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MoS2-based sensor for the detection of miRNA in serum samples related with breast cancer


Early diagnosis of cancer is critical in treatment of the patients, which can reduce the risk of death. Breast cancer is one of the most common malignant tumors in women, and miR-21 as an important breast cancer biomarker can be helpful for the early diagnosis of breast cancer. In this work, we have developed an efficient, sensitive and specific fluorescence sensor based on novel nanomaterial molybdenum disulfide (MoS2) to detect miR-21. The novel nanomaterial MoS2 was introduced to the fluorescent dye-labeled DNA probe to fabricate the fluorescence sensor, and then the non-complementary miRNA, one-base mismatched miRNA, complementary miR-21 were introduced to the sensor for hybridization with DNA probe, respectively. Through monitoring the change of fluorescence signal before and after DNA-miRNA hybridization, the miR-21 could be detected. We found that the sensor could discriminate the complementary miR-21 from one-base mismatched miRNA and non-complementary miRNA successfully. Furthermore, the biosensor was able to detect miR-21 down to 500 pM, and the detection could be completed only in 40 mins. The novel MoS2 fluorescent sensor, with the advantages of fast analysis, high sensitivity and specificity, and low cost, is suitable for miR-21 detection which is of great importance for early diagnosis of breast cancer. What's more, the novel sensor with high sensitivity and selectivity was also applied to detect miR-21 in serum samples, making it a promising method for detecting real samples of patients with cancers. Thus the novel MoS2 fluorescent sensor shows huge potential for early diagnosis of cancer.

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Publication details

The article was received on 30 Sep 2017, accepted on 28 Nov 2017 and first published on 04 Dec 2017

Article type: Paper
DOI: 10.1039/C7AY02329D
Citation: Anal. Methods, 2017, Accepted Manuscript
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    MoS2-based sensor for the detection of miRNA in serum samples related with breast cancer

    B. Cai, S. Guo and Y. Li, Anal. Methods, 2017, Accepted Manuscript , DOI: 10.1039/C7AY02329D

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