GEO series

Immune Landscape of Intrathoracic Lymph Nodes with Metastasis in Primary Lung Malignancy using EBUS-TBNA samples

GSE277742 Homo sapiens (human) Single-cell RNA-seq 22 samples Submitted 2025/03/06 Platform GPL24676

At a glance

Single-cell RNA-seq · human · 22 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: 22 samples: 18 × P vs 4 × 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.

Summary (written by the submitters)

Malignant cells modulate immune cell phenotype and function to generate a favorable microenvironment for tumor survival and progression. The draining lymph node (LN) is the most frequent and often first site of cancer metastasis. Notwithstanding, our understanding of how cancer, metastatic to LNs, alters the immune cells therein remains limited.

Read the full summary ↓

Although LN biopsy is frequently performed as a standard practice in cancer diagnosis and staging, immunologic studies employing these samples are rare. The present study investigates the immune cell alterations in metastatic intrathoracic LNs. With the advent of single-cell technologies, we comprehensively analyzed immune cells in a series of bronchoscopic LN biopsies from human subjects undergoing diagnostic workup of cancer. Using single-cell RNA sequencing and mass cytometry analyses, we compared intrathoracic LN samples from 18 subjects with pathologically confirmed metastasis and four controls without evidence of metastasis. We found that immune cell composition and gene expression patterns differed markedly between metastatic and control LNs. In particular, metastatic LNs contained relatively more neutrophils and APOE-high myeloid cells, with the latter exhibiting significant transcriptional derangement and a powerful intercellular interaction signature. Additionally, CD8 T cells in metastatic LNs demonstrated a unique exhausted phenotype. Notably, our findings are corroborated in a publicly available lung adenocarcinoma cohort derived from surgical LN biopsies. Immune cell phenotypes and gene expression patterns from bronchoscopic LN biopsies can be leveraged to advance understanding of cancer immunology and may have independent diagnostic value when malignant cells fail to be identified on histopathology.

This dataset

Open GSE277742 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 GSE277742_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 PRJNA1163716 and SRA study SRP534020. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

18 × P vs 4 × N

Supports a between-group comparison across 22 samples.

How we read this: 2 replicated groups read from 22 sample titles; they account for 22 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

GSM8528670 N1 N
GSM8528671 N2 N
GSM8528672 N3 N
GSM8528673 N4 N
GSM8528674 P1 P
GSM8528675 P2 P
GSM8528676 P3 P
GSM8528677 P4 P
GSM8528678 P5 P
GSM8528679 P6 P
GSM8528680 P7 P
GSM8528681 P8 P
GSM8528682 P9 P
GSM8528683 P10 P
GSM8528684 P11 P
GSM8528685 P12 P
GSM8528686 P13 P
GSM8528687 P14 P
GSM8528688 P15 P
GSM8528689 P16 P
GSM8528690 P17 P
GSM8528691 P18 P

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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.