A three-gene DNA methylation biomarker accurately classifies early stage prostate cancer

Palak G. Patel, Thomas Wessel, Atsunari Kawashima, John B.A. Okello, Tamara Jamaspishvili, Karl Philippe Guérard, Laura Lee, Anna Ying Wah Lee, Nathan E. How, Dan Dion, Eleonora Scarlata, Chelsea L. Jackson, Suzanne Boursalie, Tanya Sack, Rachel Dunn, Madeleine Moussa, Karen Mackie, Audrey Ellis, Elizabeth Marra, Joseph ChinKhurram Siddiqui, Khalil Hetou, Lee Anne Pickard, Vinolia Arthur-Hayward, Glenn Bauman, Simone Chevalier, Fadi Brimo, Paul C. Boutros, Jacques Lapointe PhD, John M.S. Bartlett, Robert J. Gooding, David M. Berman*

*المؤلف المقابل لهذا العمل

نتاج البحث: المساهمة في مجلةArticleمراجعة النظراء

20 اقتباسات (Scopus)


Background: We identify and validate accurate diagnostic biomarkers for prostate cancer through a systematic evaluation of DNA methylation alterations. Materials and methods: We assembled three early prostate cancer cohorts (total patients = 699) from which we collected and processed over 1300 prostatectomy tissue samples for DNA extraction. Using real-time methylation-specific PCR, we measured normalized methylation levels at 15 frequently methylated loci. After partitioning sample sets into independent training and validation cohorts, classifiers were developed using logistic regression, analyzed, and validated. Results: In the training dataset, DNA methylation levels at 7 of 15 genomic loci (glutathione S-transferase Pi 1 [GSTP1], CCDC181, hyaluronan, and proteoglycan link protein 3 [HAPLN3], GSTM2, growth arrest-specific 6 [GAS6], RASSF1, and APC) showed large differences between cancer and benign samples. The best binary classifier was the GAS6/GSTP1/HAPLN3 logistic regression model, with an area under these curves of 0.97, which showed a sensitivity of 94%, and a specificity of 93% after external validation. Conclusion: We created and validated a multigene model for the classification of benign and malignant prostate tissue. With false positive and negative rates below 7%, this three-gene biomarker represents a promising basis for more accurate prostate cancer diagnosis.

اللغة الأصليةEnglish
الصفحات (من إلى)1705-1714
عدد الصفحات10
مستوى الصوت79
رقم الإصدار14
المعرِّفات الرقمية للأشياء
حالة النشرPublished - أكتوبر 2019

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