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Spon1+ inflammatory monocytes promote collagen remodeling and lung cancer metastasis through lipoprotein receptor 8 signaling.

GSE260524 Mus musculus (mouse) RNA-seq (gene expression) 21 samples Submitted 2024/03/05 Platform GPL21103
At a glance

RNA-seq (gene expression) · mouse · 21 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
Mus musculus (mouse)
Samples
21 samples: 21 samples, each with a distinct title — no replicated groupsGroups 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
Spon1+ inflammatory monocytes promote collagen remodeling and lung cancer metastasis through lipoprotein receptor 8 signalingJCI insight · 2024
Summary (written by the submitters)
Lung cancer is the leading cause of cancer-related deaths in the world, and non-small cell lung cancer (NSCLC) is the most common subset. Although immune checkpoint inhibitors have improved outcomes in a subset of NSCLC patients, these effects are usually not durable. Thus, a more complete understanding of how immune niches within the tumor microenvironment (TME) promote NSCLC progression will allow us to build upon these advances.
Read the full summary ↓
We previously found that infiltration of tumor inflammatory monocytes (TIMs) into lung squamous carcinoma (LUSC) tumors is associated with increased metastases and poor survival. To further understand how TIMs promote metastases, we compared RNA-seq profiles of TIMs from several LUSC metastatic models with IMs of non-tumor bearing controls. We identified Spon1, a secreted extracellular matrix glycoprotein, as upregulated in TIMs and that Spon1 expression in LUSC tumors corresponds with poor survival. Bioinformatic analyses of the Cancer Genome Atlas (TCGA) revealed that LUSC tumors with high SPON1 expression significantly enriched for collagen extracellular matrix (ECM) signatures. Unexpectedly, we observed SPON1+ TIMs mediate their effects directly through LRP8 (ApoER2) on NSCLC cells, which results in TGFβ1 activation and robust production of fibrillar collagens. Using several orthogonal approaches, we demonstrate that SPON1+ TIMs are sufficient to promote NSCLC metastases. Additionally, we find that Spon1 loss in the host, or Lrp8 loss in cancer cells, results in a significant decrease of both high-density collagen matrices and metastases. Finally, we confirm the relevance of the SPON1/LRP8/TGFβ1 axis with collagen production and survival in NSCLC patients. Taken together, our study describes how SPON1+ TIMs promotes collagen remodeling and NSCLC metastases through an LRP8-TGFβ1 signaling axis in lung cancer cells.
Published in
Spon1+ inflammatory monocytes promote collagen remodeling and lung cancer metastasis through lipoprotein receptor 8 signaling
Whately KM, Sengottuvel N, Edatt L et al. · JCI insight 2024 · PMID 38716730 · doi:10.1172/jci.insight.168792
This dataset
Open GSE260524 in finder → Find more mouse RNA-seq datasets →
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 GSE260524_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: 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 PRJNA1081940 and SRA study SRP492434. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 38716730) ↗
Study design
21 samples, each with a distinct title — no replicated groups

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

How we read this: Read from 21 sample titles: 21 distinct titles with little repetition. 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
Datasets on a similar topic

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