← BioTransfer GEO Dataset Finder
GEO series

EIF6 conditions drug-tolerant persister-like transdifferentiation in small cell lung carcinoma (SCLC cell lines)

GSE262593 Homo sapiens (human) RNA-seq (gene expression) 40 samples Submitted 2024/04/30 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 40 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
40 samples: 14 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
Eukaryote initiation factor 6 modulates small-cell lung carcinoma plasticity via the integrin-FAK signaling axisNature communications · 2026
Summary (written by the submitters)
Drug-tolerant persister cells withstand treatments by adapting their identity through lineage-dependent plasticity during systemic anti-cancer therapies. This phenomenon is evident in small-cell lung carcinoma (SCLC), a lethal neuroendocrine cancer initially responsive (60-80%) to platinum-based chemotherapy but succumbing to resistance within 6 months in advanced stages.
Read the full summary ↓
This resistance associates with the transdifferentiation of residual tumour cells into a non-neuroendocrine state, a process intricately tied to SCLC's chemotolerance, yet molecular mechanisms governing this lineage conversion remain completed understood. Here we use paired cytoplasmic RNA-seq and polysomal RNA-seq to compare gene expression between NE and non-NE SCLC cell lines on both transcriptional and translational level. We report that first-line chemotherapy induces translation initiation factor eIF6 in drug-tolerant persister-like cells in SCLC, correlating with the non-neuroendocrine state in SCLC. Intervening eIF6 inhibits non-neuroendocrine transdifferentiation, thus enhancing SCLC responsiveness to chemotherapy. This study sheds light on eIF6's potential therapeutic interventions to mitigate treatment resistance in SCLC.
Published in
Eukaryote initiation factor 6 modulates small-cell lung carcinoma plasticity via the integrin-FAK signaling axis
Peng H, Wang Z, Wang M et al. · Nature communications 2026 · PMID 41735293 · doi:10.1038/s41467-026-69899-8
This dataset
Open GSE262593 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 GSE262593_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 PRJNA1092617 and SRA study SRP498771. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41735293) ↗
Study design
14 conditions, mostly in duplicate
H69 cells, cytoplasmic RNA, ×6 H69 cells, polysomal RNA, ×6 H196 cells, cytoplasmic RNA, ×3 H196 cells, polysomal RNA, ×3 H69M cells, cytoplasmic RNA, ×3 H69M cells, polysome RNA, ×3 H82 cells, cytoplasmic RNA, ×2 H82 cells, polysomal RNA, ×2 +6 more

Supports a between-group comparison across 40 samples.

How we read this: 14 replicated groups read from 40 sample titles; they account for 40 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
Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

Share this dataset

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.