GEO series

Non-Invasive Detection of Bone Marrow Fibrosis in Myeloproliferative Neoplasms Using Cell-Free RNA

GSE308705 Homo sapiens (human) RNA-seq (gene expression) 16 samples Submitted 2025/10/15 Platform GPL16791

At a glance

RNA-seq (gene expression) · human · 16 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)
Topic: Myeloproliferative neoplasmsThis series is part of our curated briefing on it.
Samples: 16 samples
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.

Summary (written by the submitters)

Myeloproliferative neoplasms (MPNs), particularly with myelofibrosis (MF), involve a disrupted perivascular hematopoietic niche ultimately leading to bone marrow fibrosis. We asked if the transcriptome in cell-free RNA (cf-RNA) from peripheral blood of MPN patients (with JAK2V617F mutation) can detect bone marrow fibrosis. Transcriptomic profiling revealed significant gene expression changes correlating with reticulin fibrosis grades.

Read the full summary ↓

Advanced reticulin fibrosis grades (2-3) showed upregulation of TGF-β pathways and ECM remodeling markers, with decreased hematopoietic support. Grade 3 fibrosis was associated with increased proliferation signals and elevated inflammatory markers (S100A8/9). RUNX1 was identified as a key transcription factor in fibrosis with its overexpression driving myofibroblast differentiation in mesenchymal stromal cells. IL-18 emerged as a critical inflammatory mediator, with elevated plasma levels correlating with the transformation to high grades fibrosis (reticulin grades 2-3). Functional assays confirmed that IL-18 stimulation of mesenchymal stromal cells induced fibrotic transformation, emphasizing its role as a biomarker and target.

This dataset

Open GSE308705 in finder → Find more human RNA-seq datasets → Myeloproliferative neoplasms landscape →

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 GSE308705_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 PRJNA1332559 and SRA study SRP624278. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

A cohort of 16 individual samples

The grouping that defines any comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Sample titles run Patient_1 … Patient_16 and carry no group information.

Samples in this study

GSM9251725 Patient_1
GSM9251726 Patient_2
GSM9251727 Patient_3
GSM9251728 Patient_4
GSM9251729 Patient_5
GSM9251730 Patient_6
GSM9251731 Patient_7
GSM9251732 Patient_8
GSM9251733 Patient_9
GSM9251734 Patient_10
GSM9251735 Patient_11
GSM9251736 Patient_12
GSM9251737 Patient_13
GSM9251738 Patient_14
GSM9251739 Patient_15
GSM9251740 Patient_16

More Myeloproliferative neoplasms datasets

GSE309082 Menin-dependent megakaryocyte proliferation and fibrosis in myeloproliferative neoplasms 29 samples same disease briefing · shares myeloproliferative, neoplasms, fibrosis
GSE242730 Comparision of bone marrow cells between trisomy 8+ myelofibrosis vs. normal cells. 4 samples same disease briefing · shares fibrosis, marrow, bone
GSE277354 Genetic charcterization of myeloproliferative neoplasms (MPN) 402 samples same disease briefing · shares myeloproliferative, neoplasms
GSE288386 Type 2 calreticulin mutations activate ATF6 to promote BCL-xL-mediated survival in myeloproliferative neoplasms 37 samples same disease briefing · shares myeloproliferative, neoplasms
GSE301154 Preclinical efficacy of tasquinimod-based combinations in advanced myeloproliferative neoplasms (MPN) in blastic phase 32 samples same disease briefing · shares myeloproliferative, neoplasms
GSE288557 ULK1 as a novel therapeutic target in Myeloproliferative Neoplasms [RNA-Seq] 12 samples same disease briefing · shares myeloproliferative, neoplasms
GSE288706 Mesenchymal stromal cells from JAK2V617F myeloproliferative neoplasms support healthy and malignant haematopoiesis in a humanised scaffold model in vivo 12 samples same disease briefing · shares myeloproliferative, neoplasms
GSE277035 Pre-clinical efficacy of CDK7 inhibitor-based combinations against myeloproliferative neoplasms (MPN) transformed to AML 10 samples same disease briefing · shares myeloproliferative, neoplasms

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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.