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Rapid microRNA detection method based on DNA strand displacement for ovarian cancer cells.
Sun, Gege; Chen, Congzhou; Li, Xin; Hong, Shangwei; Gu, Chuanqi; Shi, Xiaolong.
  • Sun G; Department of Gynecology 2, Renmin Hospital of Wuhan University, 430072, Wuhan, China.
  • Chen C; School of Computer Science, Beijing University of Technology, 100124, Beijing, China.
  • Li X; Department of Gynecology 2, Renmin Hospital of Wuhan University, 430072, Wuhan, China.
  • Hong S; Institute of Computing Science & Technology, Guangzhou University, 510006, Guangzhou, China.
  • Gu C; Department of Gynecology 2, Renmin Hospital of Wuhan University, 430072, Wuhan, China.
  • Shi X; Institute of Computing Science & Technology, Guangzhou University, 510006, Guangzhou, China.
J Cancer ; 14(5): 707-716, 2023.
Article in English | MEDLINE | ID: covidwho-2282948
ABSTRACT
The current cancer detection methods are heavily dependent on the component analysis of corresponding cancer antigens. There is a lack of effective and simple clinical methods of ovarian cancer screening, which hinders the early identification for ovarian cancer and its treatment. To develop a simple and rapid method for quantitative analysis of ovarian cancer, we developed a DNA strand displacement-based method and finished the rapid detection of miR-21 in ovarian cancer cells within 5 min by a one-step isothermal reaction. The fluorescence intensity trajectory had a good linear relationship with miR-21 concentrations in the range of 100 fM-100 nM, with a lower limit of 6.05 pM. This detection method is simple, faster, and accurate. Besides, it can be applied to detect the miRNA biomarkers of other cancers by changing the preset sequences of toehold.
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Full text: Available Collection: International databases Database: MEDLINE Type of study: Diagnostic study / Prognostic study Language: English Journal: J Cancer Year: 2023 Document Type: Article Affiliation country: Jca.81050

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Diagnostic study / Prognostic study Language: English Journal: J Cancer Year: 2023 Document Type: Article Affiliation country: Jca.81050