Novel Blood-Based RNA Profiles can Predict Human Degenerative Cervical Myelopathy
RNA-seq (gene expression) · human · 46 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
- 46 samples: 46 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
- Peripheral blood RNA biomarkers can predict lesion severity in degenerative cervical myelopathyNeural regeneration research · 2025
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.
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.
Supplementary files
The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE223227_RAW.tar (it can run to tens of gigabytes).
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 PRJNA925435 and SRA study SRP418250. Searching any of these in the dataset finder brings you back here.
Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.
How we read this: Read from 46 sample titles: 46 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.
- GSM6943644 CON [GSM2011000]
- GSM6943645 DCM [GSM2012002]
- GSM6943646 DCM [GSM2012003]
- GSM6943647 DCM [GSM2012004]
- GSM6943648 DCM [GSM2012005]
- GSM6943649 DCM [GSM2012006]
- GSM6943650 DCM [GSM2012007]
- GSM6943651 DCM [GSM2012008]
- GSM6943652 DCM [GSM2012009]
- GSM6943653 DCM [GSM2012010]
- GSM6943654 DCM [GSM2012012]
- GSM6943655 DCM [GSM2012013]
- GSM6943656 DCM [GSM2012015]
- GSM6943657 DCM [GSM2012019]
- GSM6943658 DCM [GSM2012021]
- GSM6943659 DCM [GSM2012031]
- GSM6943660 DCM [GSM2012035]
- GSM6943661 DCM [GSM2012037]
- GSM6943662 DCM [GSM2012039]
- GSM6943663 DCM [GSM2012045]
- GSM6943664 DCM [GSM2012047]
- GSM6943665 DCM [GSM2012049]
- GSM6943666 DCM [GSM2012051]
- GSM6943667 CSR [GSM2012055]
- GSM6943668 CSR [GSM2012057]
- GSM6943669 DCM [GSM2012059]
- GSM6943670 CSR [GSM2012061]
- GSM6943671 DCM [GSM2012063]
- GSM6943672 DCM [GSM2012065]
- GSM6943673 DCM [GSM2012067]
- GSM6943674 DCM [GSM2012069]
- GSM6943675 DCM [GSM2012071]
- GSM6943676 DCM [GSM2012073]
- GSM6943677 CSR [GSM2012075]
- GSM6943678 CSR [GSM2012079]
- GSM6943679 CSR [GSM2012082]
- GSM6943680 CSR [GSM2012088]
- GSM6943681 DCM [GSM2012094_]
- GSM6943682 CSR [GSM2012097]
- GSM6943683 CON [GSM2012103]
+ 6 more — browse all 46 samples with per-sample file links →
- GSE130970 Gene expression predicts histological severity and reveals distinct molecular profiles of nonalcoholic fatty liver disease 78 samples shares profiles, predict
- GSE271704 5G-exposed human skin cells do not respond with altered gene expression and methylation profiles [RNA-Seq] 120 samples shares profiles
- GSE271961 Integrated electrophysiological and genomic profiles of single cells reveal spiking tumor cells in human glioma [Patch-Seq] 95 samples shares profiles
- GSE256207 Accessible chromatin profiles (ATAC) at single-cell level of human post-mortem brain tissue from the orbitofrontal cortex 92 samples shares profiles
- GSE254569 Gene expression profiles at single-cell level of human post-mortem brain tissue from the orbitofrontal cortex 92 samples shares profiles
- GSE224698 Gene expression profiles of human dermal fibroblasts and human pluripotent stem cells analyzed by bulk RNA-seq 69 samples shares profiles
- GSE295224 Dual single-cell and bulk RNA sequencing reveal transcriptional profiles underlying heterogenous host-parasite interactions in human peripheral blood mononuclear cells 42 samples shares profiles
- GSE277906 Expression profiles of human cumulus cells from patients with PCOS and non-PCOS 40 samples shares profiles
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.