Abstract
Current diagnostic algorithms are insufficient for the optimal clinical and therapeutic management of cutaneous spitzoid tumors, particularly atypical spitzoid tumors (AST). Therefore, it is crucial to identify new markers that allow for reliable and reproducible diagnostic assessment and can also be used as a predictive tool to anticipate the individual malignant potential of each patient, leading to tailored individual therapy. Using Reduced Representation Bisulfite Sequencing (RRBS), we studied genome-wide methylation profiles of a series of Spitz nevi (SN), spitzoid melanoma (SM), and AST. We established a diagnostic algorithm based on the methylation status of seven cg sites located in TETK4P2 (Tektin 4 Pseudogene 2), MYO1D (Myosin ID), and PMF1-BGLAP (PMF1-BGLAP Readthrough), which allows the distinction between SN and SM but is also capable of subclassifying AST according to their similarity to the methylation levels of Spitz nevi or spitzoid melanoma. Thus, our epigenetic algorithm can predict the risk level of AST and predict its potential clinical outcomes.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
