GEO series

RNA-seq analysis of human multiple myeloma cell lines treated with pyridostatin and panobinostat

GSE280683 Homo sapiens (human) RNA-seq (gene expression) 12 samples Submitted 2024/12/01 Platform GPL18573

At a glance

RNA-seq (gene expression) · human · 12 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: 12 samples: 12 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: Targeting transcription-replication conflicts using G-quadruplexes stabilizers in multiple myelomaBlood neoplasia · 2025

Summary (written by the submitters)

Multiple Myeloma is the second most frequent blood cancer, characterized by clonal proliferation of terminally differentiated plasma cells (PCs) in the bone marrow (BM)1. Despite considerable progress in the treatment of MM, particularly with the emergence of immune-based therapies, it remains unclear how to overcome drug resistance, especially in high-risk patients with TP53 abnormalities including del17p and/or TP53 mutations2–4.

Read the full summary ↓

Deregulated oncogene expression can alter replication timing and transcriptional programs, resulting in increased replication stress (RS), generally referred to as oncogene-induced RS5. Accordingly, MM cells (MMCs) but not normal PCs exhibit constitutive phosphorylation of the RS marker H2A.X (termed H2A.X)6. Oncogene overexpression also results in hyperactivation of the transcriptional machinery required to sustain increased proliferation and cell growth7,8. Hypertranscription thus exacerbates RS by impeding replication fork progression, often resulting in transcription-replication conflicts (TRCs). MMCs must cope with both oncogene-induced RS and a high level of transcription linked to immunoglobulin (Ig) synthesis, thus constituting a positive loop that sustains genomic instability. In line with this, increased transcription in MM has been associated with increased mutation rate9. This genomic instability is therefore an Achilles heel for MMCs, which are expected to be more dependent on the DNA damage response (DDR) for survival than normal PCs, offering exploitable targets for the development of new therapeutic strategies.

Published in

Targeting transcription-replication conflicts using G-quadruplexes stabilizers in multiple myeloma

Dutrieux L, Ovejero S, Guillemin A et al. · Blood neoplasia 2025 · PMID 40453131 · doi:10.1016/j.bneo.2025.100072

This dataset

Open GSE280683 in finder → Find more human RNA-seq datasets →

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 GSE280683_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 PRJNA1179959 and SRA study SRP542116. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40453131) ↗

Study design

12 samples, each with a distinct title — no replicated groups

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

How we read this: Read from 12 sample titles: 12 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.

Samples in this study

GSM8603684 XG2 response, control condition
GSM8603685 XG2 response, PDS condition
GSM8603686 XG2 response, PANO condition
GSM8603687 XG2 response, PDS+PANO condition
GSM8603688 JJN3 response, control condition
GSM8603689 JJN3 response, PDS condition
GSM8603690 JJN3 response, PANO condition
GSM8603691 JJN3 response, PDS+PANO condition
GSM8603692 XG7 response, control condition
GSM8603693 XG7 response, PDS condition
GSM8603694 XG7 response, PANO condition
GSM8603695 XG7 response, PDS+PANO condition

Datasets on a similar topic

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GSE305971 Integrative single-cell analysis reveals malignant archetypes and proliferative hierarchies in multiple myeloma 2105 samples shares multiple, myeloma
GSE226176 Expression data of multiple myeloma patients at various stages of disease 23 samples shares multiple, myeloma
GSE271107 Single-cell RNA sequencing of bone marrow aspirate samples from multiple myeloma and its precursor conditions 19 samples shares multiple, myeloma
GSE296117 A single-cell RNA-seq dataset of synovial fluid from rheumatoid arthritis treated with TNF-α/JAK inhibitor 18 samples shares rna-seq, treated
GSE279180 Cell type mapping reveals tissue niches and interactions in subcortical multiple sclerosis lesions [snRNA-seq] 16 samples shares multiple, rna-seq
GSE309593 Longitudinal Analysis of Multiple Myeloma: Therapeutic Response and Immune Microenvironment Dynamics in the Context of Tumor Heterogeneity [CITE-Seq] 91 samples shares multiple, myeloma
GSE294275 Transcriptional analysis of direct NSD2 target genes in t(4;14) multiple myeloma reveals H3K36me2-dependent regulation and H3K27me3 antagonism [CUT&TAG] 40 samples shares multiple, myeloma

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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.