1.
Ultrasensitive Nanopore Sensing of Mucin 1 and Circulating Tumor Cells in Whole Blood of Breast Cancer Patients by Analyte-Triggered Triplex-DNA Release.
Sun, K, Chen, P, Yan, S, Yuan, W, Wang, Y, Li, X, Dou, L, Zhao, C, Zhang, J, Wang, Q, et al
ACS applied materials & interfaces. 2021;(18):21030-21039
Abstract
The characterization of circulating tumor cells (CTCs) by liquid biopsy has a great potential for precision medicine in oncology. Here, a universal and tandem logic-based strategy is developed by combining multiple nanomaterials and nanopore sensing for the determination of mucin 1 protein (MUC1) and breast cancer CTCs in real samples. The strategy consists of analyte-triggered signal conversion, cascaded amplification via nanomaterials including copper sulfide nanoparticles (CuS NPs), silver nanoparticles (Ag NPs), and biomaterials including DNA hydrogel and DNAzyme, and single-molecule-level detection by nanopore sensing. The amplification of the non-DNA nanomaterial gives this method considerable stability, significantly lowers the limit of detection (LOD), and enhances the anti-interference performance for complicated samples. As a result, the ultrasensitive detection of MUC1 could be achieved in the range of 0.0005-0.5 pg/mL, with an LOD of 0.1 fg/mL. Moreover, we further tested MUC1 as a biomarker for the clinical diagnosis of breast cancer CTCs under double-blind conditions on the basis of this strategy, and MCF-7 cells could be accurately detected in the range from 5 to 2000 cells/mL, with an LOD of 2 cells/mL within 6 h. The detection results of the 19 clinical samples were highly consistent with those of the clinical pathological sections, nuclear magnetic resonance imaging, and color ultrasound. These results demonstrate the validity and reliability of our method and further proved the feasibility of MUC1 as a clinical diagnostic biomarker for CTCs.
2.
Do genetic polymorphisms of the vitamin D receptor contribute to breast/ovarian cancer? A systematic review and network meta-analysis.
Li, J, Li, B, Jiang, Q, Zhang, Y, Liu, A, Wang, H, Zhang, J, Qin, Q, Hong, Z, Li, BA
Gene. 2018;:211-227
Abstract
BACKGROUND To identify the most suitable genetic model for detecting the risk of breast cancer (BC)/ovarian cancer (OC) in specific populations. METHODS Databases were searched for related studies published up to October 2017. First, VDR genetic polymorphisms were compared in patients with and without cancer. Second, a network meta-analysis was used to reveal the relation between VDR genetic polymorphisms with disease outcomes. Subgroup analyses and a meta-regression were performed according to cancer types, ethnicity and genotypic method. The study is registered in PROSPERO with an ID: CRD42017075505. RESULTS Forty-five studies were eligible, which included 65,754 patients and 55 clinical analyses. Of genetic models, results suggested that the recessive model with the CDX2 polymorphism predicted the risk of BC in all cases. The recessive polymorphism model with the rs2228570 (FokI) polymorphism seemed to the best predictor of BC in Caucasian patients, whereas the homozygote model with the CDX2 polymorphism appeared to best predict BC in African-American patients. The homozygote model with the rs2228570 (FokI) polymorphism model appeared to detect the risk of OC in all cases, whereas the heterozygote model with the rs1544410 (BsmI) polymorphism seemed to detect the risk of OC in Caucasian patients. CONCLUSIONS By detecting the risk of BC, the recessive model with the rs2228570 (FokI) polymorphism is likely the best genetic model in Caucasian patients, and the homozygote model with the CDX2 polymorphism appears to be best genetic model in African-American patients. Moreover, for detecting clinical risk of OC, heterozygote models with the rs1544410 (BsmI) polymorphism is likely the best genetic model for detecting the risk of OC in Caucasian patients.