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Electrochemical Sensor for the Detection and Accurate Early Diagnosis of Ovarian Cancer.
- Source :
-
ACS sensors [ACS Sens] 2024 Jun 28; Vol. 9 (6), pp. 2897-2906. Date of Electronic Publication: 2024 May 22. - Publication Year :
- 2024
-
Abstract
- Ovarian cancer (OC) has the highest mortality rate among malignant tumors, primarily because it is difficult to diagnose early. Exosomes, a type of extracellular vesicle rich in parental information, have garnered significant attention in the field of cancer diagnosis and treatment. They play an important regulatory role in the occurrence, development, and metastasis of OC. Consequently, exosomes have emerged as noninvasive biomarkers for early cancer detection. Therefore, identifying cancer-derived exosomes may offer a novel biomarker for the early detection of OC. In this study, we developed a metal-organic frameworks assembled "double hook"-type aptamer electrochemical sensor, which enables accurate early diagnosis of OC. Under optimal experimental conditions, electrochemical impedance spectroscopy technology demonstrated a good linear relationship within the concentration range of 31-3.1 × 10 <superscript>6</superscript> particles per microliter, with a detection limit as low as 12 particles per microliter. The universal exosome detection platform is constructed, and this platform can not only differentiate between high-grade serous ovarian cancer (HGSOC) patients and healthy individuals but also distinguish between HGSOC patients and nonhigh-grade serous OC (non-HGSOC). Consequently, it provides a novel strategy for the early diagnosis of OC and holds great significance in clinical differential diagnosis.
- Subjects :
- Female
Humans
Electrochemical Techniques methods
Biosensing Techniques methods
Aptamers, Nucleotide chemistry
Metal-Organic Frameworks chemistry
Exosomes chemistry
Limit of Detection
Dielectric Spectroscopy methods
Biomarkers, Tumor analysis
Ovarian Neoplasms diagnosis
Early Detection of Cancer methods
Subjects
Details
- Language :
- English
- ISSN :
- 2379-3694
- Volume :
- 9
- Issue :
- 6
- Database :
- MEDLINE
- Journal :
- ACS sensors
- Publication Type :
- Academic Journal
- Accession number :
- 38776471
- Full Text :
- https://doi.org/10.1021/acssensors.3c02776