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Development and validation of a gene-based classification model for pN2 lung adenocarcinoma

GSE282774 Homo sapiens (human) Single-nucleus RNA-seq 58 samples Submitted 2024/11/26 Platform GPL24676
At a glance

Single-nucleus RNA-seq · human · 58 samples

What it is
Single-nucleus RNA-seqGene activity measured in individual nuclei (used when whole cells are hard to isolate, for example from frozen tissue). Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
Organism
Homo sapiens (human)
Topic
Lung cancerThis series is part of our curated briefing on it.
Samples
58 samples
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
Highly proliferating cancer cells function as novel prognostic biomarkers for lung adenocarcinoma with particular usefulness for stage IA risk stratificationBMC cancer · 2025
Summary (written by the submitters)
Lung adenocarcinoma (LUAD) with pathological ipsilateral mediastinal lymph node (LN) involvement (pN2) exhibits strong biological and clinical heterogeneity. Thus, it is necessary to classify the biomolecular characteristics that lead to the prognostic heterogeneity of pN2-LUAD. The clinical characteristics and bulk RNA sequencing (RNA-seq) data of 75 patients with pN2-LUAD obtained from The Cancer Genome Atlas (TCGA) database were collected as the training set.
Read the full summary ↓
The disease-free survival (DFS) and overall survival (OS) of patients with different molecular classifications were evaluated. Next, differentially expressed genes (DEGs), biology, and immune cell infiltration in the microenvironment were analysed. Finally, DEGs in the pN2-A and pN2-B groups were included using a least absolute shrinkage and selection operator (LASSO) model, and gene signatures were selected for pN2-A/B type classification. The RNA-seq and single-nucleus RNA sequencing (snRNA-seq) data from our center (n=58) and the GSE68465 dataset (n=53) were used as the validation data sets.
Published in
Highly proliferating cancer cells function as novel prognostic biomarkers for lung adenocarcinoma with particular usefulness for stage IA risk stratification
Xiong Y, Ma Y, Lei J et al. · BMC cancer 2025 · PMID 39773365 · doi:10.1186/s12885-024-13308-0
This dataset
Open GSE282774 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 GSE282774_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: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

Also filed as BioProject PRJNA1190395 and SRA study SRP547561. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39773365) ↗
Study design
A cohort of 58 individual samples

The grouping that defines any comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Sample titles run LUADd1 … LUADd58 and carry no group information.

Samples in this study

+ 18 more — browse all 58 samples with per-sample file links →

More Lung cancer datasets

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.