Temozolomide-Induced RNA Interactome Uncovers Novel LncRNA Regulatory Loops in Glioblastoma.
Sabrina FritahArnaud MullerWei JiangRamkrishna MitraMohamad SarminiMonika DieterleAnna GolebiewskaTao YeEric Van DyckChristel Herold-MendeZhong-Ming ZhaoFrancisco AzuajeSimone P NiclouPublished in: Cancers (2020)
Resistance to chemotherapy by temozolomide (TMZ) is a major cause of glioblastoma (GBM) recurrence. So far, attempts to characterize factors that contribute to TMZ sensitivity have largely focused on protein-coding genes, and failed to provide effective therapeutic targets. Long noncoding RNAs (lncRNAs) are essential regulators of epigenetic-driven cell diversification, yet, their contribution to the transcriptional response to drugs is less understood. Here, we performed RNA-seq and small RNA-seq to provide a comprehensive map of transcriptome regulation upon TMZ in patient-derived GBM stem-like cells displaying different drug sensitivity. In a search for regulatory mechanisms, we integrated thousands of molecular associations stored in public databases to generate a background "RNA interactome". Our systems-level analysis uncovered a coordinated program of TMZ response reflected by regulatory circuits that involve transcription factors, mRNAs, miRNAs, and lncRNAs. We discovered 22 lncRNAs involved in regulatory loops and/or with functional relevance in drug response and prognostic value in gliomas. Thus, the investigation of TMZ-induced gene networks highlights novel RNA-based predictors of chemosensitivity in GBM. The computational modeling used to identify regulatory circuits underlying drug response and prioritizing gene candidates for functional validation is applicable to other datasets.
Keyphrases
- rna seq
- transcription factor
- genome wide identification
- single cell
- genome wide analysis
- genome wide
- drug induced
- dna binding
- high glucose
- gene expression
- diabetic rats
- dna methylation
- healthcare
- copy number
- adverse drug
- nucleic acid
- machine learning
- quality improvement
- high grade
- locally advanced
- protein protein
- rectal cancer
- data analysis
- ionic liquid
- amino acid