Rapid detection of cancer DNA in human blood using cysteamine-capped AuNPs and a machine learning-enabled smartphone
Issued Date
2023-01-05
Resource Type
eISSN
20462069
Scopus ID
2-s2.0-85146095288
Journal Title
RSC Advances
Volume
13
Issue
2
Start Page
1301
End Page
1311
Rights Holder(s)
SCOPUS
Bibliographic Citation
RSC Advances Vol.13 No.2 (2023) , 1301-1311
Suggested Citation
Koowattanasuchat S., Ngernpimai S., Matulakul P., Thonghlueng J., Phanchai W., Chompoosor A., Panitanarak U., Wanna Y., Intharah T., Chootawiriyasakul K., Anata P., Chaimnee P., Thanan R., Sakonsinsiri C., Puangmali T. Rapid detection of cancer DNA in human blood using cysteamine-capped AuNPs and a machine learning-enabled smartphone. RSC Advances Vol.13 No.2 (2023) , 1301-1311. 1311. doi:10.1039/d2ra05725e Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/81721
Title
Rapid detection of cancer DNA in human blood using cysteamine-capped AuNPs and a machine learning-enabled smartphone
Other Contributor(s)
Abstract
DNA methylation occurs when a methyl group is added to a cytosine (C) residue's fifth carbon atom, forming 5-methylcytosine (5-mC). Cancer genomes have a distinct methylation landscape (Methylscape), which could be used as a universal cancer biomarker. This study developed a simple, low-cost, and straightforward Methylscape sensing platform using cysteamine-decorated gold nanoparticles (Cyst/AuNPs), in which the sensing principle is based on methylation-dependent DNA solvation. Normal and cancer DNAs have distinct methylation profiles; thus, they can be distinguished by observing the dispersion of Cyst/AuNPs adsorbed on these DNA aggregates in MgCl2 solution. After optimising the MgCl2, Cyst/AuNPs, DNA concentration, and incubation time, the optimised conditions were used for leukemia screening, by comparing the relative absorbance (ΔA650/525). Following the DNA extraction from actual blood samples, this sensor demonstrated effective leukemia screening in 15 minutes with high sensitivity, achieving 95.3% accuracy based on the measurement by an optical spectrophotometer. To further develop for practical realisation, a smartphone assisted by machine learning was used to screen cancer patients, achieving 90.0% accuracy in leukemia screening. This sensing platform can be applied not only for leukemia screening but also for other cancers associated with epigenetic modification.