Exosomal Non-Coding RNAs Drives Multiple Myeloma Progression
RNA-seq (gene expression) · human · 37 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
- Multiple myelomaThis series is part of our curated briefing on it.
- Samples
- 37 samples: 24 × MM patient vs 7 × Healthy Control vs 6 × MM Cell lineGroups 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
- Sex-specific dysregulation of exosomal non-coding RNAs drives multiple myeloma progressionBlood cancer journal · 2025
Read the full summary ↓
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 GSE294154_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 PRJNA1248476 and SRA study SRP577534. Searching any of these in the dataset finder brings you back here.
Supports a case/control comparison: 24 samples read as cases, 7 as controls.
How we read this: 3 replicated groups read from 37 sample titles; they account for 37 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.
- GSM8899516 MM patient1 MM patient
- GSM8899517 MM patient2 MM patient
- GSM8899518 MM patient3 MM patient
- GSM8899519 MM patient4 MM patient
- GSM8899520 MM patient5 MM patient
- GSM8899521 MM patient6 MM patient
- GSM8899522 MM patient7 MM patient
- GSM8899523 MM patient8 MM patient
- GSM8899524 MM patient9 MM patient
- GSM8899525 MM patient10 MM patient
- GSM8899526 MM patient11 MM patient
- GSM8899527 MM patient12 MM patient
- GSM8899528 MM patient13 MM patient
- GSM8899529 MM patient14 MM patient
- GSM8899530 MM patient15 MM patient
- GSM8899531 MM patient16 MM patient
- GSM8899532 MM patient17 MM patient
- GSM8899533 MM patient18 MM patient
- GSM8899534 MM patient19 MM patient
- GSM8899535 MM patient20 MM patient
- GSM8899536 MM patient21 MM patient
- GSM8899537 MM patient22 MM patient
- GSM8899538 MM patient23 MM patient
- GSM8899539 MM patient24 MM patient
- GSM8899540 Healthy Control1 Healthy Control
- GSM8899541 Healthy Control2 Healthy Control
- GSM8899542 Healthy Control3 Healthy Control
- GSM8899543 Healthy Control4 Healthy Control
- GSM8899544 Healthy Control5 Healthy Control
- GSM8899545 Healthy Control6 Healthy Control
- GSM8899546 Healthy Control7 Healthy Control
- GSM8899547 MM Cell line1 MM Cell line
- GSM8899548 MM Cell line2 MM Cell line
- GSM8899549 MM Cell line3 MM Cell line
- GSM8899550 MM Cell line4 MM Cell line
- GSM8899551 MM Cell line5 MM Cell line
- GSM8899552 MM Cell line6 MM Cell line
- GSE161195 Single cell RNA-seq of relapsed and refractory multiple myeloma patients defines new resistance pathways and targeted treatments 213 samples same disease briefing · shares multiple, myeloma
- GSE148715 “Direct to Drug” screening as a precision medicine tool in multiple myeloma 38 samples same disease briefing · shares multiple, myeloma
- GSE167968 RNA-seq of Multiple Myeloma 33 samples same disease briefing · shares multiple, myeloma
- GSE179453 Expression data from control-knockdown (CTR-KD) and MAST4-KD human multiple myeloma (MM) cell lines 6 samples same disease briefing · shares multiple, myeloma
- GSE178442 Genome-wide maps of transcription in DEAF1 overexpression(OE) and wildtype(WT) cells in Multiple Myeloma 4 samples same disease briefing · shares multiple, myeloma
- GSE155631 Oncogenic mechanisms of DIS3 mutations in Multiple Myeloma 62 samples same disease briefing · shares multiple, myeloma
- GSE167969 ATAC-seq and RNA-Seq of Multiple Myeloma 46 samples same disease briefing · shares multiple, myeloma
- GSE167042 Splicing modulation increases BCL2 dependence and sensitizes Multiple Myeloma cells to Venetoclax 4 samples same disease briefing · shares multiple, myeloma
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.