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Comparative network-based analysis of toll-like receptor agonist, L-pampo signaling pathways in immune and cancer cells

GSE264131 Homo sapiens (human) RNA-seq (gene expression) 27 samples Submitted 2024/07/26 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 27 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
Homo sapiens (human)
Samples
27 samples: 9 conditions, mostly in triplicateGroups 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
Comparative network-based analysis of toll-like receptor agonist, L-pampo signaling pathways in immune and cancer cellsScientific reports · 2024
Summary (written by the submitters)
Toll-like receptors (TLRs) are critical immune components to stimulate immune responses against various infections. Recently, TLR agonists have emerged as a promising way to activate anti-tumor immunity. L-pampo, a TLR1/2 and TLR3 agonist, induces robust humoral and cellular immune responses but also causes cancer cell death. In this study, we investigated the L-pampo-induced signal pathways in immune and cancer cells and how they interact with TLRs and cancer-related pathways.
Read the full summary ↓
First, we constructed a template network with differentially expressed genes and influential genes using the weighted gene co-expression network analysis (WGCNA). Next, we obtained perturbed modules using the same method and extracted core subnetworks from the modules by running Walktrap. Finally, we attempted to link the core subnetworks for reconstructing major molecular signals regarding the response to L-pampo utilizing a shortest path finding algorithm, TOPAS. Our analysis revealed that the key molecular processes were PI3K-AKT, JAK-STAT, and oxidative phosphorylation (OXPHOS) with reactive oxygen species (ROS) in immune and cancer cells. Notably, the OXPHOS process may be the crucial regulator for the differential responses of L-pampo between cancer cells of different tissue types. Our computational approaches can be applied to similar research for inferring underlying molecular mechanisms using complex gene expression profiles.
Published in
Comparative network-based analysis of toll-like receptor agonist, L-pampo signaling pathways in immune and cancer cells
Park S, Kim G, Choi A et al. · Scientific reports 2024 · PMID 39060412 · doi:10.1038/s41598-024-67000-1
This dataset
Open GSE264131 in finder → Find more human 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 GSE264131_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 PRJNA1101035 and SRA study SRP502084. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39060412) ↗
Study design
9 conditions, mostly in triplicate
differentiated THP-1 (monocyte) cells, Non-tr… ×3 differentiated THP-1 (monocyte) cells, L-pamp… ×3 differentiated THP-1 (monocyte) cells, L-pamp… ×3 PC-3 (prostate cancer) cells, Non-treatment, … ×3 PC-3 (prostate cancer) cells, L-pampo™ 1x, 3 … ×3 PC-3 (prostate cancer) cells, L-pampo™ 1x, 6 … ×3 SW620 (colon cancer) cells, Non-treatment, 0 … ×3 SW620 (colon cancer) cells, L-pampo™ 1x, 3 ho… ×3 +1 more

Supports a between-group comparison across 27 samples.

How we read this: 9 replicated groups read from 27 sample titles; they account for 27 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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