Machine learning of serum metabolic patterns encodes early-stage lung adenocarcinoma
Machine learning of serum metabolic patterns encodes early-stage lung adenocarcinoma
复制标题
血清代谢模式的机器学习编码早期肺腺癌
DOI：
10.1038/s41467-020-17347-6
复制
发表时间：
2020-07-16
影响因子：
16.6
通讯作者：
Qian, Kun
中科院分区：
文献类型：
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作者：
Huang, Lin;Wang, Lin;Qian, Kun
早期癌症检测大大增加了成功治疗的机会，但对一些肿瘤，包括肺腺癌（LA）的可用诊断是有限的。大规模临床应用的理想LA早期诊断必须解决快速检测、低侵入性和高性能。在这里，我们对血清代谢模式进行机器学习，以检测早期LA。我们提取直接代谢模式的优化铁粒子辅助激光解吸/电离质谱在1秒内，仅使用50 nL的血清。我们定义了100-400 Da的代谢范围，具有143 m/z特征。我们通过模式的稀疏回归机器学习诊断早期LA，灵敏度约为70-90%，特异性约为90-93%。我们确定了一组由7种代谢物和相关途径组成的生物标志物，以区分早期LA和对照组（p< 0.05）。我们的方法推进了早期癌症检测的代谢分析设计，并有望成为低成本推广到诊所的有效测试。
Early cancer detection greatly increases the chances for successful treatment, but available diagnostics for some tumours, including lung adenocarcinoma (LA), are limited. An ideal early-stage diagnosis of LA for large-scale clinical use must address quick detection, low invasiveness, and high performance. Here, we conduct machine learning of serum metabolic patterns to detect early-stage LA. We extract direct metabolic patterns by the optimized ferric particle-assisted laser desorption/ionization mass spectrometry within 1 s using only 50 nL of serum. We define a metabolic range of 100–400 Da with 143 m/z features. We diagnose early-stage LA with sensitivity~70–90% and specificity~90–93% through the sparse regression machine learning of patterns. We identify a biomarker panel of seven metabolites and relevant pathways to distinguish early-stage LA from controls (p< 0.05). Our approach advances the design of metabolic analysis for early cancer detection and holds promise as an efficient test for low-cost rollout to clinics.