Multispectral 3D DNA Machine Combined with Multimodal Machine Learning for Noninvasive Precise Diagnosis of Bladder Cancer.
Anal Chem
; 96(24): 10046-10055, 2024 06 18.
Article
em En
| MEDLINE
| ID: mdl-38845359
ABSTRACT
Extracellular vesicle (EV) molecular phenotyping offers enormous opportunities for cancer diagnostics. However, the majority of the associated studies adopted biomarker-based unimodal analysis to achieve cancer diagnosis, which has high false positives and low precision. Herein, we report a multimodal platform for the high-precision diagnosis of bladder cancer (BCa) through a multispectral 3D DNA machine in combination with a multimodal machine learning (ML) algorithm. The DNA machine was constructed using magnetic microparticles (MNPs) functionalized with aptamers that specifically identify the target of interest, i.e., five protein markers on bladder-cancer-derived urinary EVs (uEVs). The aptamers were hybridized with DNA-stabilized silver nanoclusters (DNA/AgNCs) and a G-quadruplex/hemin complex to form a sensing module. Such a DNA machine ensured multispectral detection of protein markers by fluorescence (FL), inductively coupled plasma mass spectrometry (ICP-MS), and UV-vis absorption (Abs). The obtained data sets then underwent uni- or multimodal ML for BCa diagnosis to compare the analytical performance. In this study, urine samples were obtained from our prospective cohort (n = 45). Our analytical results showed that the 3D DNA machine provided a detection limit of 9.2 × 103 particles mL-1 with a linear range of 4 × 104 to 5 × 107 particles mL-1 for uEVs. Moreover, the multimodal data fusion model exhibited an accuracy of 95.0%, a precision of 93.1%, and a recall rate of 93.2% on average, while those of the three types of unimodal models were no more than 91%. The elevated diagnosis precision by using the present fusion platform offers a perspective approach to diminishing the rate of misdiagnosis and overtreatment of BCa.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Neoplasias da Bexiga Urinária
/
Aprendizado de Máquina
Limite:
Humans
Idioma:
En
Revista:
Anal Chem
Ano de publicação:
2024
Tipo de documento:
Article