Pan-cancer analysis of mRNA stability for decoding tumour post-transcriptional programs


Gabrielle Perron, Pouria Jandaghi, Elham Moslemi, Tamiko Nishimura, Maryam Rajaee, Rached Alkallas, Tianyuan Lu, Yasser Riazalhosseini, Hamed S. Najafabadi

ABSTRACT

Measuring mRNA decay in tumours is a prohibitive challenge, limiting our ability to map the post-transcriptional programs of cancer. Here, using a statistical framework to decouple transcriptional and post-transcriptional effects in RNA-seq data, we uncover the mRNA stability changes that accompany tumour development and progression. Analysis of 7760 samples across 18 cancer types suggests that mRNA stability changes are ~30% as frequent as transcriptional events, highlighting their widespread role in shaping the tumour transcriptome. Dysregulation of programs associated with >80 RNA-binding proteins (RBPs) and microRNAs (miRNAs) drive these changes, including multi-cancer inactivation of RBFOX and miR-29 families. Phenotypic activation or inhibition of RBFOX1 highlights its role in calcium signaling dysregulation, while modulation of miR-29 shows its impact on extracellular matrix organization and stemness genes. Overall, our study underlines the integral role of mRNA stability in shaping the cancer transcriptome, and provides a resource for systematic interrogation of cancer-associated stability pathways.

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SUPPLEMENTARY DATA FILES

➤ Differential mRNA stability analysis across TCGA cancers

The following files include exon/intron read counts (.tar.gz) across TCGA samples, along with the DiffRAC estimates of differntial mRNA stability and transcription between tumour and normal tissues (.rds).  For each cancer type, differential gene expression analysis between tumour and normal is also included (.rds).

–––– BLCA ––––

- Intron/exon read counts
- Differential stability
- Differential transcription
- Differential expression

–––– BRCA ––––

- Intron/exon read counts
- Differential stability
- Differential transcription
- Differential expression

–––– CHOL ––––

- Intron/exon read counts
- Differential stability
- Differential transcription
- Differential expression

–––– COAD ––––

- Intron/exon read counts
- Differential stability
- Differential transcription
- Differential expression

–––– ESCA ––––

- Intron/exon read counts
- Differential stability
- Differential transcription
- Differential expression

–––– GBM ––––

- Intron/exon read counts
- Differential stability
- Differential transcription
- Differential expression

–––– HNSC ––––

- Intron/exon read counts
- Differential stability
- Differential transcription
- Differential expression

–––– KICH ––––

- Intron/exon read counts
- Differential stability
- Differential transcription
- Differential expression

–––– KIRC ––––

- Intron/exon read counts
- Differential stability
- Differential transcription
- Differential expression

–––– KIRP ––––

- Intron/exon read counts
- Differential stability
- Differential transcription
- Differential expression

–––– LIHC ––––

- Intron/exon read counts
- Differential stability
- Differential transcription
- Differential expression

–––– LUAD ––––

- Intron/exon read counts
- Differential stability
- Differential transcription
- Differential expression

–––– LUSC ––––

- Intron/exon read counts
- Differential stability
- Differential transcription
- Differential expression

–––– PRAD ––––

- Intron/exon read counts
- Differential stability
- Differential transcription
- Differential expression

–––– READ ––––

- Intron/exon read counts
- Differential stability
- Differential transcription
- Differential expression

–––– STAD ––––

- Intron/exon read counts
- Differential stability
- Differential transcription
- Differential expression

–––– THCA ––––

- Intron/exon read counts
- Differential stability
- Differential transcription
- Differential expression

–––– UCEC ––––

- Intron/exon read counts
- Differential stability
- Differential transcription
- Differential expression

➤ Differential mRNA stability analysis between MDA-MD-231 and MDA-LM2 cells

The following files include gene-level read counts for timeseries measurements of 4sU-labeled RNA in MDA-MD-231 and MDA-LM2 cells (four time points with four replicates per cell line). The differential stability measurements and associated statistics are also provided.

–– Sample metadata ––

Sample name, associated count file, time point, and cell line (.csv)

–––– Read counts ––––

Gene-level read counts (.tar.gz)

–– Differential stability ––

DESeq2 estimates (.csv)

➤ MiR-29 mimic and inhibitor expression in the clear cell renal cell carcinoma cell lines 786-O, A-498 and ACHN

RNA-seq data available via GEO (accession GSE145088).


RBFOX1 overexpression in the human glioblastoma cell line A172

RNA-seq data available via GEO (accession GSE201639).

ADDITIONAL LINKS

  • The R functions and example datasets for differential mRNA stability analysis are available as an open-source package, called DiffRAC, from GitHub.