Facile PEG-based isolation and classification of cancer extracellular vesicles and particles with label-free surface-enhanced Raman scattering and pattern recognition algorithm
Pengju Yin, Guoqian Li, Baoyue Zhang, Haque Farjana, Lei Zhao, Hongwei Qin, Bo Hu, Jian Zhen Ou, Jie Tian
Abstract
. Based on the principal component analysis and support vector machine (PCA-SVM) algorithm, cancer and normal EVPs were classified with 97.4% accuracy. However, among the cancer EVPs, the accuracy, precision, and sensitivity were found to be 90.0%, 90.9%, and 83.3% for THP-1; 86.7%, 80.0%, and 92.3% for DU-145; 96.7%, 83.3%, and 100% for COLO-205, respectively. Thus, this work will improve the isolation, detection, and classification of EVPs and promote the development of cancer liquid biopsies.
Topics & Concepts
Extracellular vesiclesExtracellularVesiclePEG ratioChemistryLiquid biopsyRaman scatteringBiophysicsCancerRaman spectroscopyBiochemistryBiologyCell biologyPhysicsOpticsMembraneGeneticsFinanceEconomicsExtracellular vesicles in diseaseNanoplatforms for cancer theranosticsRNA Interference and Gene Delivery