GEO series
Circulating cell-free RNA signatures for the characterization and diagnosis of myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS)
GSE293840
Homo sapiens (human)
RNA-seq (gene expression)
168 samples
Submitted 2025/08/18
Platform GPL24676
At a glance
RNA-seq (gene expression) · human · 168 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
- 168 samples: 168 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
- Circulating cell-free RNA signatures for the characterization and diagnosis of myalgic encephalomyelitis/chronic fatigue syndromeProceedings of the National Academy of Sciences of the United States of America · 2025
Summary (written by the submitters)
People living with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) experience heterogeneous and debilitating symptoms that lack sufficient biological explanation, compounded by the absence of accurate, noninvasive diagnostic tools. To address these challenges, we explored circulating cell-free RNA (cfRNA) as a blood-borne bioanalyte to monitor ME/CFS.
Read the full summary ↓
cfRNA is released into the bloodstream during cellular turnover and reflects dynamic changes in gene expression, cellular signaling, and tissue-specific processes. We profiled cfRNA in plasma by RNA sequencing for 93 ME/CFS cases and 75 healthy sedentary controls, then applied machine learning to develop diagnostic models and advance our understanding of ME/CFS pathobiology. A generalized linear model with least absolute shrinkage selector operator regression trained on condition-specific signatures achieved a test-set AUC of 0.81 and an accuracy of 77%. Immune cfRNA deconvolution revealed differences in platelet-derived cfRNA between cases and controls, as well as elevated levels of plasmacytoid dendritic, monocyte, and T cell–derived cfRNA in ME/CFS. Biological network analysis further implicated immune dysfunction in ME/CFS, with signatures of cytokine signaling and T cell exhaustion. These findings demonstrate the utility of RNA liquid biopsy as a minimally invasive tool for unraveling the complex biology behind chronic illnesses.
Published in
Gardella AE, Eweis-LaBolle D, Loy CJ et al.
· Proceedings of the National Academy of Sciences of the United States of America 2025
· PMID 40789036
· doi:10.1073/pnas.2507345122
This dataset
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.
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 GSE293840_RAW.tar (it can run to tens of gigabytes).
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.
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 PRJNA1246570 and SRA study SRP576513. Searching any of these in the dataset finder brings you back here.
Study design
168 samples, each with a distinct title — no replicated groups
Supports a case/control comparison: 93 samples read as cases, 75 as controls.
How we read this: Read from 168 sample titles: 168 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
- GSM8891375 Plasma cell-free RNA, RNA seq for subject: cfs_cfrna_001, healthy control
- GSM8891376 Plasma cell-free RNA, RNA seq for subject: cfs_cfrna_002, healthy control
- GSM8891377 Plasma cell-free RNA, RNA seq for subject: cfs_cfrna_003, healthy control
- GSM8891378 Plasma cell-free RNA, RNA seq for subject: cfs_cfrna_004, ME/CFS patient
- GSM8891379 Plasma cell-free RNA, RNA seq for subject: cfs_cfrna_005, healthy control
- GSM8891380 Plasma cell-free RNA, RNA seq for subject: cfs_cfrna_006, ME/CFS patient
- GSM8891381 Plasma cell-free RNA, RNA seq for subject: cfs_cfrna_007, healthy control
- GSM8891382 Plasma cell-free RNA, RNA seq for subject: cfs_cfrna_008, healthy control
- GSM8891383 Plasma cell-free RNA, RNA seq for subject: cfs_cfrna_011, ME/CFS patient
- GSM8891384 Plasma cell-free RNA, RNA seq for subject: cfs_cfrna_012, ME/CFS patient
- GSM8891385 Plasma cell-free RNA, RNA seq for subject: cfs_cfrna_013, ME/CFS patient
