GEO series

MYC plus class IIa HDAC inhibiton potentiates mitochondrial dysfunction in non-small cell lung cancer [RNA-seq]

GSE279133 Homo sapiens (human) RNA-seq (gene expression) 64 samples Submitted 2025/04/23 Platform GPL24676

At a glance

RNA-seq (gene expression) · human · 64 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: Lung cancerThis series is part of our curated briefing on it.
Samples: 64 samples: 17 conditions, mostly with about 4 replicates eachGroups 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)

To evaluate potential therapeutic benefits from dual targeting of MYC and class IIa HDACs, we assessed the treatment efficacy of MYCi plus class IIa HDACi across 18 NSCLC cell lines, 10 of which demonstrated substantial reduction of cell viability upon combination treatment. RNA sequencing on treated cell lines identified large-scale transcriptional shifts facilitated by combination treatment, including suppression of MYC, cell cycle, and mitochondrial pathways.

Read the full summary ↓

ATAC-sequencing showed chromatin structure changes in genomic regions involved in cell cycle progression and inflammatory signaling. Furthermore, G1/S arrest and elevated mitochondrial ROS levels in combination drug-treated cells were confirmed using flow cytometry. Concordant with the role of MYC as a key cell cycle regulator, MYC protein levels were decreased in the combination treatment group as compared to the vehicle or mono-treatment groups. In summary, we define here a novel drug paradigm combining MYC and class IIa HDAC inhibitors, which potentiates anti-tumor efficacy in NSCLC via MYC depletion and oxidative stress.

This dataset

Open GSE279133 in finder → Find more human RNA-seq datasets → Lung cancer 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 GSE279133_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 PRJNA1170945 and SRA study SRP537523. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

17 conditions, mostly with about 4 replicates each

H460, Vehicle, ×4 H460, MYCi975, ×4 H460, TMP195, ×4 H460, Combo, ×4 H838, Combo, ×4 H1703, Veh, ×4 H1703, MYCi, ×4 H1703, TMP, ×4 +13 more

Supports a between-group comparison across 60 samples.

How we read this: 17 replicated groups read from 64 sample titles; they account for 60 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

GSM8561932 H460, Vehicle, rep1 H460, Vehicle,
GSM8561933 H460, MYCi975, rep1 H460, MYCi975,
GSM8561934 H460, TMP195, rep1 H460, TMP195,
GSM8561935 H460, Combo, rep1 H460, Combo,
GSM8561936 H838, Vehicle, rep1 H838, Vehicle,
GSM8561937 H838, MYCi975, rep1 H838, MYCi975,
GSM8561938 H838, Combo, rep1 H838, Combo,
GSM8561939 H1703, Veh, rep1 H1703, Veh,
GSM8561940 H1703, MYCi, rep1 H1703, MYCi,
GSM8561941 H1703, TMP, rep1 H1703, TMP,
GSM8561942 H1703, Combo, rep1 H1703, Combo,
GSM8561943 H1755, Veh, rep1 H1755, Veh,
GSM8561944 H1755, MYCi, rep1 H1755, MYCi,
GSM8561945 H1755, TMP, rep1 H1755, TMP,
GSM8561946 H1755, Combo, rep1 H1755, Combo,
GSM8561947 H460, Vehicle, rep2 H460, Vehicle,
GSM8561948 H460, MYCi975, rep2 H460, MYCi975,
GSM8561949 H460, TMP195, rep2 H460, TMP195,
GSM8561950 H460, Combo, rep2 H460, Combo,
GSM8561951 H838, Vehicle, rep2 H838, Vehicle,
GSM8561952 H838, MYCi975, rep2 H838, MYCi975,
GSM8561953 H838, Combo, rep2 H838, Combo,
GSM8561954 H1703, Veh, rep2 H1703, Veh,
GSM8561955 H1703, MYCi, rep2 H1703, MYCi,
GSM8561956 H1703, TMP, rep2 H1703, TMP,
GSM8561957 H1755, Veh, rep2 H1755, Veh,
GSM8561958 H1755, MYCi, rep2 H1755, MYCi,
GSM8561959 H1755, TMP, rep2 H1755, TMP,
GSM8561960 H1755, Combo, rep2 H1755, Combo,
GSM8561961 H460, Vehicle, rep3 H460, Vehicle,
GSM8561962 H460, MYCi975, rep3 H460, MYCi975,
GSM8561963 H460, TMP195, rep3 H460, TMP195,
GSM8561964 H460, Combo, rep3 H460, Combo,
GSM8561965 H838, Vehicle, rep3 H838, Vehicle,
GSM8561966 H838, Combo, rep3 H838, Combo,
GSM8561967 H1703, Veh, rep3 H1703, Veh,
GSM8561968 H1703, MYCi, rep3 H1703, MYCi,
GSM8561969 H1703, TMP, rep3 H1703, TMP,
GSM8561970 H1703, Combo, rep3 H1703, Combo,
GSM8561971 H1755, Veh, rep3 H1755, Veh,

+ 24 more — browse all 64 samples with per-sample file links →

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