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A contamination-adjusted tissue thrombopoietic framework identifies a clinically relevant CAF-myeloid niche in non-small cell lung cancer: a paired transcriptomic and survival study

GSE329380 Homo sapiens (human) Single-cell RNA-seq 137 samples Submitted 2026/06/03 Platform GPL21290
At a glance

Single-cell RNA-seq · human · 137 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. 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
137 samples: 69 × NSCLC tumor tissue sample C vs 68 × Matched adjacent non-tumor lung tissue sample NGroups 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
A contamination-adjusted tissue thrombopoietic framework identifies a clinically relevant CAF-myeloid niche in non-small cell lung cancer: a paired transcriptomic and survival studyJournal of translational medicine · 2026
Summary (written by the submitters)
Platelet-related cancer biology is often studied through circulating markers, but tissue-centered thrombopoietic programs in lung cancer are difficult to distinguish from blood-contamination artifacts. In this study, paired bulk RNA-seq profiles were generated from human non-small cell lung cancer tumor tissues and matched adjacent non-tumor lung tissues.
Read the full summary ↓
Sample identifiers beginning with C denote tumor tissue libraries, and identifiers beginning with N denote matched adjacent non-tumor tissue libraries; shared numeric suffixes indicate patient pairing. These data were used as the local discovery layer for a contamination-adjusted megakaryopoiesis-platelet tumor axis (MPTA) framework. The analysis combined paired differential expression, explicit contamination-aware score construction, public single-cell and spatial reference analyses, external cohort assessment, and local clinicopathologic and survival evaluation. The deposited sequencing data support the tumor-adjacent transcriptomic comparisons and locked tissue-centered MPTA score construction described in the manuscript.
Published in
A contamination-adjusted tissue thrombopoietic framework identifies a clinically relevant CAF-myeloid niche in non-small cell lung cancer: a paired transcriptomic and survival study
Mao G, Li J, Song X et al. · Journal of translational medicine 2026 · PMID 42057042 · doi:10.1186/s12967-026-08186-y
This dataset
Open GSE329380 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 GSE329380_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 PRJNA1458781 and SRA study SRP695701. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42057042) ↗
Study design
69 × NSCLC tumor tissue sample C vs 68 × Matched adjacent non-tumor lung tissue sample N

Supports a between-group comparison across 137 samples.

How we read this: 2 replicated groups read from 137 sample titles; they account for 137 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

+ 97 more — browse all 137 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.