In a study that has the potential to add significant value to the landscape of early cancer-detection methods, Department of Oncology DPhil candidate Hira Javaid and colleagues have used machine learning to predict SETD2 mutation status with remarkable precision using DNA methylation. The study, published in BMC Cancer, sheds light on the critical role of SETD2-dependent H3 Lysine-36 trimethylation (H3K36me3) in DNA methylation dysregulation across multiple cancer types and opens the door to more effective diagnosis and prognosis for patients.
SETD2, a gene often found to be mutated in various cancer types, has been associated with the deposition of de-novo DNA methylation. Until now, the functional consequences of SETD2 loss and depletion on DNA methylation and tumorigenesis remained elusive. However, this new study has unveiled a ground-breaking connection.
In a pan-cancer analysis spanning 24 different cancer types, Hira and her colleagues observed that both mutations and reduced SETD2 expression were consistently linked to DNA methylation dysregulation in 21 of these cancer types. This finding suggests a broader role for SETD2 loss in not only tumorigenesis but also cancer aggressiveness through DNA methylation disturbances.