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Schlafen 5 (SLFN5) is an Intracellular Immune Checkpoint and controls Interferon (IFN) Responses in Pancreatic Ductal Adenocarcinoma (PDAC)

GSE282985 Homo sapiens (human) RNA-seq (gene expression) 14 samples Submitted 2026/01/14 Platform GPL18573
At a glance

RNA-seq (gene expression) · human · 14 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
Pancreatic cancerThis series is part of our curated briefing on it.
Samples
14 samples: 4 × PANC-1 cells, WT, UT vs 4 × PANC-1 cells, SLFN5 KO, IFN vs 3 × PANC-1 cells, WT, IFN vs 3 × PANC-1 cells, SLFN5 KO, UTGroups 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)
We provide evidence that human and murine SLFN5 proteins are modulators of Type I IFN responses and the immune response in pancreatic cancer. Blocking expression of Slfn5 in PDAC enhances IFN-responses, suppresses tumor growth, and prolongs survival in immunocompetent mice. Notably, immunophenotypic analysis reveals a reduction in tumor-associated myeloid cells (TAMCs) alongside an increase in tumor infiltrating effector cells in tumors.
Read the full summary ↓
These findings implicate SLFN5 acts as an intracellular immune checkpoint and identify it as a unique therapeutic target for the development of novel therapies for PDAC and possibly other malignancies
This dataset
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.

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 GSE282985_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA1191415 and SRA study SRP548051. Searching any of these in the dataset finder brings you back here.

Study design
4 × PANC-1 cells, WT, UT vs 4 × PANC-1 cells, SLFN5 KO, IFN vs 3 × PANC-1 cells, WT, IFN vs 3 × PANC-1 cells, SLFN5 KO, UT

Supports a between-group comparison across 14 samples.

How we read this: 4 replicated groups read from 14 sample titles; they account for 14 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
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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.