GEO series

Blood-Based Gene Signatures Associated with Therapeutic Response to Anti-TNF Therapy in Rheumatoid Arthritis: A Combined Meta-Analytical and Machine Learning Approach

GSE309430 Homo sapiens (human) RNA-seq (gene expression) 8 samples Submitted 2026/07/01 Platform GPL24676

At a glance

RNA-seq (gene expression) · human · 8 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: 8 samples: 8 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.
Paper: Blood-based gene signatures associated with therapeutic response to anti-TNF therapy in rheumatoid arthritis: a combined meta-analytical and machine learning approachEULAR rheumatology open · 2026

Summary (written by the submitters)

Objective Tumor necrosis factor alpha (TNF-α) inhibitors have significantly improved outcomes in rheumatoid arthritis (RA); however, up to 30–40% of patients show inadequate response. This highlights the need for predictive biomarkers to guide personalized therapy. Blood transcriptome profiling is a promising strategy for identifying gene signatures linked to treatment response, though prior studies have shown inconsistent results due to technical and biological heterogeneity.

Read the full summary ↓

Methods We performed a comprehensive meta-analysis of baseline blood transcriptome datasets from eight independent RA cohorts to identify genes consistently upregulated in patients who responded to TNF-α inhibitors. This analysis identified a core set of 39 recurrent genes (the Recurrent gene set). To evaluate its predictive relevance, we trained machine learning models using four gene subsets: the Recurrent set, the Patent gene set derived from a biomarker patent, the Top-ranked gene set based on Cohen’s d effect size, and the full gene set. External validation was performed using an independent dataset. Results The Recurrent gene set showed strong and consistent predictive performance across machine learning models. Notably, several genes overlapping with the Patent and Top-ranked sets were among the top contributors, supporting their potential value for future diagnostic applications. Conclusion Our study demonstrates the feasibility of transcriptome-guided prediction of response to TNF-α inhibitors in RA. The integration of meta-analysis and machine learning provides a strong foundation for the development of precision diagnostic tools to support personalized treatment strategies in clinical practice.

Published in

Blood-based gene signatures associated with therapeutic response to anti-TNF therapy in rheumatoid arthritis: a combined meta-analytical and machine learning approach

Choi Y, Kim T, Kim HJ et al. · EULAR rheumatology open 2026 · PMID 42370093 · doi:10.1016/j.ero.2025.11.022

This dataset

Open GSE309430 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 GSE309430_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 PRJNA1336067 and SRA study SRP629428. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42370093) ↗

Study design

8 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 8 sample titles: 8 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

GSM9267314 patient A1, 0 day
GSM9267315 patient A5, 0 day
GSM9267316 patient A10, 0 day
GSM9267317 patient A12, 0 day
GSM9267318 patient A28, 0 day
GSM9267319 patient A30, 0 day
GSM9267320 patient A33, 0 day
GSM9267321 patient C13, 0 day

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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.