← BioTransfer GEO Dataset Finder
GEO series

Retinoic Acid Signaling Drives Differentiation towards Enterocytes in Colorectal Cancer

GSE163142 Mus musculus; Homo sapiens Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing 58 samples Submitted 2021/11/26 Platform GPL18573Platform GPL19057
Summary
Retinoic Acid (RA) signaling is an important and conserved pathway that regulated cellular proliferation and differentiation. Furthermore, perturbed RA signaling is implicated in cancer initiation and progression. However, the mechanisms by which RA signaling contributes homeostasis malignant transformation and disease progression in the intestine, remain incompletely understood. Here, we show that activation of the Retinoic Acid Receptor and the Retinoid X Receptor results in enhanced transcription of enterocyte-specific genes in mouse small intestinal organoids. Conversely, inhibition of this pathway results in reduced expression of genes associated with the absorptive lineage. Strikingly, this latter effect is conserved in a human organoid model for colorectal cancer (CRC) progression. We further show that the epigenome in metastatic organoids is less permissive for RXR binding compared to pre-metastatic organoids, resulting in reduced sensitivity for RA signaling perturbations. Finally, we show that reduced RXR target gene expression correlates with worse CRC prognosis, implying RA signaling as a putative therapeutic strategy in CRC.
This dataset
Download

Direct links to NCBI, no account and no request form: the whole study as GSE163142_RAW.tar, processed values as the series matrix, the supplementary file directory, and per-sample supplementary files for any of the 58 samples. Raw sequencing reads are also available from ENA.

Also filed as BioProject PRJNA685100 and SRA study SRP297871. Searching any of these in the dataset finder brings you back here.

Samples in this study

The sample list for this study is not cached yet. Press Sort into groups and it will be fetched from NCBI.

+ 58 more — browse all 58 samples with per-sample file links →

Similar datasets

Search all RNA-seq datasets in GEO →

Share this dataset

Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.