GEO series

Identification of genomic features that uniquely impact estrogen receptor alpha binding and its effects on gene expression in endometrial cancer [RNA-seq]

GSE334100 Homo sapiens (human) RNA-seq (gene expression) 16 samples Submitted 2026/06/03 Platform GPL34281

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)
Topic: Endometrial cancerThis series is part of our curated briefing on it.
Samples: 16 samples: 8 conditions, mostly in duplicateGroups 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)

Estrogen receptor alpha (ER) is an established oncogenic transcription factor in breast and endometrial cancer; however, more is known about the mechanisms controlling ER behavior in breast cancer, and therapies targeting ER have been much more successful in breast cancer. To address this disparity, we seek to identify genomic features that control ER in endometrial cancer and determine to what extent these factors differ from those in breast cancer.

Read the full summary ↓

We focus on the locations of estrogen response elements (EREs), ER’s preferred DNA binding motif, throughout the human genome. To identify factors that predict ER genomic binding and effects on target gene expression, we apply machine learning to genomic data for each ERE in Ishikawa cells (ER+ endometrial cancer) and T-47D cells (ER+ breast cancer). Many of these factors, such as chromatin accessibility and histone modifications, are predictive of ER activity in both cell lines. However, the transcription factors that predict ER activity are found to be cell type-specific, including FOXA1 and GATA3 in T-47D cells, and ETV4 and SOX17 in Ishikawa cells. In addition, the features that predict ER binding and effects on gene expression differ, with transcription at EREs in the absence of estrogen being predictive of regulatory activity. To verify our findings in the Ishikawa cells, we perform a CRISPR knockout screen, which confirms the discovery that SOX17 controls ER activity in endometrial cancer cells. These results identify important genomic features of ER binding and regulatory activity and how these features differ between endometrial cancer and breast cancer cells.

This dataset

Open GSE334100 in finder → Find more human RNA-seq datasets → Endometrial 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 GSE334100_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 PRJNA1473731 and SRA study SRP706129. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

8 conditions, mostly in duplicate

SOX17KO_cloneA3_E2 ×2 SOX17KO_cloneA3_DMSO ×2 SOX17KO_cloneE3_E2 ×2 SOX17KO_cloneE3_DMSO ×2 Cnt_cloneE5_E2 ×2 Cnt_cloneE5_DMSO ×2 Cnt_cloneF3_E2 ×2 Cnt_cloneF3_DMSO ×2

Supports a between-group comparison across 16 samples.

How we read this: 8 replicated groups read from 16 sample titles; they account for 16 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

GSM9780717 SOX17KO_cloneA3_E2_Rep1 SOX17KO_cloneA3_E2
GSM9780718 SOX17KO_cloneA3_E2_Rep2 SOX17KO_cloneA3_E2
GSM9780719 SOX17KO_cloneA3_DMSO_Rep1 SOX17KO_cloneA3_DMSO
GSM9780720 SOX17KO_cloneA3_DMSO_Rep2 SOX17KO_cloneA3_DMSO
GSM9780721 SOX17KO_cloneE3_E2_Rep1 SOX17KO_cloneE3_E2
GSM9780722 SOX17KO_cloneE3_E2_Rep2 SOX17KO_cloneE3_E2
GSM9780723 SOX17KO_cloneE3_DMSO_Rep1 SOX17KO_cloneE3_DMSO
GSM9780724 SOX17KO_cloneE3_DMSO_Rep2 SOX17KO_cloneE3_DMSO
GSM9780725 Cnt_cloneE5_E2_Rep1 Cnt_cloneE5_E2
GSM9780726 Cnt_cloneE5_E2_Rep2 Cnt_cloneE5_E2
GSM9780727 Cnt_cloneE5_DMSO_Rep1 Cnt_cloneE5_DMSO
GSM9780728 Cnt_cloneE5_DMSO_Rep2 Cnt_cloneE5_DMSO
GSM9780729 Cnt_cloneF3_E2_Rep1 Cnt_cloneF3_E2
GSM9780730 Cnt_cloneF3_E2_Rep2 Cnt_cloneF3_E2
GSM9780731 Cnt_cloneF3_DMSO_Rep1 Cnt_cloneF3_DMSO
GSM9780732 Cnt_cloneF3_DMSO_Rep2 Cnt_cloneF3_DMSO

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GSE307568 Patient-derived organoid – immune cell co-culture platform for therapeutics discovery in high-grade endometrial cancer 189 samples same disease briefing · shares endometrial
GSE212217 Distinct Mechanisms of Mismatch-Repair Deficiency Delineate Two Modes of Response to Anti–PD-1 Immunotherapy in Endometrial Carcinoma 104 samples same disease briefing · shares endometrial
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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.