← BioTransfer GEO Dataset Finder
GEO series

VAV3 antagonizes Skeletal Muscle Disulfidptosis to maintain Glucose Homeostasis in Type 2 Diabetes

GSE277813 Homo sapiens (human) RNA-seq (gene expression) 99 samples Submitted 2025/09/10 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 99 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
99 samples: 8 conditions, mostly with about 6 replicates eachGroups 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
The circRNA-mediated ceRNA molecular regulatory network in fatigue-type type 2 diabeteJournal of translational medicine · 2025
Summary (written by the submitters)
The emergence and progression of type 2 diabetes mellitus (T2DM) are intricately linked to skeletal muscle loss, yet the exact mechanisms remain elusive. Here, our whole-exome and transcriptomic results indicate that VAV3 is mutated and down-regulated in T2DM patients associated with increased sulfur metabolism. Integrated analysis of human transcriptomics, mouse and cellular proteomics, glycol-metabolomics analyses suggest a potential association between VAV3, T2DM and disulfidptosis.
Read the full summary ↓
Inhibiting disulfidptosis or SLC7A11/3A2 rescues death of Vav3-deleted cells and IR cells. Mechanistically, Vav3 deletion leads to decreased mitochondrial ATP synthesis and reduced GLUT4 expression. Insufficient glucose intake combined with elevated SLC7A11/3A2 expression induces NADPH depletion and cystine accumulation, leading to redox system imbalance and aberrant disulfide bonds formation. This results in F-actin collapse, broken fiber bundles and rapid cell death. Together, our findings reveal unexpected roles of VAV3 in regulating glucose metabolism, disulfidptosis and provide a therapeutic strategy to target skeletal muscle disulfdptosis in T2DM treatment.
Published in
The circRNA-mediated ceRNA molecular regulatory network in fatigue-type type 2 diabete
Zhen XJ, Wu T, Zhang M et al. · Journal of translational medicine 2025 · PMID 40898233 · doi:10.1186/s12967-025-07007-y
This dataset
Open GSE277813 in finder → Find more human 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 GSE277813_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 PRJNA1164252. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40898233) ↗
Study design
8 conditions, mostly with about 6 replicates each
ZC ×49 QYLXD ×13 SRKPD ×12 QYD ×7 YYLXD ×6 YXRSD ×6 WZC ×3 WS ×2 +1 more

Supports a between-group comparison across 98 samples.

How we read this: 8 replicated groups read from 99 sample titles; they account for 98 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

+ 59 more — browse all 99 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.