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Research on Differences and Immune Infiltration-Related Macrophages Between Non-Small Cell Lung Cancer and Adjacent Tissues

GSE268175 Homo sapiens (human) RNA-seq (gene expression) 67 samples Submitted 2024/05/29 Platform GPL16791
At a glance

RNA-seq (gene expression) · human · 67 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)
Topic
Lung cancerThis series is part of our curated briefing on it.
Samples
67 samples: 34 × Cells of NSCLC, patient vs 33 × Adjacent non-cancerous cells of NSCLC, patientGroups 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
SIRPG promotes lung squamous cell carcinoma pathogenesis via M1 macrophages: a multi-omics study integrating data and Mendelian randomizationFrontiers in oncology · 2024
Summary (written by the submitters)
Non-Small Cell Lung Cancer (NSCLC) is a highly lethal respiratory tumor with unclear molecular mechanisms. RNA-seq data from 34 early-stage samples and adjacent non-cancerous tissues identified 1088 differentially expressed genes using Deseq2. Mendelian randomization (MR) analysis of 26,153 genes and 63,053 LUSC patients, including 7,838,805 SNPs, identified 213 potential exposure factors.
Read the full summary ↓
Five differentially expressed genes (GYPE, PODXL2, RNF182, SIRPG, WNT7A) were identified, with PODXL2 as a risk factor. GO and KEGG analyses highlighted the mTOR and Wnt signaling pathways. Immune infiltration analysis showed decreased Plasma cells, Tregs, and activated Dendritic cells, but increased Macrophages M1. External validation using TCGA and 11 GEO datasets confirmed SIRPG's role in LUSC. SIRPG is a significant exposure risk factor for LUSC, with Macrophages M1 and the mTOR signaling pathway playing crucial roles.
Published in
SIRPG promotes lung squamous cell carcinoma pathogenesis via M1 macrophages: a multi-omics study integrating data and Mendelian randomization
Mao G, Li J, Wang N et al. · Frontiers in oncology 2024 · PMID 38894865 · doi:10.3389/fonc.2024.1392417
This dataset
Open GSE268175 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 GSE268175_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 PRJNA1114883 and SRA study SRP509326. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 38894865) ↗
Study design
34 × Cells of NSCLC, patient vs 33 × Adjacent non-cancerous cells of NSCLC, patient

Supports a between-group comparison across 67 samples.

How we read this: 2 replicated groups read from 67 sample titles; they account for 67 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

+ 27 more — browse all 67 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.