Single-atom Cu-attached MOFs as peroxide-like enzymes to construct dual-mode immunosensors for detection of breast cancer typing in serum

Molecular subtype of breast cancer has defined the treatment direction of breast cancer and guided the selection of drugs. Invasive tests such as tissue biopsy have the potential to promote the metastasis of cancer cells. Therefore, the continuous development of high-performance and low-cost in vitro diagnostic tools is crucial for the diagnosis and treatment of cancer prognosis. Liquid biopsy techniques provide noninvasive, real-time, dynamic, multicomponent, quantitative, and long-term observations at the cellular, genetic, and molecular levels in vivo. A Cu-Zr metal-organic framework(MOF) nanomases with monatomic Cu attachment has been synthesized by covalently attaching single atom Cu to the defect site of zirconia clusters in the UiO-66. The catalytic performance of Cu/UiO-66 as a peroxide-like enzyme was verified by enzyme kinetics calculation. Using o-phenylenediamine as substrate, a colorimetric fluorescence dual-mode immunosensor was constructed by introducing blue fluorescent carbon quantum dots (CDs) based on ultraviolet chromogenic reaction and fluorescence internal filtration effect. By magnetic adsorption separation, the background signal of the sensor is reduced and the sensitivity of the sensor is further improved. In 0.001–1 ng/mL detection range, detection of HER2/ER/PR/Ki-67, the result has obvious linear relationship. Among them, the colorimetric detection limit were 0.367/0.379/0.392/0.388 pg/mL; the ratio fluorescence detection limit can reach 0.3, 0.289, 0.257, 0.33 pg/mL, with high sensitivity. The proteins HER2, ER, PR and Ki-67 in serum related to human breast cancer typing were detected with high sensitivity. Finally, the actual serum samples were tested using sensors. According to the confirmed detection criteria, the classification of serum breast cancer was preliminarily explored with an accuracy of 87.5 %. The sensor has high catalytic performance and excellent real-time detection performance of biomarkers, and have practical application potential in human serum level detection. It provides a new method for the classification of breast cancer in serum.