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Machine Learning-Based Label-Free SERS Profiling of Exosomes for Accurate Fuzzy Diagnosis of Cancer and Dynamic Monitoring of Drug Therapeutic Processes.
Diao, Xingkang; Li, Xinli; Hou, Shuping; Li, Haijuan; Qi, Guohua; Jin, Yongdong.
Afiliação
  • Diao X; State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022, Jilin, P. R. China.
  • Li X; University of Science and Technology of China, Hefei 230026, P. R. China.
  • Hou S; College of Instrumentation and Electrical Engineering, Jilin University, Changchun 130061, P. R. China.
  • Li H; State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022, Jilin, P. R. China.
  • Qi G; University of Science and Technology of China, Hefei 230026, P. R. China.
  • Jin Y; State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022, Jilin, P. R. China.
Anal Chem ; 95(19): 7552-7559, 2023 05 16.
Article em En | MEDLINE | ID: mdl-37139959
ABSTRACT
Exosomes are a class of extracellular vesicles secreted by cells, which can be used as promising noninvasive biomarkers for the early diagnosis and treatment of diseases, especially cancer. However, due to the heterogeneity of exosomes, it remains a grand challenge to distinguish accurately and reliably exosomes from clinical samples. Herein, we achieve accurate fuzzy discrimination of exosomes from human serum samples for accurate diagnosis of breast cancer and cervical cancer through machine learning-based label-free surface-enhanced Raman spectroscopy (SERS), by using "hot spot" rich 3D plasmonic AuNPs nanomembranes as substrates. Due to the existence of some weak distinguishable SERS fingerprint signals and the high sensitivity of the method, the machine learning-based SERS analysis can precisely identify three (normal and cancerous) cell lines, two of which are different types of cancer cells, without specific labeling of biomarkers. The prediction accuracy based on the machine learning algorithm was up to 91.1% for the discrimination of different cell lines (H8, HeLa, and MCF-7 cell)-derived exosomes. Our model trained with SERS spectra of cell-derived exosomes could reach 93.3% prediction accuracy for clinical samples. Furthermore, the action mechanism of the chemotherapeutic process of MCF-7 cells can be revealed by dynamic monitoring of SERS profiling of the exosomes secreted. The method would be useful for noninvasive and accurate diagnosis and postoperative assessment of cancer or other diseases in the future.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Nanopartículas Metálicas / Exossomos / Neoplasias Tipo de estudo: Diagnostic_studies / Prognostic_studies / Screening_studies Limite: Female / Humans Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Nanopartículas Metálicas / Exossomos / Neoplasias Tipo de estudo: Diagnostic_studies / Prognostic_studies / Screening_studies Limite: Female / Humans Idioma: En Ano de publicação: 2023 Tipo de documento: Article