The goal of the methylation classifier in brain tumor classification is to accurately classify tumors based on their methylation profiles. Accurate brain tumor diagnosis is the first step for healthcare professionals to predict tumor prognosis and establish personalized treatment plans for patients. The methylation classifier can be used to perform classification on tumor samples with diagnostic difficulties due to ambiguous histology or mismatch between histopathology and molecular signatures, i.e., not otherwise specified (NOS) cases or not elsewhere classified (NEC) cases, aiding in pathological decision-making. Here, the authors elucidate upon the application of a methylation classifier as a tool to mitigate the inherent complexities associated with the pathological evaluation of brain tumors, even when pathologists are experts in histopathological diagnosis and have access to enough molecular genetic information. Also, it should be emphasized that methylome cannot classify all types of brain tumors, and it often produces erroneous matches even with high matching scores, so, excessive trust is prohibited. The primary issue is the considerable difficulty in obtaining reference data regarding the methylation profile of each type of brain tumor. This challenge is further amplified when dealing with recently identified novel types or subtypes of brain tumors, as such data are not readily accessible through open databases or authors of publications. An additional obstacle arises from the fact that methylation classifiers are primarily research-based, leading to the unavailability of charging patients. It is important to note that the application of methylation classifiers may require specialized laboratory techniques and expertise in DNA methylation analysis.
Keywords: Bioinformatics; Brain Tumor; Classifications; Methylation.
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