The translatome of glioblastoma

Abstract

Glioblastoma (GB), the most common and aggressive brain tumor, demonstrates intrinsic resistance to current therapies, resulting in poor clinical outcomes. Cancer progression can be partially attributed to the deregulation of protein translation mechanisms that drive cancer cell growth. In this study, we present the translatome landscape of GB as a valuable data resource. Eight patient‐derived GB sphere cultures (GSCs) were analyzed using ribosome profiling and messenger RNA (mRNA) sequencing. We investigated inter‐cell‐line differences through differential expression analysis at both the translatome and transcriptome levels. Translational changes post‐radiotherapy were assessed at 30 and 60 min. The translation of non‐coding RNAs (ncRNAs) was validated using in‐house and public mass spectrometry (MS) data, whereas RNA expression was confirmed by quantitative PCR (qPCR). Our findings demonstrate that ribosome sequencing provides more detailed information than MS or transcriptional analyses. Transcriptional similarities among GSCs correlate with translational similarities, aligning with previously defined subtypes such as proneural and mesenchymal. Additionally, we identified a broad spectrum of open reading frame types in both coding and non‐coding mRNA regions, including long non‐coding RNAs (lncRNAs) and pseudogenes undergoing active translation. Translation of ncRNAs into peptides was independently confirmed by in‐house data and external MS data. We also observed that translational regulation of histones (downregulated) and splicing factors (upregulated) occurs in response to radiotherapy. These data offer new insights into genome‐wide protein synthesis, identifying translationally regulated genes and alternative translation initiation sites in GB under normal and radiotherapeutic conditions, providing a rich resource for GB research. Further functional validation of differentially expressed genes after radiotherapy is needed. Understanding translational control in GB can reveal mechanistic insights and identify currently unknown biomarkers, ultimately enhancing the diagnosis and treatment of this aggressive brain cancer.