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Transcriptomic analysis of BPA alternative chemicals in primary human mammary epithelial cells

GSE304327 synthetic construct; Homo sapiens RNA-seq (gene expression) 666 samples Submitted 2026/01/06 Platform GPL19424Platform GPL18573
At a glance

RNA-seq (gene expression) · synthetic construct; Homo sapiens · 666 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
synthetic construct; Homo sapiens
Samples
666 samples: 666 samples, each with a distinct title — no replicated groupsGroups 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.
Paper
Transcriptomic analysis of BPA alternative chemicals in primary human mammary epithelial cellsToxicology · 2026
Summary (written by the submitters)
Some everyday consumer products contain endocrine disruptors like bisphenol A (BPA) and its replacements. To date, most in vitro chemical screening to evaluate these compounds has been accomplished using immortalized cell lines, which differ significantly from human tissues. Our goal was to test BPA and select alternatives previously screened in breast cancer cells for toxicological potency and mechanism of action in human mammary epithelial cells (HMECs).
Read the full summary ↓
HMECs from three human donors were exposed to BPA and four alternative chemicals (in concentration response format from 0.001 – 50 µM) for 48 h and global transcriptomic changes were quantified. Transcriptomic biomarker analysis was employed to explore chemically induced estrogen receptor alpha (ERα) activation and alterations in stress response pathways. Benchmark concentration (BMC) analysis was applied to gene expression data to derive transcriptomic points of departure (tPODs) to compare chemicals for potency. Pathway and upstream regulator analysis was applied among the genes fitting BMCs. All chemicals had tPODs within an order of magnitude of each other. Bisphenol AF (BPAF) was the most potent, followed by tetramethyl bisphenol F (TMBPF), bisphenol C (BPC), 4,4’-bisphenol S (BPS), and BPA. None of the chemicals activated the ERα active biomarker. Some stress response biomarkers were activated at high exposure concentrations. Genes fitting BMCs clustered chemicals into two groups, with one group (BPAF and TMBPF) primarily inhibiting expression patterns and the other (BPC, BPS, and BPA) mostly activating. These data suggest that the BPA alternatives tested have similar toxicological potencies in HMECs and oppositely enrich cancer related gene sets.
Published in
Transcriptomic analysis of BPA alternative chemicals in primary human mammary epithelial cells
Matteo G, Rowan-Carroll A, Bradford LM et al. · Toxicology 2026 · PMID 41429237 · doi:10.1016/j.tox.2025.154375
This dataset
Open GSE304327 in finder → Find more RNA-seq datasets →
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 GSE304327_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 PRJNA1300393 and SRA study SRP605868. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41429237) ↗
Study design
666 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from the first 400 of 666 sample titles: 400 distinct titles with little repetition. 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

+ 626 more — browse all 666 samples with per-sample file links →

Datasets on a similar topic

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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.