Differential gene expression analysis with or without IL-6 in multiple myeloma cells
RNA-seq (gene expression) · human · 40 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
- 40 samples: 40 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
- IL-6-driven POU2AF1 and ELL2 are key regulators of multiple myeloma-distinct transcriptional and splicing programsBlood advances · 2026
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 GSE298693_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 PRJNA1270955 and SRA study SRP589251. 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 40 sample titles: 40 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.
- GSM9021052 Sample1_MM1S_RNAseq_withoutIL6_4h_rep1
- GSM9021053 Sample2_MM1S_RNAseq_withoutIL6_4h_rep2
- GSM9021054 Sample3_MM1S_RNAseq_withIL6_4h_rep1
- GSM9021055 Sample4_MM1S_RNAseq_withIL6_4h_rep2
- GSM9021056 Sample5_INA6_RNAseq_withoutIL6_4h_rep1
- GSM9021057 Sample6_INA6_RNAseq_withoutIL6_4h_rep2
- GSM9021058 Sample7_INA6_RNAseq_withIL6_4h_rep1
- GSM9021059 Sample8_INA6_RNAseq_withIL6_4h_rep2
- GSM9021060 Sample9_MM1S_RNAseq_sgEGFP_ctrl_for_sgPOU2AF1_rep1
- GSM9021061 Sample10_MM1S_RNAseq_sgEGFP_ctrl_for_sgPOU2AF1_rep2
- GSM9021062 Sample11_MM1S_RNAseq_sgPOU2AF1_rep1
- GSM9021063 Sample12_MM1S_RNAseq_sgPOU2AF1_rep2
- GSM9021064 Sample13_MM1S_RNAseq_sgEGFP_ctrl_for_sgELL2_rep1
- GSM9021065 Sample14_MM1S_RNAseq_sgEGFP_ctrl_for_sgELL2_rep2
- GSM9021066 Sample15_MM1S_RNAseq_sgELL2_rep1
- GSM9021067 Sample16_MM1S_RNAseq_sgELL2_rep2
- GSM9021068 Sample17_MM1S_RNAseq_withoutIL6_24h_rep1
- GSM9021069 Sample18_MM1S_RNAseq_withoutIL6_24h_rep2
- GSM9021070 Sample19_MM1S_RNAseq_withIL6_24h_rep1
- GSM9021071 Sample20_MM1S_RNAseq_withIL6_24h_rep2
- GSM9021072 Sample21_INA6_RNAseq_withoutIL6_24h_rep1
- GSM9021073 Sample22_INA6_RNAseq_withoutIL6_24h_rep2
- GSM9021074 Sample23_INA6_RNAseq_withIL6_24h_rep1
- GSM9021075 Sample24_INA6_RNAseq_withIL6_24h_rep2
- GSM9021076 Sample25_MM1S_RNAseq_sgEGFP_ctrl_for_sgPOU2AF1_withIL6_rep1
- GSM9021077 Sample26_MM1S_RNAseq_sgEGFP_ctrl_for_sgPOU2AF1_withIL6_rep2
- GSM9021078 Sample27_MM1S_RNAseq_sgPOU2AF1_withIL6_rep1
- GSM9021079 Sample28_MM1S_RNAseq_sgPOU2AF1_withIL6_rep2
- GSM9021080 Sample29_MM1S_RNAseq_sgEGFP_ctrl_for_sgELL2_withIL6_rep1
- GSM9021081 Sample30_MM1S_RNAseq_sgEGFP_ctrl_for_sgELL2_withIL6_rep2
- GSM9021082 Sample31_MM1S_RNAseq_sgELL2_withIL6_rep1
- GSM9021083 Sample32_MM1S_RNAseq_sgELL2_withIL6_rep2
- GSM9021084 Sample33_INA6_RNAseq_sgEGFP_withIL6_rep1
- GSM9021085 Sample34_INA6_RNAseq_sgEGFP_withIL6_rep2
- GSM9021086 Sample35_INA6_RNAseq_sgPOU2AF1_withIL6_rep1
- GSM9021087 Sample36_INA6_RNAseq_sgPOU2AF1_withIL6_rep2
- GSM9021088 Sample37_INA6_RNAseq_sgELL2_withIL6_rep1
- GSM9021089 Sample38_INA6_RNAseq_sgELL2_withIL6_rep2
- GSM9021090 Sample39_Pt_RNAseq_withoutIL6_24h
- GSM9021091 Sample40_Pt_RNAseq_withIL6_24h
- GSE305971 Integrative single-cell analysis reveals malignant archetypes and proliferative hierarchies in multiple myeloma 2105 samples shares multiple, myeloma
- GSE305969 Integrative single-cell analysis reveals malignant archetypes and proliferative hierarchies in multiple myeloma [Part 3 of 4: Illumina NovaSeq 6000; MARS_v2] 874 samples shares multiple, myeloma
- GSE305967 Integrative single-cell analysis reveals malignant archetypes and proliferative hierarchies in multiple myeloma [Part 1 of 4: Illumina NovaSeq 6000; MARS] 691 samples shares multiple, myeloma
- GSE305970 Integrative single-cell analysis reveals malignant archetypes and proliferative hierarchies in multiple myeloma [Part 4 of 4: Illumina NovaSeq X; MARS_v2] 273 samples shares multiple, myeloma
- GSE309595 Longitudinal Analysis of Multiple Myeloma: Therapeutic Response and Immune Microenvironment Dynamics in the Context of Tumor Heterogeneity [scRNA-seq] 270 samples shares multiple, myeloma
- GSE305968 Integrative single-cell analysis reveals malignant archetypes and proliferative hierarchies in multiple myeloma [Part 2 of 4: Illumina NextSeq 500; MARS] 267 samples shares multiple, myeloma
- GSE226176 Expression data of multiple myeloma patients at various stages of disease 23 samples shares multiple, myeloma
- GSE261171 High-Resolution Single-Cell RNA Sequencing Uncovers Disparate Neutrophil Populations within Metastatic Cancer Lesions Linked to Progression Free Survival in Multiple Myeloma Patients 28 samples 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.