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UHRF1 drives subtype-independent aggressiveness and immune evasion in small cell lung cancer through GATA2-ST6GALNAC5 and PRC2 interactions

GSE303043 Mus musculus (mouse) RNA-seq (gene expression) 30 samples Submitted 2026/02/26 Platform GPL24676Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 30 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
Mus musculus (mouse)
Topic
Small cell lung cancerThis series is part of our curated briefing on it.
Samples
30 samples: 6 conditions, mostly with about 5 replicates eachGroups 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
UHRF1 drives subtype-independent aggressiveness and immune evasion in small cell lung cancer through PRC2 interactionsiScience · 2026
Summary (written by the submitters)
Small cell lung cancer (SCLC) is an aggressive neuroendocrine carcinoma characterized by early metastasis, immune evasion, and limited therapeutic progress. While its genomic landscape is well defined, the epigenetic programs sustaining its lineage fidelity, metastatic potential, and immune evasion remain incompletely understood. We used inducible CRISPR-Cas9 knockout models, transcriptomic and epigenetic profiling, immune-competent allografts, and genetically engineered mouse models (GEMMs) to investigate the role of the chromatin regulator UHRF1 in SCLC progression.
Read the full summary ↓
We identify the chromatin remodeler UHRF1 as a subtype-independent oncogenic driver in SCLC, with high expression correlating with poor survival. UHRF1 loss impaired proliferation, invasion, and metastasis across ASCL1-, NEUROD1-, and POU2F3-driven subtypes. Mechanistically, UHRF1 maintained neuroendocrine lineage identity and suppressed immune gene expression via DNA methylation and interaction with PRC2. UHRF1-deficient tumors displayed increased infiltration of CD8⁺ T cells, monocytes, neutrophils, and dendritic cells, along with a higher CD8⁺/Treg ratio. Secretome profiling revealed upregulation of chemokines, linking UHRF1 loss to active recruitment of immune effector cells. In parallel, UHRF1 regulated the pro-metastatic sialyltransferase ST6GALNAC5 via the transcription factor GATA2. UHRF1 loss downregulated GATA2 and ST6GALNAC5, impairing tumor dissemination. Lastly, UHRF1 depletion increased expression of cancer/testis antigens such as MAGE-A4, without altering DLL3 levels, suggesting compatibility with both antigen-specific and DLL3-targeted immunotherapies. In conclusion, UHRF1 is a master regulator of tumor progression in SCLC, promoting lineage stability, immune evasion, and metastatic spread. Its inhibition reprograms tumors toward an inflamed, less metastatic, and more immunogenic state. These findings establish UHRF1 as a compelling therapeutic target for overcoming the epigenetic and immune resistance barriers in small cell lung cancer.
Published in
UHRF1 drives subtype-independent aggressiveness and immune evasion in small cell lung cancer through PRC2 interactions
Gu Y, De La Torre JJ, Fazeli Y et al. · iScience 2026 · PMID 42006373 · doi:10.1016/j.isci.2026.115475
This dataset
Open GSE303043 in finder → Find more RNA-seq datasets → Small cell lung 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 GSE303043_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 PRJNA1293407 and SRA study SRP601835. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42006373) ↗
Study design
6 conditions, mostly with about 5 replicates each
NCI-H526 VC 5wk tumo ×5 NCI-H526 UHRF1 KO 5wk tumo ×5 NCI-H446 VC 5wk tumo ×5 NCI-H446 UHRF1 KO 5wk tumo ×5 TR DKO tumo ×5 TRU TKO tumo ×5

Supports a between-group comparison across 30 samples.

How we read this: 6 replicated groups read from 30 sample titles; they account for 30 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 Small cell lung cancer datasets

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