A Machine Learning Analysis of TCGA Expression Data to Finding Signatures for “Normal-Like” IDH-WT Diffuse Gliomas with a Longer Survival

Classification of primary CNS tumours is currently achieved by complementing histologic analysis with molecular information, in accordance the WHO guidelines, and aims at providing accurate prognosis optimal patient management. cIMPACT-NOW update 3 now recommends grading diffuse IDH-wild type astrocytomas as grade IV glioblastomas if they bear one or more following alterations: EGFR amplification, TERT promoter mutation, whole-chromosome 7 gain combined chromosome 10 loss. In this reanalysis Cancer Genome Atlas (TCGA) glioma expression datasets, we identified 14 IDH-wt infiltrating astrocytic gliomas displaying a “normal-like (NL)” transcriptomic profile associated longer survival rate. Some these would be considered GBM-equivalents current diagnostic algorithm. A k-nearest neighbors model was used to identify 3-gene signatures able NL IDH-WT gliomas. Genes such C5AR1 (complement receptor) SLC32A1 (vesicular gamma-aminobutyric acid transporter), SMIM10L2A (long non-coding RNA) were overrepresented which validated further using Chinese Glioma Ivy Glioblastoma Atlases. They showed high discriminative power correlation survival. This finding could lead validation an immunohistochemical PCR test facilitate classification unclear histological grading. Furthermore, signaling pathways might represent novel treatment targets for aggressive tumours. LEARNING OBJECTIVES presentation will enable learner to: 1. Reconsider recent updates 2. Discuss advanced bioinformatics profiling brain cancer transcriptome.