Morcuende-Ventura Violeta, Sánchez-Gracia Oscar, Abian-Franco Natalia, Jiménez-Pardo Isabel, Herrer Lucía, Castillo-Vallés Martín, Lancelot Alexandre, Falcó-Martí F Javier, Hermoso-Durán Sonia, Pazo-Cid Roberto, Lanas Ángel, Velazquez-Campoy Adrián, Sierra Teresa, Abian Olga
Instituto de Nanociencia y Materiales de Aragón (INMA), CSIC-Universidad de Zaragoza, 50009 Zaragoza, Spain.
Departamento de Química Orgánica, Facultad de Ciencias, Universidad de Zaragoza, 50009 Zaragoza, Spain.
Anal Chem. 2025 Jul 8;97(26):13850-13860. doi: 10.1021/acs.analchem.5c00974. Epub 2025 Jun 23.
: Early detection of oncological diseases such as pancreatic ductal adenocarcinoma (PDAC) and ovarian cancer (OV) is pivotal for successful treatment but remains a significant challenge due to the lack of sensitive and specific diagnostic tests. Fluorescence spectroscopy, enhanced by the interaction of serum proteins with nanoparticles (NPs) based on linear-dendritic block copolymers, has emerged as a promising technique for the noninvasive detection of these malignancies. This study introduces a novel array-based assay methodology to evaluate the diagnostic capabilities of various NPs within serum samples using fluorescence. : We synthesized three types of NPs (1-SH, 2-OH, 3-NH) and analyzed their fluorescence spectra in serum samples from patients with PDAC, OV, and control subjects. The samples were excited at 330 and 350 nm wavelengths to obtain their fluorescence emission spectra. An array of machine learning algorithms was applied, including boosting and tree-based methods, to assess the ability of the spectral data to discriminate between pathological and nonpathological states. The algorithms' performance was measured by the area under the receiver operating characteristic curves (AUC). : The fluorescence spectra revealed distinct patterns for PDAC and OV pathologies. 3-NH NPs exhibited the highest differential capacity with AUCs exceeding 80% for PDAC across all algorithms, except one. 2-OH NPs showed a strong discriminatory ability for OV with AUCs over 70%, utilizing all but one of the algorithms. 1-SH NPs, however, did not significantly increase differentiability. Boosting algorithms generally outperformed other methods, indicating their suitability for this diagnostic approach. : The proposed assay array methodology enables the systematic evaluation of NPs' diagnostic potential using fluorescence spectroscopy. The differential interactions between NPs and serum proteins specific to PDAC and OV highlight the method's capability to discern pathological states. These findings suggest a path forward for developing NP-assisted fluorescence spectroscopy as a viable tool for cancer diagnostics, potentially leading to earlier detection and improved patient outcomes.
早期检测诸如胰腺导管腺癌（PDAC）和卵巢癌（OV）等肿瘤疾病对于成功治疗至关重要，但由于缺乏灵敏且特异的诊断测试，仍然是一项重大挑战。基于线性 - 树枝状嵌段共聚物的血清蛋白与纳米颗粒（NPs）相互作用增强的荧光光谱法，已成为一种用于这些恶性肿瘤无创检测的有前景的技术。本研究引入了一种基于阵列的新型检测方法，以利用荧光评估血清样本中各种纳米颗粒的诊断能力。我们合成了三种类型的纳米颗粒（1 - SH、2 - OH、3 - NH），并分析了它们在来自PDAC患者、OV患者和对照受试者的血清样本中的荧光光谱。样本在330和350 nm波长下激发以获得其荧光发射光谱。应用了一系列机器学习算法，包括增强算法和基于树的方法，以评估光谱数据区分病理状态和非病理状态的能力。算法的性能通过接收器操作特征曲线（AUC）下的面积来衡量。荧光光谱揭示了PDAC和OV病理的不同模式。3 - NH纳米颗粒表现出最高的鉴别能力，在所有算法中，除一种算法外，PDAC的AUC超过80%。2 - OH纳米颗粒对OV表现出很强的鉴别能力，除一种算法外，利用所有算法时AUC超过70%。然而，1 - SH纳米颗粒并没有显著提高可区分性。增强算法通常优于其他方法，表明它们适用于这种诊断方法。所提出的检测阵列方法能够使用荧光光谱法系统地评估纳米颗粒的诊断潜力。PDAC和OV特有的纳米颗粒与血清蛋白之间的差异相互作用突出了该方法辨别病理状态的能力。这些发现为开发纳米颗粒辅助荧光光谱法作为癌症诊断的可行工具指明了一条道路，这可能会带来更早的检测和改善患者的治疗结果。
