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High-throughput sequencing of chemosensitive small cell lung cancer cell lines H82 and DMS114, and their corresponding chemoresistant counterparts

GSE313099 Homo sapiens (human) RNA-seq (gene expression) 12 samples Submitted 2026/06/18 Platform GPL11154
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
Small cell lung cancerThis series is part of our curated briefing on it.
Samples
12 samples: 3 × H82, parental, vs 3 × H82DDP, cisplatin-induced, vs 3 × DMS114, parental, vs 3 × DMS114DDP, cisplatin-induced,Groups 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
KDM4B/DHX9 promotes chemoresistance in small-cell lung cancer through the MYCN-driven signaling pathwayOncogene · 2026
Summary (written by the submitters)
Small cell lung cancer (SCLC) is the most aggressive subtype of lung cancer. Although most patients are initially sensitive to first-line chemotherapy with combined cisplatin and etoposide, chemotherapy drug resistance easily develops and quickly leads to tumor progression. Therefore, understanding the mechanisms of chemotherapy drug resistance and how to reverse it is key to improving the prognosis of patients with SCLC.
Read the full summary ↓
The parental SCLC cell lines H82 and DMS114, along with their corresponding cisplatin-induced resistant counterparts (H82DDP and DMS114DDP), were used to explore the mechanisms of chemoresistance in SCLC.
Published in
KDM4B/DHX9 promotes chemoresistance in small-cell lung cancer through the MYCN-driven signaling pathway
Lyu Q, Liu H, Shen W et al. · Oncogene 2026 · PMID 42056557 · doi:10.1038/s41388-026-03798-6
This dataset
Open GSE313099 in finder → Find more human RNA-seq datasets → Small cell 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 GSE313099_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 PRJNA1378256 and SRA study SRP653847. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42056557) ↗
Study design
3 × H82, parental, vs 3 × H82DDP, cisplatin-induced, vs 3 × DMS114, parental, vs 3 × DMS114DDP, cisplatin-induced,

Supports a case/control comparison: 6 samples read as cases, 6 as controls.

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 Small cell 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.