An RNA seq-based reference landscape of human normal and neoplastic brain.
Sonali AroraFrank SzulzewskyMatt JensenNicholas NuechterleinSiobhan S PattwellEric C HollandPublished in: bioRxiv : the preprint server for biology (2023)
In order to better understand the relationship between normal and neoplastic brain, we combined five publicly available large-scale datasets, correcting for batch effects and applying Uniform Manifold Approximation and Projection (UMAP) to RNA-seq data. We assembled a reference Brain-UMAP including 702 adult gliomas, 802 pediatric tumors and 1409 healthy normal brain samples, which can be utilized to investigate the wealth of information obtained from combining several publicly available datasets to study a single organ site. Normal brain regions and tumor types create distinct clusters and because the landscape is generated by RNA seq, comparative gene expression profiles and gene ontology patterns are readily evident. To our knowledge, this is the first meta-analysis that allows for comparison of gene expression and pathways of interest across adult gliomas, pediatric brain tumors, and normal brain regions. We provide access to this resource via the open source, interactive online tool Oncoscape, where the scientific community can readily visualize clinical metadata, gene expression patterns, gene fusions, mutations, and copy number patterns for individual genes and pathway over this reference landscape.
Keyphrases
- rna seq
- single cell
- copy number
- gene expression
- resting state
- white matter
- genome wide
- systematic review
- dna methylation
- mitochondrial dna
- healthcare
- cerebral ischemia
- high grade
- genome wide identification
- magnetic resonance imaging
- endothelial cells
- young adults
- randomized controlled trial
- brain injury
- subarachnoid hemorrhage
- social media
- deep learning
- transcription factor
- meta analyses
- clinical evaluation