sciences, Seoul National University, Seoul, Korea., Park YS; Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Seoul National University Hospital, Seoul, Korea., Kim JH; Department of Integrative Bioscience & Biotechnology, Sejong University, Seoul, Korea., Hong YC; Department of Human Systems Medicine, Seoul National University College of Medicine, Seoul, Korea., Kim YC; Department of Internal Medicine, Lung and Esophageal Cancer Clinic, Chonnam National University Hwasun Hospital, Hwasun, Korea., Oh IJ; Department of Internal Medicine, Lung and Esophageal Cancer Clinic, Chonnam National University Hwasun Hospital, Hwasun, Korea., Jee SH; Department of Epidemiology and Health Promotion, Institute for Health Promotion, Graduate School of Public Health, Yonsei University, Seoul, Korea., Ahn MJ; Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea., Kim JW; Department of Laboratory Medicine and Genetics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea., Yim JJ; Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Seoul National University Hospital, Seoul, Korea., Won S; Department of Public Health Sciences, Seoul National University, Seoul, Korea.; RexSoft Corps, Seoul, Korea.; Institute of Health and Environment, Seoul National University, Seoul, Korea.; Interdisciplinary Program of Bioinformatics, Seoul National University, Seoul, Korea.
Jazyk: angličtina
Zdroj: Genetic epidemiology [Genet Epidemiol] 2025 Jan; Vol. 49 (1), pp. e22586. Date of Electronic Publication: 2024 Sep 23.
DOI: 10.1002/gepi.22586
Abstrakt: In the last few decades, genome-wide association studies (GWAS) with more than 10,000 subjects have identified several loci associated with lung cancer and these loci have been used to develop novel risk prediction tools for cancer. The present study aimed to establish a lung cancer prediction model for Korean never-smokers using polygenic risk scores (PRSs); PRSs were calculated using a pruning-thresholding-based approach based on 11 genome-wide significant single nucleotide polymorphisms (SNPs). Overall, the odds ratios tended to increase as PRSs were larger, with the odds ratio of the top 5% PRSs being 1.71 (95% confidence interval: 1.31-2.23) using the 40%-60% percentile group as the reference, and the area under the curve (AUC) of the prediction model being of 0.76 (95% confidence interval: 0.747-0.774). The receiver operating characteristic (ROC) curves of the prediction model with and without PRSs as covariates were compared using DeLong's test, and a significant difference was observed. Our results suggest that PRSs can be valuable tools for predicting the risk of lung cancer.
(© 2024 Wiley Periodicals LLC.)
Databáze: MEDLINE
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