Back to Search
Start Over
Graphene-based synthetic peptide electrochemical sensor for colorectal cancer diagnosis.
- Source :
- Alexandria Engineering Journal; Aug2024, Vol. 101, p90-97, 8p
- Publication Year :
- 2024
-
Abstract
- This work reports the development of an electrochemical sensor using graphene-peptide conjugates for detecting colorectal cancer (CRC) biomarker leucine-rich alpha-2 glycoprotein-1 (LRG1). To enable LRG1 quantification, we rationally designed peptides with dual graphene anchoring motifs for optimal orientation and binding activity when immobilized on a reduced graphene oxide (rGO) electrochemical transducer surface. The graphene nanomaterial provides several advantages such as high conductivity, large surface area, and excellent stability that can enhance the sensor's analytical performance metrics. Furthermore, the synthetic peptides offer benefits like smaller size, specificity, ease of modification and cost-effective production compared to traditional antibody receptors. Under optimized conditions, the peptide sensor exhibited high sensitivity of 22.3 μA/(ng/mL.cm<superscript>2</superscript>), low limit of detection (75 pg/mL LRG1 in serum), accuracy of 101.1 % spiked recovery, and precision within 6 % RSD. Testing with colonoscopy-classified patient serum specimens discriminated normal, precancerous adenomatous polyps and malignant carcinoma stages based on LRG1 overexpression. A 24 % elevation for adenomas and 103 % higher levels in CRC were observed. Validation with spiked plasma samples indicated 97–104 % recovery and <7 % RSD, proving accurate detection capability. Comparison to antibody-based sensors showed superior linear range, sensitivity, reproducibility, and faster assay time. This demonstrates the promise of computational peptide designing combined with advanced nanomaterials for electrochemical detection of CRC progression through serum protein biomarkers. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 11100168
- Volume :
- 101
- Database :
- Supplemental Index
- Journal :
- Alexandria Engineering Journal
- Publication Type :
- Academic Journal
- Accession number :
- 178734200
- Full Text :
- https://doi.org/10.1016/j.aej.2024.05.048