PO.PR01.03 · 预防研究
基于蛋白质组学的模型提高上消化道肿瘤预测的准确性
A proteomics-based model improves the accuracy of upper gastrointestinal cancer prediction
作者与单位 Authors & Affiliations
摘要 Abstract
中文摘要
背景：上消化道肿瘤（UGI），包括食管癌（EC）、胃食管交界处癌（GEJ）和胃癌（GC），构成了重大的全球健康负担，其预后在很大程度上取决于早期诊断。目前的筛查策略在识别高危个体方面尚不充分。尽管蛋白质组学的进展为风险评估和早期检测带来了希望，但其在UGI肿瘤筛查中的应用仍未得到充分探索。
方法：我们在UK Biobank内开展了一项前瞻性分析。参与者按7:3比例随机分为训练集和测试集。在采用Olink Explore 3072面板测定的蛋白中，缺失值>30%的蛋白被排除，最终得到2,920种蛋白的分析集。通过Cox比例风险模型（FDR < 0.05）鉴定蛋白与UGI肿瘤的关联。随后应用带十折交叉验证的L1惩罚LASSO-Cox回归筛选候选蛋白质组学生物标志物。随后构建了两个风险预测模型（一个基于流行病学因素的简单模型，以及一个进一步纳入蛋白的整合模型）并进行内部验证。使用曲线下面积（AUC）评估模型的区分能力，并通过DeLong检验进行比较。
结果：在排除具有基线肿瘤或数据缺失的参与者后，共纳入48,366名个体（中位随访14.7年；261例UGI肿瘤病例）。我们鉴定出912种与UGI肿瘤显著相关的蛋白（830种风险相关，82种保护性）。LASSO-Cox回归将该集合精简至46种蛋白（31种风险，15种保护性），包括五种新型风险相关蛋白——TEX101、MYBPC1、KIR2DL3、CLSTN2和ADAMTS4——而其余41种此前已在文献中报道。与纳入了一个新型高危状态变量（定义为年龄≥45岁加上至少一项：吸烟史、大量饮酒、癌前病变、幽门螺杆菌感染或不健康饮食）的传统模型相比，整合模型展现出更优的诊断性能，在训练集（0.83 [95% CI：0.80-0.86] 对 0.70 [0.67-0.73]）和测试集（0.81 [95% CI：0.77-0.85] 对 0.69 [0.65-0.74]；DeLong检验，p < 0.05）中的AUC均显著更高。
结论：我们构建了一个将46种血浆蛋白生物标志物与传统流行病学因素相结合的整合模型。该方法在识别UGI肿瘤方面展现出优于传统模型的性能。仍需在不同地区和人群中开展进一步的前瞻性研究，以验证这一颇具前景的早期筛查策略的普适性。
查看英文原文 English abstract
Background: Upper gastrointestinal cancers (UGI), including esophageal cancer (EC), gastroesophageal junction cancer (GEJ), and gastric cancer (GC), pose a major global health burden, with prognosis heavily dependent on early diagnosis. Current screening strategies inadequately identify high-risk individuals. While advances in proteomics offer promise for risk assessment and early detection, their application in UGI cancer screening remains underexplored.
Methods : We conducted a prospective analysis within the UK Biobank. Participants were randomly divided into training and testing sets at a 7:3 ratio. Among proteins measured using the Olink Explore 3072 panel, those with >30% missing values were excluded, resulting in a final analysis set of 2,920 proteins. The associations of proteins with UGI cancer were identified by Cox proportional hazards models (FDR < 0.05). L1-penalized LASSO-Cox regression with ten-fold cross-validation was then applied to select candidate proteomic biomarkers. Then, two risk prediction models (a simple model based on epidemiological factors and an integrated model that further incorporated proteins) were developed and internally validated. The discrimination of the models was assessed using the area under the curve (AUC) and compared via DeLong's test.
Results: After excluding participants with baseline cancer or missing data, 48,366 individuals were included (median follow-up: 14.7 years; 261 UGI cancer cases). We identified 912 proteins significantly associated with UGI cancer (830 risk-related, 82 protective). LASSO-Cox regression refined this set to 46 proteins (31 risk, 15 protective), including five novel risk-related proteins-TEX101, MYBPC1, KIR2DL3, CLSTN2, and ADAMTS4-while the remaining 41 have been previously reported in the literature. Compared to the traditional model, which incorporated a novel high-risk status variable (defined as age ≥45 years plus at least one of: smoking history, heavy alcohol consumption, precancerous lesions, Helicobacter pylori infection, or an unhealthy diet), the integrated model demonstrated superior diagnostic performance, with significantly higher AUCs in both the training set (0.83 [95% CI: 0.80-0.86] vs. 0.70 [0.67-0.73]) and the testing set (0.81 [95% CI: 0.77-0.85] vs. 0.69 [0.65-0.74]; DeLong's test, p < 0.05).
Conclusion: We developed an integrated model that combines 46 plasma protein biomarkers with traditional epidemiological factors. This approach demonstrated superior performance over the traditional model in identifying UGI cancer. Further prospective studies in diverse regions and populations are needed to validate the generalizability of this promising early screening strategy.
利益披露 Disclosure
X. Liu, None..
Z. Lyu, None..
K. Chen, None.