GEO series

Population-associated molecular variation in histologically normal breast tissue is context-dependent and associated with distinct transcriptional states

GSE333857 Homo sapiens (human) RNA-seq (gene expression) 29 samples Submitted 2026/05/31 Platform GPL24676

At a glance

RNA-seq (gene expression) · human · 29 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: 29 samples: 29 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.

Summary (written by the submitters)

Population-associated molecular variation in breast tissue may contribute to differences in tissue biology and disease susceptibility, yet the extent to which such variation is shaped by underlying tissue states remains unclear. We performed integrated transcriptomic and lipidomic profiling of histologically normal breast tissue samples from African American and Caucasian White individuals.

Read the full summary ↓

Unsupervised transcriptomic analysis revealed two baseline transcriptional states, G1 and G2, that defined the primary axis of molecular variation and corresponded to epithelial-enriched and vascular-enriched tissue contexts. Global comparisons between African American and Caucasian White samples showed limited transcriptomic differences. However, when samples were stratified by baseline transcriptional state, 191 genes were differentially expressed within G1, with coordinated upregulation of extracellular matrix organization and proliferative/cytoskeletal programs in African American samples. No comparable population-associated differences were observed within G2. Lipidomic analyses showed partial but non-significant trends consistent with transcriptomic structure. These findings suggest that population-associated molecular differences in normal breast tissue are context-dependent and emerge within specific baseline transcriptional states.

This dataset

Open GSE333857 in finder → Find more human 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 GSE333857_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 PRJNA1472697 and SRA study SRP705000. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

29 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 29 sample titles: 29 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

GSM9776051 AA1 normal breast tissue RNA-seq
GSM9776052 AA2 normal breast tissue RNA-seq
GSM9776053 CW1 normal breast tissue RNA-seq
GSM9776054 AA3 normal breast tissue RNA-seq
GSM9776055 CW2 normal breast tissue RNA-seq
GSM9776056 CW3 normal breast tissue RNA-seq
GSM9776057 AA4 normal breast tissue RNA-seq
GSM9776058 CW4 normal breast tissue RNA-seq
GSM9776059 CW5 normal breast tissue RNA-seq
GSM9776060 CW6 normal breast tissue RNA-seq
GSM9776061 CW7 normal breast tissue RNA-seq
GSM9776062 AA5 normal breast tissue RNA-seq
GSM9776063 AA6 normal breast tissue RNA-seq
GSM9776064 CW8 normal breast tissue RNA-seq
GSM9776065 AA7 normal breast tissue RNA-seq
GSM9776066 CW9 normal breast tissue RNA-seq
GSM9776067 AA8 normal breast tissue RNA-seq
GSM9776068 AA9 normal breast tissue RNA-seq
GSM9776069 AA10 normal breast tissue RNA-seq
GSM9776070 CW10 normal breast tissue RNA-seq
GSM9776071 CW11 normal breast tissue RNA-seq
GSM9776072 AA11 normal breast tissue RNA-seq
GSM9776073 CW12 normal breast tissue RNA-seq
GSM9776074 AA12 normal breast tissue RNA-seq
GSM9776075 CW13 normal breast tissue RNA-seq
GSM9776076 CW14 normal breast tissue RNA-seq
GSM9776077 CW15 normal breast tissue RNA-seq
GSM9776078 CW16 normal breast tissue RNA-seq
GSM9776079 AA13 normal breast tissue RNA-seq

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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.