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Spatial dynamics links PD-L1 and Tumor-Associated Macrophage–Enriched Niches to Immune and Mesenchymal States in microsatellite stable colorectal cancer

GSE319722 Homo sapiens (human) RNA-seq (gene expression) 117 samples Submitted 2026/05/11 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 117 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
Colorectal cancerThis series is part of our curated briefing on it.
Samples
117 samples: 50 × 16B vs 30 × 17B vs 24 × 12B vs 12 × 13BGroups 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
Spatial Dynamics Links PD-L1 and Tumor-Associated Macrophage-Enriched Niches to Immune and Mesenchymal States in Microsatellite-Stable Colorectal CancerCancers · 2026
Summary (written by the submitters)
Microsatellite-stable colorectal cancer comprises a heterogeneous group of tumors generally considered “immune cold” due to limited neoantigen generation and T-cell exclusion or inactivation. Current evidence indicates that the composition of T and B immune cells within the tumor microenvironment represents a prognostically relevant factor, significantly associated with both tumor expression profiles and molecular subtypes.
Read the full summary ↓
We conducted an exploratory analysis to identify prognostically relevant immune cell components in this group of tumors and to investigate corresponding differences in RNA-based bulk expression and high-resolution spatial transcriptomic profiles. A total of 254 cases of localized mismatch repair–proficient colorectal cancer cases were evaluated. Our findings revealed PD-L1 expression as a robust, independent prognostic biomarker associated with favorable outcomes in this specific population. Bulk RNA expression analysis showed that PD-L1–negative tumors exhibited an expression profile consistent with abundant cancer-associated fibroblast infiltration, increased matrix stiffness, and impaired immune activation—features aligned with tumor progression and poorer clinical outcomes. In contrast, PD-L1–positive tumors displayed stromal programs enriched in immune activation and controlled remodeling, consistent with an immunologically active microenvironment. Spatial transcriptomics added an additional layer of evidence, revealing that epithelial-to-mesenchymal transition–related programs can dominate stromal niches in PD-L1–negative tumors, particularly within macrophage-enriched stromal regions. In conclusion, our observations suggest a crosstalk link between PD-L1 expression on immune cells and immune-activated vs mesenchymal-dominant states driven within tumor-associated macrophage-enriched stromal niches. These results provide insight into the biological mechanisms underlying disease progression and highlight tumor-associated macrophages as a potential therapeutic target to overcome immune resistance particularly in PD-L1–negative MSS-CRC tumors.
Published in
Spatial Dynamics Links PD-L1 and Tumor-Associated Macrophage-Enriched Niches to Immune and Mesenchymal States in Microsatellite-Stable Colorectal Cancer
Palomar de Lucas B, Ortega M, Camblor DG et al. · Cancers 2026 · PMID 42073611 · doi:10.3390/cancers18081288
This dataset
Open GSE319722 in finder → Find more human RNA-seq datasets → Colorectal 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 GSE319722_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 PRJNA1424996 and SRA study SRP677818. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42073611) ↗
Study design
50 × 16B vs 30 × 17B vs 24 × 12B vs 12 × 13B

Supports a between-group comparison across 116 samples.

How we read this: 4 replicated groups read from 117 sample titles; they account for 116 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

+ 77 more — browse all 117 samples with per-sample file links →

More Colorectal cancer datasets

Search all human RNA-seq datasets in GEO →

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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.