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m6A sequencing of vitreous bodies of diabetic retinopathy patients

GSE263660 Homo sapiens (human) RNA-seq (gene expression) 12 samples Submitted 2025/06/30 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 12 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
12 samples: 3 × Input D vs 3 × Input NC vs 3 × IP D vs 3 × IP NCGroups 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
FTO-modulated m(6)A demethylation and upregulation of KIF11 mRNA promotes retinal microvascular dysfunction in diabetic retinopathyJournal of translational medicine · 2026
Summary (written by the submitters)
As the most prevalent, abundant and conserved internal cotranscriptional modification in eukaryotic RNAs, N6-methyladenosine (m6A) plays vital role in the progression of different types of human diseases, including cancers. m6A RNA methylation have been reported to affects the physiological and pathological progressions of hematopoietic, central nervous and reproductive systems.
Read the full summary ↓
As diabetic retinopathy is one of the most importent microvascular complication of diabetes, in this work, MeRIP-seq was employed to investigate the RNA m6A levels in the vitreous body samples of diabetic retinopathy and control groups. A group of mRNAs were suggested with significantly fluctuated transcription and m6A levels, and they would be inverstigated in the further work to understand the mechanisam of the progression of diabetic retinopathy.
Published in
FTO-modulated m(6)A demethylation and upregulation of KIF11 mRNA promotes retinal microvascular dysfunction in diabetic retinopathy
Lin C, Cao C, Cui S et al. · Journal of translational medicine 2026 · PMID 42226292 · doi:10.1186/s12967-026-08356-y
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 GSE263660_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 PRJNA1098720 and SRA study SRP500820. Searching any of these in the dataset finder brings you back here.

Study design
3 × Input D vs 3 × Input NC vs 3 × IP D vs 3 × IP NC

Supports a between-group comparison across 12 samples.

How we read this: 4 replicated groups read from 12 sample titles; they account for 12 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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