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Loss of tumor-derived SMAD4 enhances primary tumor growth but not metastasis following BMP4 signalling

GSE199628 Homo sapiens (human) RNA-seq (gene expression) 16 samples Submitted 2024/04/30 Platform GPL18573
At a glance

RNA-seq (gene expression) · human · 16 samples

What it is
RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism
Homo sapiens (human)
Samples
16 samples
Data you can get
A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.
Paper
Loss of tumor-derived SMAD4 enhances primary tumor growth but not metastasis following BMP4 signallingCell communication and signaling : CCS · 2024
Summary (written by the submitters)
We have reported previously that in preclinical models, BMP4 is a potent inhibitor of breast cancer metastasis and that high BMP4 protein levels predict favourable patient outcome. Here, we investigated the requirement for functional SMAD4 in mediating the anti-metastatic response of BMP4, given reports of promotion of metastasis by BMP4 in cancers where SMAD4 is frequently deleted.
Read the full summary ↓
BMP4-induced inhibition of metastasis does not require functional SMAD4 in tumor cells. However, tumor cell intrinsic signalling using a constitutively active BMP receptor does require functional SMAD4 to suppress metastasis, thus implicating BMP4 mediated paracrine signalling as a contributor to the inhibition of metastasis.
Published in
Loss of tumor-derived SMAD4 enhances primary tumor growth but not metastasis following BMP4 signalling
Chi LH, Redfern AD, Roslan S et al. · Cell communication and signaling : CCS 2024 · PMID 38689334 · doi:10.1186/s12964-024-01559-0
This dataset
Download · which file do I need?

Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.

1 · Just have a look

Processed table

The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.

2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE199628_RAW.tar (it can run to tens of gigabytes).

3 · Start from the raw reads

Raw sequencing reads

The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.

What the files usually look like: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA820866 and SRA study SRP366379. Searching any of these in the dataset finder brings you back here.

Samples in this study

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+ 16 more — browse all 16 samples with per-sample file links →

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