实用老年医学

实用老年医学 ›› 2025, Vol. 39 ›› Issue (7): 686-692.doi: 10.3969/j.issn.1003-9198.2025.07.008

• 临床研究 • 上一篇    下一篇

基于蛋白质组学的生物学年龄与肿瘤的发病死亡和预后的关联研究

王冠融, 鲍云颖, 张露露, 陆胜男, 沈思鹏   

  1. 210036 江苏省南京市,江苏省卫生健康发展研究中心,国家卫生健康委避孕药具警戒与生育力监测重点实验室(王冠融);
    210009 江苏省南京市,江苏省疾病预防控制中心(王冠融);
    211166 江苏省南京市,南京医科大学公共卫生学院(鲍云颖,张露露,陆胜男);
    213000 江苏省常州市,南京医科大学常州公共卫生高等研究院(沈思鹏)
  • 收稿日期:2025-02-07 出版日期:2025-07-20 发布日期:2025-07-22
  • 通讯作者: 沈思鹏,Email:sshen@njmu.edu.cn
  • 基金资助:
    江苏省卫生健康发展研究中心开放课题(JSHD2021046);南京医科大学常州公共卫生高等研究院开放课题基金资助(CPHM202301);国家自然科学基金面上项目(82373685)

Association between proteomics-based biological age and the incidence, mortality and prognosis of cancers

WANG Guanrong, BAO Yunying, ZHANG Lulu, LU Shengnan, SHEN Sipeng   

  1. NHC Key Laboratory of Contraceptives Vigilance and Fertility Surveillance, Jiangsu Health Development Research Center, Nanjing 210036, China (WANG Guanrong);
    Jiangsu Provincial Center for Disease Control and Prevention, Nanjing 210009, China (WANG Guanrong);
    School of Public Health, Nanjing Medical University, Nanjing 211166, China (BAO Yunying, ZHANG Lulu, LU Shengnan);
    Changzhou Institute for Advanced Study of Public Health, Nanjing Medical University, Changzhou 213000, China (SHEN Sipeng)
  • Received:2025-02-07 Online:2025-07-20 Published:2025-07-22
  • Contact: SHEN Sipeng, Email: sshen@njmu.edu.cn

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摘要： 目的 探索基于蛋白质组学的生物学年龄与肿瘤的发病、死亡和预后的关联。 方法 本研究共纳入52 680例英国生物样本库(UK Biobank)蛋白质组学队列参与者,分析了18种新发病例数超过100例的肿瘤。生物学年龄采用基于蛋白质组学和机器学习的ProtAge-204模型进行计算。时序年龄与生物学年龄的关联采用线性相关分析进行评估,生物学年龄与全肿瘤/各肿瘤的发病、死亡和预后关联均采用Cox比例风险模型进行分析。 结果 ProtAge-204生物学年龄在验证集中与时序年龄的关联较高(R2=0.84),可作为生物学年龄的标志物。生物学年龄与全肿瘤发病风险(HRper year=1.03, 95%CI: 1.02~1.04)、全肿瘤死亡风险(HRper year=1.09, 95%CI: 1.07~1.11)和预后(HRper year=1.05, 95%CI: 1.02~1.17)均显著关联,且在死亡风险(HR时序=1.02)和预后结局(HR时序=0.99)预测中表现优于传统的时序年龄。各癌种分析发现生物学年龄与8种肿瘤发病风险显著关联,与7种肿瘤导致死亡显著关联,分别优于时序年龄的6种和2种。在肿瘤预后方面2种年龄均未呈现出广泛的关联趋势。 结论 蛋白质组学构建的生物学年龄与肿瘤发病、因肿瘤死亡和预后结局关联显著,优于传统的时序年龄,推荐作为肿瘤衰老生物标志物使用。

关键词: 生物学年龄, 蛋白质组学, 肿瘤, 人群队列

Abstract: Objective To explore the association between proteomics-based biological age and the incidence, mortality due to tumors and prognostic outcomes of cancers. Methods A total of 52 680 participants from the proteomics cohort of the UK Biobank were enrolled in this study. Eighteen types of cancers with more than 100 new cases were analyzed. The biological age was calculated using ProtAge-204 based on proteomics and machine learning. The association between chronological age and biological age was evaluated using linear correlation. The associations between biological age and the incidence, mortality due to tumors, and prognosis of all tumors and each individual tumor were analyzed using the Cox proportional hazards model. Results The ProtAge-204 biological age was highly associated with chronological age in the validation set (R2=0.84), which could be used as a biomarker of biological age. Biological age was significantly associated with the risk of overall cancer incidence (HRper year=1.03, 95%CI: 1.02－1.04), the risk of overall cancer mortality (HRper year=1.09, 95%CI: 1.07－1.11), and prognostic outcomes (HRper year=1.05, 95%CI: 1.02－1.17). Moreover, in terms of mortality risk (HRchronage=1.02)and prognostic outcomes(HRchronage=0.99), biological age performed better than the traditional chronological age. Subsequently, in the analysis of each cancer type, biological age was significantly associated with the incidence risk of 8 types of cancers and the tumor-related mortality of 7 types of cancers, which was better than chronological age (6 types for incidence and 2 types for mortality). In terms of prognostic outcomes, neither age type showed a widespread association trend. Conclusions In this study, biological age constructed by proteomic biomarkers is significantly associated with cancer incidence, tumor-related mortality, and prognostic outcomes. Compared with chronological age, it has certain advantages and is recommended as a biomarker of aging.

Key words: biological age, proteomics, cancer, population cohort

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