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Proteogenomic Characterization Reveals Estrogen Signaling as a Target for Never-Smoker Lung Adenocarcinoma Patients without EGFR or ALK Alterations [RNA-seq]

GSE256091 Homo sapiens (human) RNA-seq (gene expression) 141 samples Submitted 2024/03/11 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 141 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
141 samples: 110 × NSLA vs 31 × NSLA_2Groups 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
Proteogenomic Characterization Reveals Estrogen Signaling as a Target for Never-Smoker Lung Adenocarcinoma Patients without EGFR or ALK AlterationsCancer research · 2024
Summary (written by the submitters)
Never-smoker lung adenocarcinoma (NSLA) is prevalent in Asian populations, and is even more in women. EGFR mutations and ALK fusions are major alterations observed in NSLA. We have focused on NSLA without EGFR and ALK alteration (NENA) rather than NSLA with EGFR and ALK (EA). First, we selected 141 NSLA tissues, and performed proteogenomic analyses including the whole-genome sequencing (WGS), transcriptome, methylation EPIC array, total proteome and phosphoproteome.
Read the full summary ↓
We then excluded 40 patients with EA and 7 patients with NENA microsatellite instability. Genome analysis revealed that TP53 (25%), KRAS (22%), ROS1 fusion (14%), and SETD2 (11%) were the most frequently mutated genes in NENA patients. Proteogenomic impact analysis revealed that STK11 and ERBB2 somatic mutations had broader effects on cancer-associated genes in NENA. Through DNA copy-number alteration analysis, we identified 22 prognostic proteins, influencing transcriptomic and proteomic changes. Gene set enrichment analysis revealed that the estrogen signaling emerged as the key pathway activated in NENA. A lot of proteogenomic evidence supported the increased estrogen signaling, such as copy-number deletions in chromosomes 14 and 21, STK11 mutation, and DNA hypomethylation of LLGL2 and ST14. Finally, saracatinib, an Src inhibitor, was suggested as a potential drug for targeting activated estrogen signaling in NENA, and was experimentally validated in vitro using cell line model. In this study, we enhanced our understanding of the etiology of NENA NSLA through the proteogenomic landscape, based on which we proposed saracatinib as an effective drug
Published in
Proteogenomic Characterization Reveals Estrogen Signaling as a Target for Never-Smoker Lung Adenocarcinoma Patients without EGFR or ALK Alterations
Park SJ, Ju S, Goh SH et al. · Cancer research 2024 · PMID 38607364 · doi:10.1158/0008-5472.CAN-23-1551
This dataset
Open GSE256091 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 GSE256091_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 PRJNA1078228 and SRA study SRP490416. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 38607364) ↗
Study design
110 × NSLA vs 31 × NSLA_2

Supports a between-group comparison across 141 samples.

How we read this: 2 replicated groups read from 141 sample titles; they account for 141 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

+ 101 more — browse all 141 samples with per-sample file links →

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.