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Multi-Omics Integration Identifies Critical Molecular Drivers and Alternative Splicing Events in Small Cell Lung Cancer

GSE330338 Homo sapiens (human) RNA-seq (gene expression) 16 samples Submitted 2026/05/13 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 16 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
16 samples: 8 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.
Summary (written by the submitters)
Small cell lung cancer (SCLC) remains a highly aggressive malignancy and a therapeutic challenge in clinical oncology. Here, we conducted an integrated multi-omics analysis of treatment-naïve human SCLC tissues along with paired adjacent tissues (NAT, n = 12), including transcriptomic, proteomic, phosphoproteomic, and glycoproteomic profiling to identify novel prognostic markers and therapeutic targets.
Read the full summary ↓
Integration of these datasets revealed significant enrichment of RNA splicing pathway in SCLC. We systematically characterized aberrant alternative splicing events (AASEs), identifying 6,972 events with exon skipping representing the most prevalent type. These AASEs were regulated by RNA-binding proteins and predominantly enriched in pathways related to tumor proliferation and metastasis. We further identified four splicing-related proteins (THOC1, MAGOHB, RBM8A, and TXNL4A) that were markedly overexpressed in SCLC, correlated with proliferation, and predicted poor prognosis. Functional validation showed that THOC1 knockdown suppressed tumor growth in vivo, potentially through enhanced anti-tumor immunity. Additionally, we present the first N-glycoproteomic atlas of SCLC implicating glycosylated CEACAM proteins as potential biomarkers and revealing aberrantly glycosylated sites and proteins associated with SCLC. Our study unveils novel molecular features underlying SCLC pathogenesis and provides insights for developing therapeutic strategies targeting RNA splicing and protein glycosylation.
This dataset
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.

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 GSE330338_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA1463671 and SRA study SRP698631. Searching any of these in the dataset finder brings you back here.

Study design
8 conditions, mostly in duplicate
A ×2 C ×2 D ×2 E ×2 F ×2 I ×2 K ×2 L ×2

Supports a between-group comparison across 16 samples.

How we read this: 8 replicated groups read from 16 sample titles; they account for 16 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

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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.