González-Muñoz JF, Sánchez-Sendra B, Monteagudo C. Diagnostic Algorithm to Subclassify Atypical Spitzoid Tumors in Low and High Risk According to Their Methylation Status.
Int J Mol Sci 2023;
25:318. [PMID:
38203489 PMCID:
PMC10779069 DOI:
10.3390/ijms25010318]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 12/20/2023] [Accepted: 12/21/2023] [Indexed: 01/12/2024] Open
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.
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