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LKB1 Loss Sensitizes Lung Cancer Cells to PRMT5 inhibition by upregulating NNMT

GSE279105 Homo sapiens (human) RNA-seq (gene expression) 12 samples Submitted 2026/01/01 Platform GPL24676
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)
Topic
Lung cancerThis series is part of our curated briefing on it.
Samples
12 samples: 3 × H460 cells, veh vs 3 × H460 cells, treat vs 3 × A549 cells, veh vs 3 × A549 cells, treatGroups 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)
Cancer is a major obstacle to health of modern people, in which lung cancer has a fairly high incidence and mortality rate, according to the results of the WHO IARC agency in 2021. Lung cancer is known for its hard treatment because of its genetic complexity and heterogeneity, especially the KRAS-mutant and LKB1-lost NSCLC. Here we show that LKB1 inactivation upregulates NNMT through SIK1/2-CRTC2 axis, which decreases the ratio of SAM/SAH.
Read the full summary ↓
Low ratio of SAM/SAH predicts high sensitivity to PRMT5 inhibition in NSCLC independent of MTA abundance, which was previously broadcast negative to PRMT5 activity in MTAP-absent cancer cells. What’s more, PRMT5 inhibition induces senescence in LKB1-mutant NSCLC cells by upregulating P21. Although there has been quite researches on the mechanism of metabolic remodeling of K/L lung cancer, the treatment methods for this type of lung cancer are still relatively limited. So this project attempts to find the unique metabolic remodeling mode of K/L mutant NSCLC and give specific treatment options.
This dataset
Open GSE279105 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 GSE279105_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 PRJNA1170872 and SRA study SRP537367. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
3 × H460 cells, veh vs 3 × H460 cells, treat vs 3 × A549 cells, veh vs 3 × A549 cells, treat

Supports a between-group comparison across 12 samples.

How we read this: 4 replicated groups read from 12 sample titles; they account for 12 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
More Lung cancer datasets

Search all human RNA-seq datasets in GEO →

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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.