- GSM8891386 Plasma cell-free RNA, RNA seq for subject: cfs_cfrna_014, ME/CFS patient
- GSM8891387 Plasma cell-free RNA, RNA seq for subject: cfs_cfrna_015, healthy control
- GSM8891388 Plasma cell-free RNA, RNA seq for subject: cfs_cfrna_016, ME/CFS patient
- GSM8891389 Plasma cell-free RNA, RNA seq for subject: cfs_cfrna_017, healthy control
- GSM8891390 Plasma cell-free RNA, RNA seq for subject: cfs_cfrna_018, ME/CFS patient
- GSM8891391 Plasma cell-free RNA, RNA seq for subject: cfs_cfrna_021, healthy control
- GSM8891392 Plasma cell-free RNA, RNA seq for subject: cfs_cfrna_022, healthy control
- GSM8891393 Plasma cell-free RNA, RNA seq for subject: cfs_cfrna_023, ME/CFS patient
- GSM8891394 Plasma cell-free RNA, RNA seq for subject: cfs_cfrna_024, healthy control
- GSM8891395 Plasma cell-free RNA, RNA seq for subject: cfs_cfrna_025, ME/CFS patient
- GSM8891396 Plasma cell-free RNA, RNA seq for subject: cfs_cfrna_026, ME/CFS patient
- GSM8891397 Plasma cell-free RNA, RNA seq for subject: cfs_cfrna_027, ME/CFS patient
- GSM8891398 Plasma cell-free RNA, RNA seq for subject: cfs_cfrna_028, ME/CFS patient
- GSM8891399 Plasma cell-free RNA, RNA seq for subject: cfs_cfrna_031, ME/CFS patient
- GSM8891400 Plasma cell-free RNA, RNA seq for subject: cfs_cfrna_032, healthy control
- GSM8891401 Plasma cell-free RNA, RNA seq for subject: cfs_cfrna_033, ME/CFS patient
- GSM8891402 Plasma cell-free RNA, RNA seq for subject: cfs_cfrna_034, healthy control
- GSM8891403 Plasma cell-free RNA, RNA seq for subject: cfs_cfrna_035, ME/CFS patient
- GSM8891404 Plasma cell-free RNA, RNA seq for subject: cfs_cfrna_036, healthy control
- GSM8891405 Plasma cell-free RNA, RNA seq for subject: cfs_cfrna_037, healthy control
- GSM8891406 Plasma cell-free RNA, RNA seq for subject: cfs_cfrna_038, healthy control
- GSM8891407 Plasma cell-free RNA, RNA seq for subject: cfs_cfrna_041, ME/CFS patient
- GSM8891408 Plasma cell-free RNA, RNA seq for subject: cfs_cfrna_042, ME/CFS patient
- GSM8891409 Plasma cell-free RNA, RNA seq for subject: cfs_cfrna_043, healthy control
- GSM8891410 Plasma cell-free RNA, RNA seq for subject: cfs_cfrna_044, healthy control
- GSM8891411 Plasma cell-free RNA, RNA seq for subject: cfs_cfrna_045, healthy control
- GSM8891412 Plasma cell-free RNA, RNA seq for subject: cfs_cfrna_046, ME/CFS patient
- GSM8891413 Plasma cell-free RNA, RNA seq for subject: cfs_cfrna_047, healthy control
- GSM8891414 Plasma cell-free RNA, RNA seq for subject: cfs_cfrna_048, healthy control
+ 128 more — browse all 168 samples with per-sample file links →
Datasets on a similar topic
- GSE287043 Unique Mycobacterium tuberculosis-specific blood immune signatures in sarcoidosis: implications for clinical diagnosis 542 samples shares signatures, diagnosis
- GSE307931 Circulating transcriptome analysis finds gene signatures and immune cell activation and abundance predict response to immunotherapy in bladder cancer 164 samples shares circulating, signatures
- GSE274114 Metabolic dysfunction-associated steatohepatitis reduces interferon and macrophage liver gene signatures in chronic HBV patients 39 samples shares signatures, chronic
- GSE286042 Transcriptomic and Proteomic Signatures Following AS03-adjuvanted Influenza A/H7N9 Vaccine 1232 samples shares signatures
- GSE280113 Progenitor effects and unique transcriptomic signatures linked to differentiation phenotype in clonally expanded antigen-specific CD8 T memory stem cells (TSCM) 509 samples shares signatures
- GSE285943 A reference model for circulating hematopoietic stem cells with applications to diagnostics 299 samples shares circulating
- GSE313307 Systemic infections alter cortical transcriptional signatures in Alzheimer′s disease 202 samples shares signatures
- GSE171741 Albumin treatment transcriptionally reprograms circulating immune cells in patients with acutely decompensated cirrhosis 139 samples shares circulating
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.