GEO series

Genome-wide CRISPR-Cas9 knockout screening identifies NUDCD2 depletion as sensitizer for bortezomib, carfilzomib and ixazomib in multiple myeloma.

GSE301632 Homo sapiens (human) RNA-seq (gene expression) 27 samples Submitted 2025/07/16 Platform GPL24676

At a glance

RNA-seq (gene expression) · human · 27 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: 27 samples: 9 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.

Summary (written by the submitters)

Background/Objectives: The treatment of multiple myeloma (MM) remains a challenge as almost all patients will eventually relapse. Proteasome inhibitors are a cornerstone in the management of MM. Unfortunately, validated biomarkers predict-ing drug response are largely missing. Therefore, we aimed to identify genes associated with drug resistance or sensitization to proteasome inhibitors.

Read the full summary ↓

Methods: We per-formed genome-wide CRISPR-Cas9 knockout (KO) screens in human KMS-28-BM my-eloma cells to identify genetic determinants associated with resistance or sensitization to proteasome inhibitors. Results: We show that KO of KLF13 and PSMC4 induces drug resistance, while NUDCD2, OSER1 and HERC1 KO cause drug sensitization. Sub-sequently, we focused on top sensitization hit, NUDCD2, which acts as a co-chaperone of Hsp90 to regulate the LIS1/dynein complex. RNA sequencing showed downregula-tion of genes involved in the ERAD pathway and in ER-associated ubiqui-tin-dependent protein catabolic processes in both untreated and carfilzomib-treated NUDCD2 KO cells, suggesting that NUDCD2 depletion alters protein degradation. Furthermore, bortezomib-treated NUDCD2 KO cells showed a decreased expression of genes that have a function in oxidative phosphorylation and the mitochondrial mem-brane, such as Carnitine Palmitoyltransferase 1A (CPT1A). CPT1A catalyzes the up-take of long chain fatty acids into mitochondria. Mitochondrial lipid metabolism has recently been reported as a possible therapeutic target for MM drug sensitivity. Con-clusions: These results contribute to the search of therapeutic targets that can sensitize MM patients to proteasome inhibitors.

This dataset

Open GSE301632 in finder → Find more human RNA-seq datasets → Multiple myeloma 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 GSE301632_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 PRJNA1285847 and SRA study SRP598098. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

9 conditions, mostly in triplicate

NUDCD2 sg2 D N= ×3 NUDCD2 sg2 B N= ×3 NUDCD2 sg2 C N= ×3 AAVS1 sg1 D N= ×3 AAVS1 sg1 B N= ×3 AAVS1 sg1 C N= ×3 NUDCD2 sg1 D N= ×2 NUDCD2 sg1 B N= ×2 +4 more

Supports a between-group comparison across 24 samples.

How we read this: 9 replicated groups read from 27 sample titles; they account for 24 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.

Samples in this study

GSM9086693 NUDCD2 sg1 DMSO N=1
GSM9086694 NUDCD2 sg1 Bortezomib N=1
GSM9086695 NUDCD2 sg1 Carflzomib N=1
GSM9086696 NUDCD2 sg2 D N=1 NUDCD2 sg2 D N=
GSM9086697 NUDCD2 sg2 B N=1 NUDCD2 sg2 B N=
GSM9086698 NUDCD2 sg2 C N=1 NUDCD2 sg2 C N=
GSM9086699 AAVS1 sg1 D N=1 AAVS1 sg1 D N=
GSM9086700 AAVS1 sg1 B N=1 AAVS1 sg1 B N=
GSM9086701 AAVS1 sg1 C N=1 AAVS1 sg1 C N=
GSM9086702 NUDCD2 sg1 D N=2 NUDCD2 sg1 D N=
GSM9086703 NUDCD2 sg1 B N=2 NUDCD2 sg1 B N=
GSM9086704 NUDCD2 sg1 C N=2 NUDCD2 sg1 C N=
GSM9086705 NUDCD2 sg2 D N=2 NUDCD2 sg2 D N=
GSM9086706 NUDCD2 sg2 B N=2 NUDCD2 sg2 B N=
GSM9086707 NUDCD2 sg2 C N=2 NUDCD2 sg2 C N=
GSM9086708 AAVS1 sg1 D N=2 AAVS1 sg1 D N=
GSM9086709 AAVS1 sg1 B N=2 AAVS1 sg1 B N=
GSM9086710 AAVS1 sg1 C N=2 AAVS1 sg1 C N=
GSM9086711 NUDCD2 sg1 D N=3 NUDCD2 sg1 D N=
GSM9086712 NUDCD2 sg1 B N=3 NUDCD2 sg1 B N=
GSM9086713 NUDCD2 sg1 C N=3 NUDCD2 sg1 C N=
GSM9086714 NUDCD2 sg2 D N=3 NUDCD2 sg2 D N=
GSM9086715 NUDCD2 sg2 B N=3 NUDCD2 sg2 B N=
GSM9086716 NUDCD2 sg2 C N=3 NUDCD2 sg2 C N=
GSM9086717 AAVS1 sg1 D N=3 AAVS1 sg1 D N=
GSM9086718 AAVS1 sg1 B N=3 AAVS1 sg1 B N=
GSM9086719 AAVS1 sg1 C N=3 AAVS1 sg1 C N=

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Search all human RNA-seq datasets in GEO →

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