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EDF1 accelerates ganglioside GD3 accumulation to boost CD52-mediated CD8+ T cell dysfunction in neuroblastoma

GSE285546 Homo sapiens (human) RNA-seq (gene expression) 89 samples Submitted 2025/02/12 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 89 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
NeuroblastomaThis series is part of our curated briefing on it.
Samples
89 samples
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
EDF1 accelerates ganglioside GD3 accumulation to boost CD52-mediated CD8(+) T cell dysfunction in neuroblastomaJournal of experimental & clinical cancer research : CR · 2025
Summary (written by the submitters)
Background Heterogeneous clinical features and prognosis in neuroblastoma (NB) children are frequently dominated by immune elements. Dysfunction and apoptosis in immune cells result from the exposure to continuous tumor-related antigen stimulation and coinhibitory signals. To date, key factors pointing to the restriction of NB-specific CD8+T cells remain elusive.
Read the full summary ↓
Methods We performed bulk-RNA sequencing and lipidomic analyses of children with mediastinal neuroblastoma. Bioinformatics analysis and validation of molecular biology assays were applied to uncover the underlying mechanism. Results Three subtypes were identified using nonnegative matrix factorization (NMF), we highlighted an apoptotic status of infiltrated CD8+T cells, along with the highest CD52 expression in Cluster3 (C3) subtypes, and verified high EDF1 expression in NB cells led to Lactosylceramide (LacCer) and downstream ganglioside-GD3 accumulation, which subsequently increased chemotaxis, CD52 and immune checkpoint expression, and apoptosis-related events in activated CD8+T cells. Mechanistically, EDF1 was recruited as a coactivator to form the NF-κB/RelA/EDF1 complex, which prevented the methylation of targeted site after binding to the promoter of GD3 synthase ST8SIA1, resulting in elevated ST8SIA1 transcription. Conclusion These findings characterizes the content of GD3 in NB cells, which is regulated by the EDF1/RelA/ST8SIA1 axis, is responsible for CD8+T cell dysfunction and inducing immune escape of NB cells. Inhibiting EDF1 may decreases suppressive factors and resolve apoptotic CD8+T cells. Controlling NB-associated GD3 levels through metabolic intervention is beneficial for tuning the depth and duration of response to current NB therapies.The integration of transcriptomic and lipidomic data provided a better understanding of the interaction between LacCer metabolites and the immune status in NB.
Published in
EDF1 accelerates ganglioside GD3 accumulation to boost CD52-mediated CD8(+) T cell dysfunction in neuroblastoma
Li D, Li M, Zhuo Z et al. · Journal of experimental & clinical cancer research : CR 2025 · PMID 39905449 · doi:10.1186/s13046-025-03307-9
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 GSE285546_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 PRJNA1201858. Searching any of these in the dataset finder brings you back here.

Samples in this study

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