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  3. Machine learning-assisted SERS-based dual-aptamer biosensor for ultrasensitive clinical screening of breast cancer

Machine learning-assisted SERS-based dual-aptamer biosensor for ultrasensitive clinical screening of breast cancer

  • Spectrochim Acta A Mol Biomol Spectrosc. 2026 Jan 5;344(Pt 1):126640. doi: 10.1016/j.saa.2025.126640.
Haiqian Xia 1,  Lingzi Xiong 1,  Ruoyu Huang 2,  Ning Liu 3,  Muhammad Muhammad 4,  Jingfang Hong 5,  Qing Huang 6
Affiliations

Affiliations

  • 1 School of Nursing, Anhui Medical University, Hefei, Anhui 230032, China; CAS Key Laboratory of Ion Beam Bioengineering, Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui 230031, China.
  • 2 First Affiliated Hospital of Anhui Medical University, Hefei, Anhui 230032, China.
  • 3 CAS Key Laboratory of Ion Beam Bioengineering, Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui 230031, China; College of Materials and Chemistry, Anhui Agricultural University, Hefei, Anhui 230036, China.
  • 4 CAS Key Laboratory of Ion Beam Bioengineering, Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui 230031, China; CAS Key Laboratory of Atmospheric Optics, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei 230031, China. Electronic address: muhammad@iim.ac.cn.
  • 5 School of Nursing, Anhui Medical University, Hefei, Anhui 230032, China. Electronic address: hongjingfang@ahmu.edu.cn.
  • 6 School of Nursing, Anhui Medical University, Hefei, Anhui 230032, China; CAS Key Laboratory of Ion Beam Bioengineering, Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui 230031, China. Electronic address: huangq@ipp.ac.cn.
PMID: 40627939 DOI: 10.1016/j.saa.2025.126640
Abstract

Breast Cancer remains a global health challenge, with an increasing number of cases necessitating innovative approaches to streamline patient management prior to treatment. In this study, we present a comprehensive aptamer-involved surface-enhanced Raman spectroscopy (aptamer-SERS)-based protocol specifically designed for large-scale clinical screening of the circulating protein MUC1 overexpressed in the majority of breast Cancer cases. Central to our approach was a "sandwich" assay, where MUC1 was anchored between aptamer-functionalized bimetallic core-shell nanoparticles (NPs) and magnetic nanobeads. The biosensor was applied to monitor MUC1 in serum samples through SERS signals from the reporter molecule 4-ATP, exhibiting a strong linear correlation across a wide dynamic range and the lower the limit of detection (LOD) of 2.96 fg/mL. To support clinical decision-making, the protocol was integrated with a machine learning (ML)model for the classification of SERS signal patterns, demonstrating performance metrics of 96% accuracy and 93.7% specificity when applied to diverse serum samples. This integration enabled robust, non-invasive preclinical screening that informs therapeutic regulation and patient monitoring long before clinical symptoms present. By establishing a scalable framework for continuous monitoring of MUC1 levels across large populations, our study offered a forward-thinking tool, namely, dual-aptamer-SERS biosensor, that may optimize individualized treatment planning and improve overall clinical management strategies.

Keywords

Aptamer; Biosensor; Breast cancer; MUC1; Machine learning (ML); Surface-enhanced Raman spectroscopy (SERS).

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