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IF 2.8 3区医学 Q2 BIOCHEMICAL RESEARCH METHODS

Clinical proteomics Pub Date : 2025-04-07 DOI:10.1186/s12014-025-09532-w

Bin Jia, Tingting Wang, Liangxuan Pan, Xiaoyao Du, Jing Yang, Fei Gao, Lujian Liao, Bianqin Guo, Junqiang Dong

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引用次数: 0

摘要

背景：直径为 8-30 毫米的肺结节具有很高的发生率，区分良性和恶性结节可大大改善肺癌患者的预后。然而，灵敏而特异的液体活检方法尚未达到令人满意的临床目标：方法：我们招募了三个队列共 185 名确诊为良性（BE）和恶性（MA）肺结节的患者。利用数据独立采集（DIA）质谱技术，我们对这些患者的血浆蛋白质组进行了量化。然后，我们利用队列 1 作为发现数据集，队列 2 和队列 3 作为独立验证数据集，进行了逻辑回归分析，对良性结节和恶性结节进行了分类。我们还开发了一种靶向多反应监测（MRM）方法，用于测量血浆样本中选定的六种肽标记物的浓度：我们共对 451 种血浆蛋白进行了量化，其中 15 种蛋白上调，5 种蛋白下调，这些蛋白均来自被诊断为恶性结节的患者。逻辑回归确定了一个由 APOA4、CD14、PFN1、APOB、PLA2G7 和 IGFBP2 组成的六蛋白面板，该面板能更准确地对良性和恶性结节进行分类。在群组 1 中，训练和测试的曲线下面积（AUC）分别达到了 0.87 和 0.91。我们的灵敏度达到了 100%，特异性达到了 40%，阳性预测值（PPV）达到了 62.5%，阴性预测值（NPV）达到了 100%。在两个独立的队列中，队列 2 中的 6 个生物标记物面板的灵敏度、特异性、PPV 和 NPV 分别为 96.2%、35%、65.8% 和 87.5%，队列 3 中的灵敏度、特异性、PPV 和 NPV 分别为 91.4%、54.2%、74.4% 和 81.3%。我们采用靶向液相色谱-质谱/质谱方法定量检测了六种肽的血浆浓度，并应用逻辑回归对良性和恶性结节进行了分类，训练和测试的AUC分别达到了0.758和0.751：我们的研究发现了一组区分良性和恶性肺结节的血浆蛋白生物标记物，值得进一步开发成具有临床价值的检测方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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An integrated proteomic classifier to distinguish benign from malignant pulmonary nodules.

Background: Pulmonary nodule with diameters ranging 8-30 mm has a high occurrence rate, and distinguishing benign from malignant nodules can greatly improve the patient outcome of lung cancer. However, sensitive and specific liquid-biopsy methods have yet to achieve satisfactory clinical goals.

Methods: We enrolled three cohorts and a total of 185 patients diagnosed with benign (BE) and malignant (MA) pulmonary nodules. Utilizing data-independent acquisition (DIA) mass spectrometry, we quantified plasma proteome from these patients. We then performed logistic regression analysis to classify benign from malignant nodules, using cohort 1 as discovery data set and cohort 2 and 3 as independent validation data sets. We also developed a targeted multi-reaction monitoring (MRM) method to measure the concentration of the selected six peptide markers in plasma samples.

Results: We quantified a total of 451 plasma proteins, with 15 up-regulated and 5 down-regulated proteins from patients diagnosed as having malignant nodules. Logistic regression identified a six-protein panel comprised of APOA4, CD14, PFN1, APOB, PLA2G7, and IGFBP2 that classifies benign and malignant nodules with improved accuracy. In cohort 1, the area under curve (AUC) of the training and testing reached 0.87 and 0.91, respectively. We achieved a sensitivity of 100%, specificity of 40%, positive predictive value (PPV) of 62.5%, and negative predictive value (NPV) of 100%. In two independent cohorts, the 6-biomarker panel showed a sensitivity, specificity, PPV, and NPV of 96.2%, 35%, 65.8%, and 87.5% respectively in cohort 2, and 91.4%, 54.2%, 74.4%, and 81.3% respectively in cohort 3. We performed a targeted LC-MS/MS method to quantify plasma concentration of the six peptides and applied logistic regression to classify benign and malignant nodules with AUC of the training and testing reached 0.758 and 0.751, respectively.

Conclusions: Our study identified a panel of plasma protein biomarkers for distinguishing benign from malignant pulmonary nodules that worth further development into a clinically valuable assay.

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来源期刊

Clinical proteomics BIOCHEMICAL RESEARCH METHODS-

CiteScore

5.80

自引率

2.60%

发文量

审稿时长

17 weeks

期刊介绍： Clinical Proteomics encompasses all aspects of translational proteomics. Special emphasis will be placed on the application of proteomic technology to all aspects of clinical research and molecular medicine. The journal is committed to rapid scientific review and timely publication of submitted manuscripts.