Background: Lung cancer is among the most prevalent and fatal cancers worldwide. Traditional diagnostic methods, such as computed tomography, are not ideal for screening due to their high cost and radiation exposure. In contrast, blood-based diagnostics, as non-invasive approaches, are expected to reduce patient burden, thereby increasing screening participation and ultimately improving survival rates. However, conventional tumor markers have shown limited effectiveness in early detection. Methods: We recruited 199 patients with lung cancer and 590 healthy volunteers. Nine tumor markers (CEA, CA19-9, CYFRA, AFP, PSA, CA125, CA15-3, SCC antigen, and NCC-ST439) were analyzed, along with enriched glycopeptides (EGPs) derived from serum proteins using liquid chromatography–mass spectrometry. Machine learning models, including decision trees and deep learning approaches, were employed to develop a predictive model for accurately distinguishing lung cancer from healthy controls based on tumor markers and EGP profiles. Results: We found that α1-antitrypsin with fully sialylated biantennary glycan, attached to asparagine 271 (AT271-FSG), and α2-macroglobulin with fully sialylated biantennary glycan, attached to asparagine 70 (MG70-FSG), could significantly distinguish between patients with lung cancer and healthy individuals. Comprehensive Serum Glycopeptide Spectra Analysis (CSGSA), integrating nine conventional tumor markers and 1688 EGPs using a machine learning model, enhanced diagnostic accuracy and achieved an ROC-AUC score of 0.935. It also identified stage I cases with an ROC-AUC of 0.914, indicating the possibility of early-stage detection. The PPV reached 2.8%, which was sufficient for practical application. Conclusions: This method represents a significant advancement in cancer diagnostics, combining multiple biomarkers with cutting-edge machine learning to improve the early detection of lung cancer.
背景：肺癌是全球范围内最常见且致命的癌症之一。传统诊断方法如计算机断层扫描，因其高成本和辐射暴露，并不适合用于筛查。相比之下，基于血液的诊断作为非侵入性方法，有望减轻患者负担，从而提高筛查参与率，最终改善生存率。然而，传统肿瘤标志物在早期检测中的效果有限。方法：我们招募了199名肺癌患者和590名健康志愿者。分析了九种肿瘤标志物（CEA、CA19-9、CYFRA、AFP、PSA、CA125、CA15-3、SCC抗原和NCC-ST439），以及通过液相色谱-质谱法从血清蛋白中提取的富集糖肽。采用机器学习模型，包括决策树和深度学习方法，基于肿瘤标志物和EGP谱开发了一个预测模型，以准确区分肺癌患者和健康对照。结果：我们发现，附着在天冬酰胺271上的完全唾液酸化双天线聚糖的α1-抗胰蛋白酶（AT271-FSG）和附着在天冬酰胺70上的完全唾液酸化双天线聚糖的α2-巨球蛋白（MG70-FSG）能够显著区分肺癌患者和健康个体。综合血清糖肽谱分析（CSGSA）通过机器学习模型整合了九种传统肿瘤标志物和1688个EGP，提高了诊断准确性，并获得了0.935的ROC-AUC评分。该方法还能识别I期病例，ROC-AUC为0.914，表明其具备早期检测的潜力。阳性预测值达到2.8%，足以满足实际应用需求。结论：该方法代表了癌症诊断领域的重大进展，通过将多种生物标志物与前沿机器学习技术相结合，提高了肺癌的早期检测能力。
肿瘤(癌症)患者之家