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血浆蛋白质组学和多基因谱分析可改善结直肠癌的风险分层和个体化筛查。

Plasma proteomic and polygenic profiling improve risk stratification and personalized screening for colorectal cancer.

机构信息

Department of Colorectal Surgery and Oncology (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education), The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.

Department of Big Data in Health Science, School of Public Health and The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.

出版信息

Nat Commun. 2024 Oct 15;15(1):8873. doi: 10.1038/s41467-024-52894-2.

Abstract

This study aims to identify colorectal cancer (CRC)-related proteomic profiles and develop a prediction model for CRC onset by integrating proteomic profiles with genetic and non-genetic factors (QCancer-15) to improve the risk stratification and estimate of personalized initial screening age. Here, using a two-stage strategy, we prioritize 15 protein biomarkers as predictors to construct a protein risk score (ProS). The risk prediction model integrating proteomic profiles with polygenic risk score (PRS) and QCancer-15 risk score (QCancer-S) shows improved performance (C-statistic: 0.79 vs. 0.71, P = 4.94E-03 in training cohort; 0.75 vs 0.69, P = 5.49E-04 in validation cohort) and net benefit than QCancer-S alone. The combined model markedly stratifies the risk of CRC onset. Participants with high ProS, PRS, or combined risk score are proposed to start screening at age 46, 41, or before 40 years old. In this work, the integration of blood proteomics with PRS and QCancer-15 demonstrates improved performance for risk stratification and clinical implication for the derivation of risk-adapted starting ages of CRC screening, which may contribute to the decision-making process for CRC screening.

摘要

本研究旨在通过整合蛋白质组学特征与遗传和非遗传因素(QCancer-15)来确定与结直肠癌(CRC)相关的蛋白质组学特征,并建立 CRC 发病预测模型,以改善风险分层和估计个性化初始筛查年龄。在这里,我们使用两阶段策略,优先选择 15 种蛋白质生物标志物作为预测因子来构建蛋白质风险评分(ProS)。整合蛋白质组学特征与多基因风险评分(PRS)和 QCancer-15 风险评分(QCancer-S)的风险预测模型显示出了更好的性能(训练队列中的 C 统计量:0.79 与 0.71,P = 4.94E-03;验证队列中的 0.75 与 0.69,P = 5.49E-04)和净收益优于 QCancer-S 单独使用。联合模型明显划分了 CRC 发病风险。建议高 ProS、PRS 或联合风险评分的参与者在 46 岁、41 岁或 40 岁之前开始筛查。在这项工作中,血液蛋白质组学与 PRS 和 QCancer-15 的整合显示出了更好的风险分层性能,并为 CRC 筛查的风险适应起始年龄的推导提供了临床意义,这可能有助于 CRC 筛查的决策过程。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b460/11473805/47c8341cb7a2/41467_2024_52894_Fig1_HTML.jpg

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