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Identification of BET Inhibitors (BETi) Against Solitary Fibrous Tumor (SFT) Through High-Throughput Screening (HTS)

GSE322765 Homo sapiens (human) RNA-seq (gene expression) 17 samples Submitted 2026/03/08 Platform GPL34281
At a glance

RNA-seq (gene expression) · human · 17 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
17 samples: 6 conditions, mostly in triplicateGroups are read from the sample names, not from the paper. Check the list below.
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)
Solitary Fibrous Tumor (SFT) is a rare mesenchymal tumor driven by the NAB2-STAT6 fusion oncogene. To investigate transcriptional changes induced by BET inhibition in SFT models, we performed bulk RNA sequencing of INT-SFT and IEC139 cell lines treated with the BET inhibitors Mivebresib (ABBV-075) or BMS-986158 (50 nM for 24 h). Total RNA was extracted and libraries were sequenced on the Illumina NovaSeq X platform.
Read the full summary ↓
Gene-level expression was quantified using Salmon against the GRCh38 reference genome with GENCODE annotation. Differential expression and pathway enrichment analyses were performed to characterize transcriptional programs associated with BET inhibitor treatment.
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 GSE322765_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 PRJNA1431440 and SRA study SRP680681. Searching any of these in the dataset finder brings you back here.

Study design
6 conditions, mostly in triplicate
INT-SFT cells treated with vehicle control fo… ×3 INT-SFT cells treated with Mivebresib (50 nM)… ×3 IEC139 cells treated with vehicle control for… ×3 IEC139 cells treated with Mivebresib (50 nM) … ×3 IEC139 cells treated with BMS-986158 (50 nM) … ×3 INT-SFT cells treated with BMS-986158 (50 nM)… ×2

Supports a between-group comparison across 17 samples.

How we read this: 6 replicated groups read from 17 sample titles; they account for 17 of them. These groups are guessed from the sample names, not taken from the paper, so check them against the sample list below before relying on them.

Samples in this study
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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.