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Gene Expression Analysis of CD4 T Cells in Autoimmune Pancreatitis: Treatment-Correlated Expression Profiles

GSE282913 Homo sapiens (human) RNA-seq (gene expression) 45 samples Submitted 2026/02/28 Platform GPL16791
At a glance

RNA-seq (gene expression) · human · 45 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
45 samples: 23 × HC vs 22 × AIPGroups 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)
Introduction: Autoimmune pancreatitis (AIP) is a rare inflammatory disease characterized by elevated serum IgG4, pancreatic fibrosis, and immune cell infiltration. Prednisolone (PSL) effectively induces remission; however, high relapse rates after treatment cessation highlight the need for reliable biomarkers to predict long-term outcomes.
Read the full summary ↓
While T cells, particularly regulatory T cells (Tregs) and Th2 cells, are implicated in AIP pathogenesis, comprehensive immune profiling has been limited. Methods: Peripheral blood CD4+ T cells were isolated from AIP patients in the active phase (aAIP, n = 8), remission phase (tAIP, n = 14), and healthy controls (HC, n = 23). RNA sequencing was used to identify differentially expressed genes (DEGs) across groups, and PSL-responsive genes were determined through comparative analysis. Immune-related functions were analyzed via gene ontology (GO) enrichment.
This dataset
Open GSE282913 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 GSE282913_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 PRJNA1190997 and SRA study SRP547790. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
23 × HC vs 22 × AIP

Supports a between-group comparison across 45 samples.

How we read this: 2 replicated groups read from 45 sample titles; they account for 45 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

+ 5 more — browse all 45 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

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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.