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Peripheral mRNA expression of first-episode drug-naïve patients with schizophrenia

GSE243841 Homo sapiens (human) RNA-seq (gene expression) 181 samples Submitted 2025/02/28 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 181 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
181 samples: 97 × YD vs 84 × owGroups 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
Identifying periphery biomarkers of first-episode drug-naïve patients with schizophrenia using machine-learning-based strategiesProgress in neuro-psychopharmacology & biological psychiatry · 2025
Summary (written by the submitters)
Schizophrenia is a complex mental disorder. Accurate diagnosis and classification of schizophrenia has always been a major challenge in clinic due to the lack of biomarkers. Therefore, identifying molecular biomarkers, particularly in the peripheral blood, is of great significance. This study aimed to identify immune-related molecular biomarkers of schizophrenia in peripheral blood.
Read the full summary ↓
Eighty-four Peripheral blood leukocytes of first-episode drug-naïve (FEDN) patients with schizophrenia and 97 healthy controls were collected and examined using high-throughput RNA-sequencing. Differentially-expressed genes (DEGs) were analysed. Weighted correlation network analysis (WGCNA) was employed to identify schizophrenia-associated module genes. The CIBERSORT algorithm was adopted to analyse immune cell proportions. Then, machine-learning algorithms including random forest, LASSO, and SVM-RFE were employed to screen immune-related predictive genes of schizophrenia. The RNA-seq analyses revealed 734 DEGs. Further machine-learning-based bioinformatic analyses screened out three immune-related predictive genes of schizophrenia (FOSB, NUP43, and H3C1), all of which were correlated with neutrophils and natural killer cells resting.
Published in
Identifying periphery biomarkers of first-episode drug-naïve patients with schizophrenia using machine-learning-based strategies
Pan B, Li X, Weng J et al. · Progress in neuro-psychopharmacology & biological psychiatry 2025 · PMID 40015618 · doi:10.1016/j.pnpbp.2025.111302
This dataset
Open GSE243841 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 GSE243841_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 PRJNA1020139 and SRA study SRP462743. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40015618) ↗
Study design
97 × YD vs 84 × ow

Supports a between-group comparison across 181 samples.

How we read this: 2 replicated groups read from 181 sample titles; they account for 181 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

+ 141 more — browse all 181 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.