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Sex-specific and multiomic integration enhance accuracy of peripheral blood biomarkers of major depressive disorder [RNA-Seq]

GSE251778 Homo sapiens (human) RNA-seq (gene expression) 169 samples Submitted 2024/12/31 Platform GPL24676Platform GPL20301
At a glance

RNA-seq (gene expression) · human · 169 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
169 samples: 55 × WholeBlood-Control-F vs 50 × WholeBlood-MDD-F vs 34 × WholeBlood-Control-M vs 30 × WholeBlood-MDD-MGroups 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
Using multiomic integration to improve blood biomarkers of major depressive disorder: a case-control studyEBioMedicine · 2025
Summary (written by the submitters)
Major depressive disorder (MDD) is a leading cause of disability and reduced life expectancy, with a two-fold increase in prevalence in women compared to men. Over the last few years, identifying reliable molecular biomarkers of MDD has proved challenging, likely reflecting the fact that, in addition to sex-differences, a variety of environmental and genetic risk factors are implicated.
Read the full summary ↓
Recently, epigenetic processes have been proposed as mediators of the impact of life experiences on functional regulation of the genome, with the potential to contribute to MDD biomarker development. In this context, here we characterized and integrated transcriptomic gene expression data with two upstream mechanisms for epigenomic regulation, DNA methylation and micro-RNAs. The 3 molecular layers were analyzed in peripheral blood samples from a well-characterized cohort of individuals with MDD (n=80) and healthy controls (n=89), and processed using 3 complementary bioinformatic strategies. First, we conducted case-control comparisons for each single omic layer, and contrasted sex-specific adaptations. Second, we leveraged network theory to define gene co-expression modules, followed by step-by-step annotations across omic layers. Finally, we implemented a genome-wide and multiomic integration strategy that included cross-validation and bootstrapping. The approach was used to systematically compare the accuracy of MDD prediction across 6 methods for dimensionality reduction and, importantly, for every combination of 1, 2 or 3 types of molecular data. Results showed that accuracy was higher when female and male cohorts were analyzed separately, rather than combined, and also progressively increased with the number of molecular datasets considered. While multiomic informational gain has already been illustrated in other medical fields, our results pave the way towards similar advances in molecular psychiatry, and have practical implications towards developing clinically useful biomarkers of MDD.
Published in
Using multiomic integration to improve blood biomarkers of major depressive disorder: a case-control study
Mokhtari A, Ibrahim EC, Gloaguen A et al. · EBioMedicine 2025 · PMID 39914267 · doi:10.1016/j.ebiom.2025.105569
This dataset
Open GSE251778 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 GSE251778_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 PRJNA1055463 and SRA study SRP479443. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39914267) ↗
Study design
55 × WholeBlood-Control-F vs 50 × WholeBlood-MDD-F vs 34 × WholeBlood-Control-M vs 30 × WholeBlood-MDD-M

Supports a between-group comparison across 169 samples.

How we read this: 4 replicated groups read from 169 sample titles; they account for 169 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

+ 129 more — browse all 169 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

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