Machine learning-assisted biosensor for microRNA analysis based on RCA-mediated hemin/G-quadruplex and...

Area of Science:

• Biomedical Engineering
• Analytical Chemistry
• Molecular Diagnostics

Background:

• Tumor biomarkers are crucial for early cancer detection.
• Existing miRNA detection methods often lack speed, user-friendliness, or precision, particularly in resource-limited settings.
• There is a need for advanced diagnostic tools for early cancer identification.

Purpose of the Study:

• To develop a novel label-free fluorescence biosensor for ultrasensitive and specific detection of miRNA let-7a.
• To integrate red-emitting carbon dots (R-CDs) with nicking enzyme-enhanced rolling circle amplification (RCA).
• To provide a rapid and user-friendly diagnostic tool for early cancer detection.

Main Methods:

• Synthesis and functionalization of red-emitting carbon dots (R-CDs) with thiol groups.
• Design of a template strand for G-quadruplex formation, miRNA let-7a complementarity, and nicking enzyme recognition.
• Initiation of RCA using miRNA let-7a as a primer, followed by nicking enzyme-triggered amplification.
• Formation of DNAzymes with peroxidase-like activity and subsequent oxidation of R-CDs-SH leading to fluorescence quenching.

Main Results:

• The developed biosensor demonstrated ultrasensitive detection of miRNA let-7a.
• An ultralow detection limit of 0.16 fM was achieved.
• The system successfully integrated R-CDs, a label-free approach, and nicking-enhanced RCA for miRNA identification.

Conclusions:

• The novel label-free fluorescence biosensor offers a promising platform for early cancer diagnostics.
• The integration of R-CDs and nicking-enhanced RCA provides a sensitive and specific method for miRNA detection.
• This technology has the potential to improve cancer diagnosis in resource-limited environments.