GEO series

Metabolism-dependent secondary effect of anti-MAPK cancer therapy on DNA repair [QuantSeq_Q_siCTU2]

GSE233121 Homo sapiens (human) RNA-seq (gene expression) 72 samples Submitted 2024/05/08 Platform GPL24676

At a glance

RNA-seq (gene expression) · human · 72 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: 72 samples: 24 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: Metabolism-dependent secondary effect of anti-MAPK cancer therapy on DNA repairNAR cancer · 2024

Summary (written by the submitters)

The translation-dependent effect of amino acid bioavailability on the nature of proteins that cells express could have major consequences on their phenotype. We report that the intracellular concentration decrease of amino acids like aspartate and glutamate in a melanoma cell line exposed to anti-cancer MAPK inhibitors (MAPKi) triggers ribosomal peaks in mRNA regions enriched in codons corresponding to these amino acids and in the translation-dependent degradation of mRNAs encoding proteins enriched in these amino acids.

Read the full summary ↓

Since aspartate- and glutamate-rich proteins are involved in cell proliferation and DNA repair, we propose that MAPKi surviving cells degrade aspartate and glutamate to generate energy which simultaneously decreases their needs through the downregulation of gene products involved in cell proliferation. Moreover, the expression level decrease of aspartate- and glutamate-rich proteins involved in DNA repair increases as a side effect the probability of DNA damages and consequently the emergence of mutated cells in response to MAPKi.

Published in

Metabolism-dependent secondary effect of anti-MAPK cancer therapy on DNA repair

Aubé F, Fontrodona N, Guiguettaz L et al. · NAR cancer 2024 · PMID 38690580 · doi:10.1093/narcan/zcae019

This dataset

Open GSE233121 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 GSE233121_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 PRJNA975076 and SRA study SRP438933. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 38690580) ↗

Study design

24 conditions, mostly in triplicate

A375_CTU2-T-0 ×3 A375_CTU2-T-3 ×3 A375_CTU2-T-5 ×3 A375_CTU2-TCHX-0 ×3 A375_CTU2-TCHX-3 ×3 A375_CTU2-TCHX-5 ×3 A375_GL2-T-0 ×3 A375_GL2-T-3 ×3 +16 more

Supports a between-group comparison across 72 samples.

How we read this: 24 replicated groups read from 72 sample titles; they account for 72 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

GSM7401615 A375_CTU2-T-0_R1 A375_CTU2-T-0
GSM7401616 A375_CTU2-T-3_R1 A375_CTU2-T-3
GSM7401617 A375_CTU2-T-5_R1 A375_CTU2-T-5
GSM7401618 A375_CTU2-TCHX-0_R1 A375_CTU2-TCHX-0
GSM7401619 A375_CTU2-TCHX-3_R1 A375_CTU2-TCHX-3
GSM7401620 A375_CTU2-TCHX-5_R1 A375_CTU2-TCHX-5
GSM7401621 A375_CTU2-T-0_R2 A375_CTU2-T-0
GSM7401622 A375_CTU2-T-3_R2 A375_CTU2-T-3
GSM7401623 A375_CTU2-T-5_R2 A375_CTU2-T-5
GSM7401624 A375_CTU2-TCHX-0_R2 A375_CTU2-TCHX-0
GSM7401625 A375_CTU2-TCHX-3_R2 A375_CTU2-TCHX-3
GSM7401626 A375_CTU2-TCHX-5_R2 A375_CTU2-TCHX-5
GSM7401627 A375_CTU2-T-0_R3 A375_CTU2-T-0
GSM7401628 A375_CTU2-T-3_R3 A375_CTU2-T-3
GSM7401629 A375_CTU2-T-5_R3 A375_CTU2-T-5
GSM7401630 A375_CTU2-TCHX-0_R3 A375_CTU2-TCHX-0
GSM7401631 A375_CTU2-TCHX-3_R3 A375_CTU2-TCHX-3
GSM7401632 A375_CTU2-TCHX-5_R3 A375_CTU2-TCHX-5
GSM7401633 A375_GL2-T-0_R1 A375_GL2-T-0
GSM7401634 A375_GL2-T-3_R1 A375_GL2-T-3
GSM7401635 A375_GL2-T-5_R1 A375_GL2-T-5
GSM7401636 A375_GL2-TCHX-0_R1 A375_GL2-TCHX-0
GSM7401637 A375_GL2-TCHX-3_R1 A375_GL2-TCHX-3
GSM7401638 A375_GL2-TCHX-5_R1 A375_GL2-TCHX-5
GSM7401639 A375_GL2-T-0_R2 A375_GL2-T-0
GSM7401640 A375_GL2-T-3_R2 A375_GL2-T-3
GSM7401641 A375_GL2-T-5_R2 A375_GL2-T-5
GSM7401642 A375_GL2-TCHX-0_R2 A375_GL2-TCHX-0
GSM7401643 A375_GL2-TCHX-3_R2 A375_GL2-TCHX-3
GSM7401644 A375_GL2-TCHX-5_R2 A375_GL2-TCHX-5
GSM7401645 A375_GL2-T-0_R3 A375_GL2-T-0
GSM7401646 A375_GL2-T-3_R3 A375_GL2-T-3
GSM7401647 A375_GL2-T-5_R3 A375_GL2-T-5
GSM7401648 A375_GL2-TCHX-0_R3 A375_GL2-TCHX-0
GSM7401649 A375_GL2-TCHX-3_R3 A375_GL2-TCHX-3
GSM7401650 A375_GL2-TCHX-5_R3 A375_GL2-TCHX-5
GSM7401651 A375_mQ-T-0_R1 A375_mQ-T-0
GSM7401652 A375_mQ-T-3_R1 A375_mQ-T-3
GSM7401653 A375_mQ-T-5_R1 A375_mQ-T-5
GSM7401654 A375_mQ-TCHX-0_R1 A375_mQ-TCHX-0

+ 32 more — browse all 72 samples with per-sample file links →

Datasets on a similar topic

GSE233120 Metabolism-dependent secondary effect of anti-MAPK cancer therapy on DNA repair [QuantSeq_BRAF] 96 samples shares metabolism-dependent, secondary, anti-mapk, quantseq, therapy, repair
GSE233122 Metabolism-dependent secondary effect of anti-MAPK cancer therapy on DNA repair [RNAseq_siRNA_MAPKi] 36 samples shares metabolism-dependent, secondary, anti-mapk, therapy, repair
GSE232711 Metabolism-dependent secondary effect of anti-MAPK cancer therapy on DNA repair 24 samples shares metabolism-dependent, secondary, anti-mapk, therapy, repair
GSE232604 Metabolism-dependent secondary effect of anti-MAPK cancer therapy on DNA repair [RNA-seq] 12 samples shares metabolism-dependent, secondary, anti-mapk, therapy, repair
GSE232551 Metabolism-dependent secondary effect of anti-MAPK cancer therapy on DNA repair [glutamine] 12 samples shares metabolism-dependent, secondary, anti-mapk, therapy, repair
GSE232709 Metabolism-dependent secondary effect of anti-MAPK cancer therapy on DNA repair [MAPK inhibitors] 12 samples shares metabolism-dependent, secondary, anti-mapk, therapy, repair
GSE235057 Crosstalk between myeloid and B cells shapes the distinct microenvironments of primary and secondary liver cancer 29 samples shares secondary
GSE285008 Transcriptome Response and Protein Abundance are Correlated with Split Formation and Cell Detachment as Secondary Effects to Autoantibodies in Human Skin Organ Culture Models for Pemphgus Vulgaris 78 samples shares secondary

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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.