A Novel Machine Learning-enhanced Microfluidic CircRNAs Detection Platform for Breast Cancer Precision Diagnosis

The discovery and favourable detection of breast cancer biomarkers are significant for cancer diagnosis. Here, we show that hsa_circ_044235 and hsa_circ_000250 are effective biomarkers for breast cancer diagnosis. Moreover, we present an integrated electrochemical microfluidic circRNAs detection platform (ECMCDP) that combined gold platinum nanoparticles (AuPts)-modified screen-printed electrodes (SPEs), catalytic hairpin assembly (CHA), electrochemical microfluidic chip and a customed low-power electronic system for simultaneous detection of two breast cancer-associated circRNAs. The limit of detection (LOD) were 0.12 fM and 0.1 fM, and the diagnostic accuracy were 92.50% and 88.75% in clinical blood samples, respectively. The platform was validated using paired pre-/post-operative blood and tissue samples. Combined with five machine learning-based diagnostic models, the ensemble diagnosis model achieved a high accuracy of 93.75%. This work aims to identify novel breast cancer biomarkers and establish an innovative circRNAs detection platform to improve breast cancer diagnosis and support clinical prognosis assessment.