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Contribution of crosstalk of mesothelial and tumoral epithelial cells in pleural metastasis of lung cancer

GSE242951 Homo sapiens (human) Single-cell RNA-seq 12 samples Submitted 2024/06/26 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 12 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
12 samples: 3 × MET-5A, Control, vs 3 × MET-5A, Co-culture, vs 3 × A549, Control, vs 3 × A549, Co-culture,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
Contribution of crosstalk of mesothelial and tumoral epithelial cells in pleural metastasis of lung cancerTranslational lung cancer research · 2024
Summary (written by the submitters)
Tumor metastasis commonly affects pleura in advanced lung cancer and is related to poor prognosis, but without systematic investigation on different cell types and their crosstalk at single cell resolution. Here, by integrating 180785 single cells from lung cancer and control samples, the most distinctive transcriptome profiles between tumor and control were revealed in mesothelial cells, which is the predominate cell type of pleura.
Read the full summary ↓
Four subtypes were divided, including one predominately identified in malignant pleural effusion which was characterized by enriched cancer related pathways (e.g., cell migration) along evolutionary trajectory from normal mesothelial cells. Cancer-associated mesothelial cells exhibited varied interactions with different subtypes of malignant epithelial cells, and multiple ligands/receptors exhibited significant correlation with poor prognosis. Experimentally, mesothelial cells can increase the migration ability of lung cancer cells through co-culturing assay, and EGFR was the only affected gene in cancer cells that exhibited interaction with mesothelial cells and was associated with poor prognosis. Using EGFR antagonist cetuximab prevented the increased invasiveness of lung cancer cells induced by mesothelial cells. Moreover, EPGN-EGFR interaction was supported through spatial distribution analysis, revealing the significant proximity between EPGN+ mesothelial cells and EGFR+ epithelial cells. Our findings highlighted the important role of mesothelial cells and their interactions with cancer cells in pleural metastasis of lung cancer, which may facilitate cancer progression.
Published in
Contribution of crosstalk of mesothelial and tumoral epithelial cells in pleural metastasis of lung cancer
Li PH, Zhang X, Yan H et al. · Translational lung cancer research 2024 · PMID 38854934 · doi:10.21037/tlcr-24-118
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 GSE242951_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: 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 PRJNA1015604 and SRA study SRP459936. Searching any of these in the dataset finder brings you back here.

Study design
3 × MET-5A, Control, vs 3 × MET-5A, Co-culture, vs 3 × A549, Control, vs 3 × A549, Co-culture,

Supports a between-group comparison across 12 samples.

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
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