Diagnosis and prognosis of breast cancer by high-performance serum metabolic fingerprints.
Diagnosis and prognosis of breast cancer by high-performance serum metabolic fingerprints.
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DOI：
10.1073/pnas.2122245119
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发表时间：
2022-03-22
影响因子：
11.1
通讯作者：
Wang H
中科院分区：
文献类型：
--
作者：
Huang Y;Du S;Liu J;Huang W;Liu W;Zhang M;Li N;Wang R;Wu J;Chen W;Jiang M;Zhou T;Cao J;Yang J;Huang L;Gu A;Niu J;Cao Y;Zong WX;Wang X;Liu J;Qian K;Wang H
乳腺癌（BrCa）是世界范围内最常见的恶性肿瘤，高性能代谢分析在BrCa的诊断和预后方面正在兴起。在这里，我们使用纳米颗粒增强激光解吸/电离质谱法在几秒钟内记录BrCa的血清代谢指纹，实现了直接血清检测的高重现性和低消耗。我们的分析方法，结合机器学习算法的帮助，被证明提供了高诊断效率，准确率为88.8%和理想的预后预测（P < 0.005）。此外，七个代谢生物标志物差异富集BrCa血清及其相关途径进行了鉴定。总之，我们的研究结果提供了一种工具来表征BrCa，并强调某些代谢特征作为疾病的潜在诊断和预后因素，包括但不限于BrCa。高性能代谢分析在乳腺癌（BrCa）的诊断和预后中正在出现。尽管如此，仍然需要先进的工具来提供代谢分析的应用潜力。在这里，我们使用快速纳米颗粒增强激光解吸/电离质谱（NPELDI-MS）在几秒钟内记录BrCa的血清代谢指纹（SMF），实现了无需治疗的直接血清检测的高重现性和低消耗。随后，通过NPELDI-MS生成的SMF的机器学习用作有效的读出，以区分BrCa和非BrCa，曲线下面积为0.948。此外，使用SMF构建了代谢预后评分系统，对BrCa具有有效的预测性能（P < 0.005）。最后，我们确定了一个生物标志物面板的七种代谢物，差异富集BrCa血清及其相关途径。总之，我们的研究结果提供了一种有效的血清代谢工具来表征BrCa，并强调某些代谢特征作为疾病的潜在诊断和预后因素，包括但不限于BrCa。
Breast cancer (BrCa) is the most common cancer worldwide, and high-performance metabolic analysis is emerging in diagnosis and prognosis of BrCa. Here, we used nanoparticle-enhanced laser desorption/ionization mass spectrometry to record serum metabolic fingerprints of BrCa in seconds, achieving high reproducibility and low consumption of direct serum detection. Our analytical method, combined with the aid of machine learning algorithms, was demonstrated to provide high diagnostic efficiency with accuracy of 88.8% and desirable prognostic prediction (P < 0.005). Furthermore, seven metabolic biomarkers differentially enriched in BrCa serum and their related pathways were identified. Together, our findings provide a tool to characterize BrCa and highlight certain metabolic signatures as potential diagnostic and prognostic factors of diseases including but not limited to BrCa. High-performance metabolic analysis is emerging in the diagnosis and prognosis of breast cancer (BrCa). Still, advanced tools are in demand to deliver the application potentials of metabolic analysis. Here, we used fast nanoparticle-enhanced laser desorption/ionization mass spectrometry (NPELDI-MS) to record serum metabolic fingerprints (SMFs) of BrCa in seconds, achieving high reproducibility and low consumption of direct serum detection without treatment. Subsequently, machine learning of SMFs generated by NPELDI-MS functioned as an efficient readout to distinguish BrCa from non-BrCa with an area under the curve of 0.948. Furthermore, a metabolic prognosis scoring system was constructed using SMFs with effective prediction performance toward BrCa (P < 0.005). Finally, we identified a biomarker panel of seven metabolites that were differentially enriched in BrCa serum and their related pathways. Together, our findings provide an efficient serum metabolic tool to characterize BrCa and highlight certain metabolic signatures as potential diagnostic and prognostic factors of diseases including but not limited to BrCa.
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影响因子：
16.6
作者：
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254.7
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