Identification of NOTCH-driven matrisome-associated genes as prognostic indicators of multiple myeloma patient survival
RNA-seq (gene expression) · human · 30 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
- 30 samples: 10 conditions, mostly in triplicateGroups 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
- Identification of NOTCH-driven matrisome-associated genes as prognostic indicators of multiple myeloma patient survivalBlood cancer journal · 2023
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 GSE221038_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 PRJNA912649 and SRA study SRP413232. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 30 samples.
How we read this: 10 replicated groups read from 30 sample titles; they account for 30 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.
- GSM6840773 RPMI_8226_shNTC_rep1 RPMI_8226_shNTC
- GSM6840774 RPMI_8226_shNTC_rep2 RPMI_8226_shNTC
- GSM6840775 RPMI_8226_shNTC_rep3 RPMI_8226_shNTC
- GSM6840776 RPMI_8226_shN1_1_rep1 RPMI_8226_shN1_1
- GSM6840778 RPMI_8226_shN1_1_rep2 RPMI_8226_shN1_1
- GSM6840779 RPMI_8226_shN1_1_rep3 RPMI_8226_shN1_1
- GSM6840780 RPMI_8226_shN1_2_rep1 RPMI_8226_shN1_2
- GSM6840781 RPMI_8226_shN1_2_rep2 RPMI_8226_shN1_2
- GSM6840782 RPMI_8226_shN1_2_rep3 RPMI_8226_shN1_2
- GSM6840784 RPMI_8226_shN2_1_rep1 RPMI_8226_shN2_1
- GSM6840785 RPMI_8226_shN2_1_rep2 RPMI_8226_shN2_1
- GSM6840786 RPMI_8226_shN2_1_rep3 RPMI_8226_shN2_1
- GSM6840787 RPMI_8226_shN2_2_rep1 RPMI_8226_shN2_2
- GSM6840788 RPMI_8226_shN2_2_rep2 RPMI_8226_shN2_2
- GSM6840790 RPMI_8226_shN2_2_rep3 RPMI_8226_shN2_2
- GSM6840791 MM1S_shNTC_rep1 MM1S_shNTC
- GSM6840792 MM1S_shNTC_rep2 MM1S_shNTC
- GSM6840793 MM1S_shNTC_rep3 MM1S_shNTC
- GSM6840794 MM1S_shN1_1_rep1 MM1S_shN1_1
- GSM6840795 MM1S_shN1_1_rep2 MM1S_shN1_1
- GSM6840797 MM1S_shN1_1_rep3 MM1S_shN1_1
- GSM6840798 MM1S_shN1_2_rep1 MM1S_shN1_2
- GSM6840799 MM1S_shN1_2_rep2 MM1S_shN1_2
- GSM6840800 MM1S_shN1_2_rep3 MM1S_shN1_2
- GSM6840801 MM1S_shN2_1_rep1 MM1S_shN2_1
- GSM6840803 MM1S_shN2_1_rep2 MM1S_shN2_1
- GSM6840804 MM1S_shN2_1_rep3 MM1S_shN2_1
- GSM6840805 MM1S_shN2_2_rep1 MM1S_shN2_2
- GSM6840806 MM1S_shN2_2_rep2 MM1S_shN2_2
- GSM6840807 MM1S_shN2_2_rep3 MM1S_shN2_2
- 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.