Screening human lung cancer with predictive models of serum magnetic resonance spectroscopy metabolomics.
Screening human lung cancer with predictive models of serum magnetic resonance spectroscopy metabolomics.
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DOI：
10.1073/pnas.2110633118
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发表时间：
2021-12-21
影响因子：
11.1
通讯作者：
Cheng, Leo L
中科院分区：
文献类型：
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作者：
Schult, Tjada A;Lauer, Mara J;Cheng, Leo L
目前人类肺癌的高死亡率很大程度上源于缺乏可行的早期疾病检测工具。血清代谢组学预测模型的有效测试能够提示患有疾病的患者，可以加快对患者进行专门影像评估的分类。在这里，我们使用训练-验证-测试-队列设计，建立了基于高分辨率魔角旋转 (HRMAS) 磁共振波谱 (MRS) 的代谢组学预测模型，使用疾病诊断前收集的血清样本来指示肺癌的存在和患者的生存情况。研究的血清样本是从肺癌诊断前（5 年内）和诊断时的 79 名患者收集的。通过比较我们的训练队列之间的血清代谢组模式来建立疾病预测模型：诊断时的肺癌患者和匹配的健康对照。然后应用这些预测模型来评估我们的验证和测试队列的血清样本，这些样本全部从肺癌诊断前的患者身上收集。我们的研究发现，预测模型产生的检测前血清样本值介于诊断时患者值和健康对照值之间；这些中间值与两组显着不同，癌症预测的 F1 得分 = 0.628。此外，从诊断前血清测量的代谢组学预测模型的值可以显着预测局部疾病患者的 5 年生存率。
The current high mortality of human lung cancer stems largely from the lack of feasible, early disease detection tools. An effective test with serum metabolomics predictive models able to suggest patients harboring disease could expedite triage patient to specialized imaging assessment. Here, using a training-validation-testing-cohort design, we establish our high-resolution magic angle spinning (HRMAS) magnetic resonance spectroscopy (MRS)-based metabolomics predictive models to indicate lung cancer presence and patient survival using serum samples collected prior to their disease diagnoses. Studied serum samples were collected from 79 patients before (within 5.0 y) and at lung cancer diagnosis. Disease predictive models were established by comparing serum metabolomic patterns between our training cohorts: patients with lung cancer at time of diagnosis, and matched healthy controls. These predictive models were then applied to evaluate serum samples of our validation and testing cohorts, all collected from patients before their lung cancer diagnosis. Our study found that the predictive model yielded values for prior-to-detection serum samples to be intermediate between values for patients at time of diagnosis and for healthy controls; these intermediate values significantly differed from both groups, with an F1 score = 0.628 for cancer prediction. Furthermore, values from metabolomics predictive model measured from prior-to-diagnosis sera could significantly predict 5-y survival for patients with localized disease.