← BioTransfer GEO Dataset Finder
GEO series

A macrophage-predominant, immunosuppressive immune microenvironment and subtype-specific therapeutic vulnerabilities in advanced salivary gland cancer [bulk RNA-seq]

GSE294016 Homo sapiens (human) RNA-seq (gene expression) 95 samples Submitted 2025/04/14 Platform GPL24676Platform GPL20301Platform GPL33758
At a glance

RNA-seq (gene expression) · human · 95 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
95 samples: 93 × bulkrna seq of tumor biopsy, patient PGroups 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
A macrophage-predominant immunosuppressive microenvironment and therapeutic vulnerabilities in advanced salivary gland cancerNature communications · 2025
Summary (written by the submitters)
Salivary gland cancers are rare, diverse malignancies characterized by poor response to immunotherapy. The tumor immune environment in these cancers remains poorly understood. To address this, we perform an integrative analysis of the tumor immune microenvironment in a large cohort of advanced salivary gland cancer samples. Most tumors exhibit low immune activity with limited immune cell infiltration.
Read the full summary ↓
Inflammation is linked to higher tumor mutational burden in non-adenoid cystic carcinoma histologies. Subtype specific expression of immune checkpoints is identified with prominent expression of VTCN1 in luminal-like cells within adenoid cystic carcinoma. Macrophages with immunosuppressive properties dominate the immune microenvironment across subtypes. Responses to immunotherapy are limited and associated with a higher ratio of T-cells relative to macrophages in individual cases, warranting further investigation. Here, we show an immunosuppressive environment in salivary gland cancers and identify subtype-specific immune vulnerabilities that could inform tailored therapeutic strategies.
Published in
A macrophage-predominant immunosuppressive microenvironment and therapeutic vulnerabilities in advanced salivary gland cancer
Zuljan E, von der Emde B, Piwonski I et al. · Nature communications 2025 · PMID 40506428 · doi:10.1038/s41467-025-60421-0
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 GSE294016_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 PRJNA1247796. Searching any of these in the dataset finder brings you back here.

Study design
93 × bulkrna seq of tumor biopsy, patient P

Supports a between-group comparison across 93 samples.

How we read this: 1 replicated groups read from 95 sample titles; they account for 93 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

+ 55 more — browse all 95 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

Share this dataset

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.