Hand in hand catalytic hairpin assembly-based FÖrster resonance energy transfer biosensor for simultaneous detection...

The early breast cancer diagnosis is a great challenge for the treatment success. Traditional FRET biosensors often rely on a one-to-one output pattern, meaning that each target biomarker generates a single fluorescence signal, limiting both the analytical efficiency and diagnostic value. Furthermore, the use of single biomarker for early diagnosis has substantial drawbacks, as it often leads to incomplete or misleading results. The simultaneous detection of miR-155 and miR-105 offers a more robust and clinically relevant diagnostic tool, reducing the likelihood of false positives and allowing for a more accurate assessment of breast cancer at an early stage.

In this work, a hand in hand catalytic hairpin assembly (CHA) centered FÖrster resonance energy transfer (HCA-FRET) biosensor is designed, which allows two-in-one analysis of breast cancer related two biomarkers. In this system, miR-155 and miR-105, which are involved in regulating the malignant proliferation of breast cancer, are designed as two inputs as the AND DNA logic gate. Hand in hand CHA amplification circuit is effectively triggered under dual miRNA (miR-155 and miR-105), and the resulting significantly high FRET detection signal is measured which satisfies the AND logic gate function for the simultaneous detection of dual miRNA allied with breast cancer. HCA-FRET biosensor is successfully applied to detect miR-155 (0.01-1000 nM) and miR-105 (0.01-1000 nM) with limit of detections as 0.02 and 0.05 pM, respectively. HCA-FRET has excellent selectivity for recognizing single base mismatches and interfering miRNAs, and shows good stability in different matrices. The FRET detection signal recorded from the serum samples of breast cancer patients is found much greater than that of the healthy controls, which effectively distinguishes breast cancer patients from the healthy persons.

The study reports a simple, unique HCA-FRET biosensor which undergo dual target miR-155 and miR-105 biomarkers detection enabling early breast cancer diagnosis. Compared with the traditional single marker detection or two signal output analysis methods, this strategy allows more accurate diagnosis of breast cancer patients from normal people. More importantly, through testing the clinical serum samples, we found that this method is expected to be used to distinguish breast cancer patients from normal people. Therefore, the proposed method has great potential applications for nucleic acid based clinical diagnostics.