Classifying Medulloblastoma Subgroups Based on Small, Clinically Achievable Gene Sets.

ABSTRACT: As treatment protocols for medulloblastoma (MB) are becoming subgroup-specific, means for reliably distinguishing between its subgroups are a timely need. Currently available methods include immunohistochemical stains, which are subjective and often inconclusive, and molecular techniques-e.g., NanoString, microarrays, or DNA methylation assays-which are time-consuming, expensive and not widely available. Quantitative PCR (qPCR) provides a good alternative for these methods, but the current NanoString panel which includes 22 genes is impractical for qPCR. Here, we applied machine-learning-based classifiers to extract reliable, concise gene sets for distinguishing between the four MB subgroups, and we compared the accuracy of these gene sets to that of the known NanoString 22-gene set. We va

SUBMITTER: Gershanov S 

PROVIDER: S-EPMC8223061 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature