Improving neuroblastoma risk prediction through a polygenic risk score derived from genome-wide association study-identified loci

作者：Wenli Zhang, Jinhong Zhu, Mengzhen Zhang, Jiaming Chang, Jiabin Liu, Liping Chen, Xinxin Zhang, Haiyan Wu, Chunlei Zhou, Jing He · 发表于：Chinese Journal of Cancer Research · 年份：2025 · DOI：10.21147/j.issn.1000-9604.2025.01.01 · 被引用次数：8 · 研究领域：Neuroblastoma Research and Treatments、Genomics and Rare Diseases、Cancer, Hypoxia, and Metabolism

Objective: Neuroblastoma is the most common extracranial solid tumor in children and has complex genetic underpinnings. Previous genome-wide association studies (GWASs) have identified many loci associated with neuroblastoma susceptibility; however, their application in risk prediction for Chinese children has not been systematically explored. This study seeks to enhance neuroblastoma risk prediction by validating these loci and evaluating their performance in polygenic risk models. Methods: We validated 35 GWAS-identified neuroblastoma susceptibility loci in a cohort of Chinese children, consisting of 402 neuroblastoma patients and 473 healthy controls. Genotyping these polymorphisms was conducted via the TaqMan method. Univariable and multivariable logistic regression analyses revealed the genetic loci significantly associated with neuroblastoma risk. We constructed polygenic risk models by combining these loci and assessed their predictive performance via area under the curve (AUC) analysis. We also established a polygenic risk scoring (PRS) model for risk prediction by adopting the PLINK method. Results: were significantly associated with neuroblastoma risk. Compared with single-gene model, the 8-gene model (AUC=0.72) and 13-gene model (AUC=0.73) demonstrated superior predictive performance. Additionally, a PRS incorporating six significant loci achieved an AUC of 0.66, effectively stratifying individuals into distinct risk categories regarding neuroblastoma susceptibilit...