← BioTransfer GEO Dataset Finder
GEO series

AR-independent prostate cancer is sustained through FGF signaling

GSE99381 Homo sapiens (human) RNA-seq (gene expression) 8 samples Submitted 2017/10/09 Platform GPL16791
At a glance

RNA-seq (gene expression) · human · 8 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)
Topic
Prostate cancerThis series is part of our curated briefing on it.
Samples
8 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.
Summary (written by the submitters)
Androgen receptor (AR) signaling is a distinctive feature of prostate cancer (PC) and represents the major therapeutic target for the treatment of metastatic disease. Though highly effective, AR antagonism has the potential to generate tumors that bypass a functional requirement for AR activity. We show here that a phenotypic shift has occurred in metastatic PCs with the emer-gence of a double-negative AR-null neuroendocrine-null phenotype that is notable for MAPK and FGF pathway activity.
Read the full summary ↓
To identify mechanisms capable of sustaining PC survival, we gener-ated a model system designated AR program-independent prostate cancer (APIPC) which re-sists AR-targeted therapeutics, lacks neuroendocrine features, expresses high levels of FGF8 and the ID1 oncogene, and activates MAPK signaling. Pharmacological blockade of MAPK or FGF signaling inhibited APIPC tumor growth, supporting FGF/MAPK as a therapeutic avenue for treating AR-null PC.
This dataset
Open GSE99381 in finder → Find more human RNA-seq datasets → Prostate cancer landscape →
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.

Series matrix ↓
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 GSE99381_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓
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.

Raw reads on ENA ↗

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 PRJNA388341 and SRA study SRP108222. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 29017058) ↗
Samples in this study

The sample list for this study is not cached yet. Press Load samples and it will be fetched from NCBI.

+ 8 more — browse all 8 samples with per-sample file links →

More Prostate cancer datasets

Search all human 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.