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1.Effects of L013 treatment on gene expression in KYSE150 and KYSE510 cells. 2. 5 Patient-derived organoids RNA-seq data

GSE324866 Homo sapiens (human) RNA-seq (gene expression) 27 samples Submitted 2026/08/01 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 27 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
Esophageal cancerThis series is part of our curated briefing on it.
Samples
27 samples: 9 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)
Esophageal squamous cell carcinoma (ESCC) patients with the NRF2 oncogenic activation (NRFA) subtype have poor prognosis and limited response to conventional therapies due to excessive NRF2 accumulation. Here, through high-content screening of 725 CRBN ligand-based PROTACs, we identified L013 as an efficient NRF2 degrader. L013 reduces NRF2 in ESCC cells through dual mechanisms, including ubiquitination-mediated proteasomal degradation and suppression of NRF2 transcription.
Read the full summary ↓
Functional studies showed that L013 inhibits the proliferation and tumorigenic capacity of NRF2-hyperactivated ESCC cells. In preclinical models, L013 lowered NRF2 levels and suppressed tumor growth in ESCC or lung squamous cell carcinoma (LUSC) patient-derived organoids and xenograft models, while showing acceptable safety. These findings identify L013 as a promising therapeutic candidate for NRFA-subtype ESCC and support a new strategy for targeting NRF2-driven oncogenesis.
This dataset
Open GSE324866 in finder → Find more human RNA-seq datasets → Esophageal 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 GSE324866_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 PRJNA1438018 and SRA study SRP683982. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
9 conditions, mostly in triplicate
150DMSO ×3 150L013 ×3 510DMSO ×3 510L013 ×3 ESCC_1 ×3 ESCC_2 ×3 ESCC_3 ×3 LUSC_1 ×3 +1 more

Supports a between-group comparison across 27 samples.

How we read this: 9 replicated groups read from 27 sample titles; they account for 27 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
More Esophageal cancer datasets

Search all human RNA-seq datasets in GEO →

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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.