Visualizing genomic characteristics across an RNA-Seq based reference landscape of normal and neoplastic brain

Area of Science:

• Neuroscience
• Genomics
• Bioinformatics

Background:

• Understanding the molecular differences between normal brain tissue and various brain tumors is crucial for developing targeted therapies.
• Existing large-scale datasets are often fragmented, hindering comprehensive comparative analysis.

Approach:

• Combined five large-scale public RNA-Seq datasets for adult gliomas, pediatric brain tumors, and normal brain samples.
• Applied batch effect correction and Uniform Manifold Approximation and Projection (UMAP) for data integration and visualization.
• Developed an interactive online tool, Oncoscape, for data exploration.

Key Points:

• Created a unified Brain-UMAP reference landscape integrating 702 adult gliomas, 802 pediatric tumors, and 1409 normal brain samples.
• Demonstrated distinct clustering of normal brain regions and tumor types based on gene expression.
• Facilitated comparative analysis of gene expression profiles, gene ontology patterns, and pathways across different brain conditions.

Conclusions:

• This meta-analysis provides a novel resource for comparing gene expression and pathways in adult gliomas, pediatric brain tumors, and normal brain.
• The open-source Oncoscape tool enables visualization of clinical metadata, gene expression, mutations, and copy number variations.
• This integrated landscape serves as a valuable platform for advancing brain tumor research and discovery.