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Inhibition of tumor-intrinsic NAT10 enhances antitumor immunity by triggering type I interferon responses via MYC/CDK2/DNMT1 pathway.

GSE285518 Mus musculus (mouse) RNA-seq (gene expression) 18 samples Submitted 2024/12/30 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 18 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)
Samples
18 samples: 18 samples, each with a distinct title — no replicated groupsGroups 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)
Purpose:Posttranscriptional modifications are deeply involved in cancer progression; however, there remains knowledge gap regarding the function and immune regulatory mechanism of newly discovered mRNA acetylation modification.Methods: The expression of NAT10 in human tumor tissues was analyzed based on TCGA data. NAT10 knockout murine cell lines were generated to analyze the NAT10-associated tumor progression and anti-tumor immune response in tumor xenograft models.
Read the full summary ↓
Immune cells and cytokines in TME were quantified by immunofluorescence, flow cytometry, quantitative reverse transcription-PCR, and ELISPOT assay. NAT10-associated differentially expressed genes were investigated in cancer cells and tissues with RNA-Seq. Results:Loss of tumoral NAT10 significantly stimulated tumor-specific cellular immune responses and suppressed tumor growth. Mechanistically, we identified MYC as a key downstream target of NAT10 via enhancing mRNA stability and translation efficiency. Inhibition of NAT10 blocked the MYC/CDK2/DNMT1 pathway, subsequently enhancing double-stranded RNAs (dsRNA) formation, which triggered type I interferon responses to enhance the in vivo response of tumor specific CD8+ T cells. Conclusions: Inhibition of NAT10 using either small molecule inhibitor (Remodelin) or PEI/PC7A/siNAT10 nanoparticles combined with PD-1 blockade synergistically enhanced the anti-tumor immune response and repressed tumor progression. Our findings uncovered the crucial role of tumor-intrinsic NAT10 in tumor immune microenvironment, representing a promising target for enhancing cancer immunotherapy.
This dataset
Open GSE285518 in finder → Find more mouse RNA-seq datasets →
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 GSE285518_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 PRJNA1204289 and SRA study SRP554369. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
18 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 18 sample titles: 18 distinct titles with little repetition. 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
Datasets on a similar topic

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