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Spatially resolved whole-transcriptomic and proteomic profiling of lung cancer and immune-microenvironment according to PD-L1 expression

GSE265899 Homo sapiens (human) RNA-seq (gene expression) 95 samples Submitted 2024/08/21 Platform GPL21697
At a glance

RNA-seq (gene expression) · human · 95 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
95 samples: 95 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.
Summary (written by the submitters)
The expression of PD-L1 on tumor cells (TCs) is used as an immunotherapy biomarker in lung cancer, but heterogeneous intratumoral expression is often observed. Using a Digital Spatial Profiler, we performed proteomic and whole-transcriptomic analyses of TCs and immune cells (ICs) in spatially matched areas according to tumor PD-L1 expression and the status of the immune microenvironment.
Read the full summary ↓
Several findings were validated using immunohistochemistry, The Cancer Genome Atlas, and immunotherapy cohorts. ICs in areas with high PD-L1 expression showed more features indicative of immunosuppression and exhaustion than ICs in areas with low PD-L1 expression. TCs highly expressing PD-L1 within immune-inflamed (IF) areas show up-regulation of pro-inflammatory processes, whereas TCs highly expressing PD-L1 within immune-deficient (ID) areas show up-regulation of various metabolic processes. Using differentially expressed genes of TCs between the IF and ID areas, we identified a novel prognostic gene signature for lung cancer. In addition, the high ratio of CD8+ cells to M2 macrophages was found to predict favorable outcomes in patients with PD-L1-expressing lung cancer after immune checkpoint inhibitor therapy. This study demonstrates that TCs and ICs have distinct spatial features within the tumor microenvironment that are related to tumoral PD-L1 expression and IC infiltration.
This dataset
Open GSE265899 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 GSE265899_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 PRJNA1104638 and SRA study SRP504085. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
95 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 95 sample titles: 95 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

+ 55 more — browse all 95 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.