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ICAM1 mRNA Functions Independently of Protein Coding to Inhibit Translation via a dsRNA-ILF2/ILF3 Pathway in Lung Cancer

GSE277238 Homo sapiens (human) RNA-seq (gene expression) 32 samples Submitted 2025/12/29 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 32 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
32 samples: 12 conditions, mostly in duplicateGroups 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
ICAM1 mRNA entraps ILF2/ILF3 to inhibit transcription of EIF4E and global protein synthesisMolecular cell · 2026
Summary (written by the submitters)
This study investigates the role of Intercellular Adhesion Molecule 1 (ICAM1) in lung cancer using RNA-seq and CUT&Tag sequencing. We performed ICAM1 knockout and knockout&knockdown experiments in CALU-1 lung cancer cells to examine changes in gene expression and chromatin accessibility. Our findings provide insights into ICAM1's regulatory mechanisms and its potential as a therapeutic target in lung cancer.
Published in
ICAM1 mRNA entraps ILF2/ILF3 to inhibit transcription of EIF4E and global protein synthesis
Jiang S, Sun J, Gao Y et al. · Molecular cell 2026 · PMID 41512856 · doi:10.1016/j.molcel.2025.12.017
This dataset
Open GSE277238 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 GSE277238_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 PRJNA1161413 and SRA study SRP532823. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41512856) ↗
Study design
12 conditions, mostly in duplicate
ICAM1 KD1 ×3 ICAM1 KD2 ×3 Calu1-1 ×3 ICAM1 KO ×3 ICAM1-AS KD1 ×2 ICAM1-AS KD2 ×2 NC ×2 ICAM1 CDS ×2 +8 more

Supports a between-group comparison across 28 samples.

How we read this: 12 replicated groups read from 32 sample titles; they account for 28 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.