基于抗幽门螺杆菌蛋白和胃蛋白酶原抗体反应的非贲门胃腺癌风险预测模型。
A Predictive Model of Noncardia Gastric Adenocarcinoma Risk Using Antibody Response to Helicobacter pylori Proteins and Pepsinogen.
机构信息
Department of Epidemiology, University of North Carolina at Chapel Hill, Gillings School of Global Public Health, Chapel Hill, North Carolina.
Department of Biostatistics, University of North Carolina at Chapel Hill, Gillings School of Global Public Health, Chapel Hill, North Carolina.
出版信息
Cancer Epidemiol Biomarkers Prev. 2022 Apr 1;31(4):811-820. doi: 10.1158/1055-9965.EPI-21-0869.
BACKGROUND
Blood-based biomarkers for gastric cancer risk stratification could facilitate targeting screening to people who will benefit from it most. The ABC Method, which stratifies individuals by their Helicobacter pylori infection and serum-diagnosed chronic atrophic gastritis status, is currently used in Japan for this purpose. Most gastric cancers are caused by chronic H. pylori infection, but few studies have explored the capability of antibody response to H. pylori proteins to predict gastric cancer risk in addition to established predictors.
METHODS
We used the least absolute shrinkage and selection operator (Lasso) to build a predictive model of noncardia gastric adenocarcinoma risk from serum data on pepsinogen and antibody response to 13 H. pylori antigens as well as demographic and lifestyle factors from a large international study in East Asia.
RESULTS
Our best model had a significantly (P < 0.001) higher AUC of 73.79% [95% confidence interval (CI), 70.86%-76.73%] than the ABC Method (68.75%; 95% CI, 65.91%-71.58%). At 75% specificity, the new model had greater sensitivity than the ABC Method (58.67% vs. 52.68%) as well as NPV (68.24% vs. 66.29%).
CONCLUSIONS
Along with serologically defined chronic atrophic gastritis, antibody response to the H. pylori proteins HP 0305, HP 1564, and UreA can improve the prediction of gastric cancer risk.
IMPACT
The new risk stratification model could help target more invasive gastric screening resources to individuals at high risk.
背景
用于胃癌风险分层的基于血液的生物标志物可以促进将筛查针对最受益的人群。ABC 方法根据幽门螺杆菌感染和血清诊断的慢性萎缩性胃炎状态对个体进行分层，目前在日本用于此目的。大多数胃癌是由慢性 H. pylori 感染引起的，但很少有研究探讨针对 H. pylori 蛋白的抗体反应除了已建立的预测因子之外，预测胃癌风险的能力。
方法
我们使用最小绝对收缩和选择算子（Lasso），从东亚一项大型国际研究中血清胃蛋白酶原和针对 13 种 H. pylori 抗原的抗体反应以及人口统计学和生活方式因素的数据中，构建非贲门胃腺癌风险的预测模型。
结果
我们的最佳模型具有显著更高的 AUC（73.79% [95%置信区间（CI），70.86%-76.73%]）（P < 0.001）比 ABC 方法（68.75%；95% CI，65.91%-71.58%）。在特异性为 75%时，新模型的敏感性高于 ABC 方法（58.67% vs. 52.68%）和阴性预测值（68.24% vs. 66.29%）。
结论
除了血清学定义的慢性萎缩性胃炎外，针对 H. pylori 蛋白 HP 0305、HP 1564 和 UreA 的抗体反应可以提高胃癌风险的预测。
影响
新的风险分层模型可以帮助将更具侵入性的胃癌筛查资源靶向高风险个体。
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