4.7 Article

Diagnostic potential of nanoparticle aided assays for MUC16 and MUC1 glycovariants in ovarian cancer

期刊

INTERNATIONAL JOURNAL OF CANCER
卷 151, 期 7, 页码 1175-1184

出版社

WILEY
DOI: 10.1002/ijc.34111

关键词

diagnosis; epithelial ovarian cancer; europium nanoparticle; mucins; STn

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资金

  1. ALF-VGR Region, Sweden [ALFGBG-721051, ALFGBG932583]
  2. Jane ja Aatos Erkon Saatio
  3. Nordic Cancer Union, Denmark [194914]
  4. Swedish Cancer Foundation [CAN-2018/834]

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Our study discovered and evaluated nanoparticle aided sensitive assays for glycovariants of MUC16 and MUC1 in ovarian cyst fluids and serum samples. The glycovariants showed improved separation capability compared to conventional assays in differentiating ovarian carcinoma from controls. The STn glycovariants in serum performed the best, especially in postmenopausal and early-stage disease.
Our study reports the discovery and evaluation of nanoparticle aided sensitive assays for glycovariants of MUC16 and MUC1 in a unique collection of paired ovarian cyst fluids and serum samples obtained at or prior to surgery for ovarian carcinoma suspicion. Selected glycovariants and the immunoassays for CA125, CA15-3 and HE4 were compared and validated in 347 cyst fluid and serum samples. Whereas CA125 and CA15-3 performed poorly in cyst fluid to separate carcinoma and controls, four glycovariants including MUC16(MGL), MUC16(STn), MUC1(STn) and MUC1(Tn) provided highly improved separations. In serum, the two STn glycovariants outperformed conventional CA125, CA15-3 and HE4 assays in all subcategories analyzed with main benefits obtained at high specificities and at postmenopausal and early-stage disease. Serum MUC16(STn) performed best at high specificity (90%-99%), but sensitivity was also improved by the other glycovariants and CA15-3. The highly improved specificity, excellent analytical sensitivity and robustness of the nanoparticle assisted glycovariant assays carry great promise for improved identification and early detection of ovarian carcinoma in routine differential diagnostics.

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