GEO series

Transcriptomic signatures in peripheral CD4 + T-lymphocytes may reflect melanoma staging and immunotherapy responsiveness prior to ICI initiation

GSE292798 Homo sapiens (human) RNA-seq (gene expression) 24 samples Submitted 2025/03/27 Platform GPL21697

At a glance

RNA-seq (gene expression) · human · 24 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: MelanomaThis series is part of our curated briefing on it.
Samples: 24 samples: 24 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: Transcriptomic signatures in peripheral CD4(+)T-lymphocytes may reflect melanoma staging and immunotherapy responsiveness prior to ICI initiationFrontiers in immunology · 2025

Summary (written by the submitters)

Background and Purpose: Promoting adaptive immunity with ICIs has drastically improved melanoma prognosis, but not for all patients. Some cases relapse in the first few months, while others keep durable benefit, even after immunotherapy discontinuation. To identify cellular/molecular signatures in peripheral blood that could differentiate advanced from metastatic melanoma and predict dynamics for primary/secondary immune escape, we examined 100 consecutive patients with stage III/IV melanoma scheduled to start ICIs .

Read the full summary ↓

Materials and Methods: At melanoma diagnosis, a multiparameter flow cytometric analysis and purification scheme using standard conjugated antibodies were performed for all individuals prior to ICI initiation. In each stage (III/IV) according to their RFS/PFS, we retrospectively selected the cases with the clearest clinical outcomes and focused our analysis on the extreme responders (n=7) and non-responders (n=7) to characterize the transcriptomes of circulating CD4+T-cells by bulk RNA-seq, Differential Expression Analysis(DEA)and Gene Ontology (GO) enrichment analysis. Based on our selected patient cohort, we examined for differentially expressed genes (DEGs) and key-pathways that appear preferentially activated in stage III vs. IV melanoma, and in long vs. short immunotherapy responders.Results: Although circulating immune-cells did not numerically differ in both sets of analysis(staging and ICI responsiveness), DEA and GO data showed that patients could be clustered separately, identifying 189vs.92 DEGs in stage IV/III and 101vs.47 DEGs in early progressors/long responders. These DEGs were functionally implicated in distinct pathways. For metastatic cases: inflammatory response (log p-value=-9.2: ADGRE5/2,CYBA,GRN,HMOX1,IRF5,ITGAM), adaptive immunity(log p-value=-7.7: CD1C,CD74,CYBB,NCF2,CTSA,S100A8/9,BCL3,FCER1G), T-cell activation(log p-value=-6.3: BCL3,CD1C,CD74,FCER1G,FGL2)and lipid metabolism/catabolism(log p-value=-2.5/-2.6: ARF3,GPX1,MVD,OCRL,PCCB,CTSA,PNPLA2,NAGLU,GBA2,ABHD4); while in early- progressors to ICIs: immune effector processing(log p-value=-13.7: BCL6,FGR,HLA-DQA1/DQB1,HLA-DRA,HLA- DRB1/DRB5,NKG7,SLC11A1,TYROBP,SPON2,HAVCR2),PD-1(log p-value=-10.2: HLA-DQA1/DQB1,HLA-DRA,HLA-DRB1/DRB5)and IFN signaling(logp-value=-8.5: HLA-DQA1/DQB1,HLA-DRA,HLA-DRB1/DRB5,NCAM1,IFITM3),positive regulation of T-cell activation(log p- value=-7.7: BCL6,HLA-DQA1/DQB1,HLA-DRA,HLA-DRB1/DRB5,SASH3,HAVCR2)and CD28 co-stimulation(log p-value=-10.3: HLA- DQA1/DQB1,HLA-DRA,HLA-DRB1/DRB5), supporting an immune-mediated behavior. Conclusions: Specific pathways and marker genes in the peripheral CD4+T-cells may predetermine melanoma staging and immunotherapy resistance.

Published in

Transcriptomic signatures in peripheral CD4(+)T-lymphocytes may reflect melanoma staging and immunotherapy responsiveness prior to ICI initiation

Palli E, Lavigne M, Verginis P et al. · Frontiers in immunology 2025 · PMID 40226614 · doi:10.3389/fimmu.2025.1529707

This dataset

Open GSE292798 in finder → Find more human RNA-seq datasets → Melanoma 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 GSE292798_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 PRJNA1241275 and SRA study SRP572820. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40226614) ↗

Study design

24 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 24 sample titles: 24 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

GSM8865466 M6,S4L
GSM8865467 M8,S4L
GSM8865468 M9,S4L
GSM8865469 M12,S4S
GSM8865470 M43,S3S
GSM8865471 M51,S4L
GSM8865472 M52,S4L
GSM8865473 M53,S3L
GSM8865474 M55,S3S
GSM8865475 M59,S3L
GSM8865476 M63,S3L
GSM8865477 M64,S4S
GSM8865478 M68,S3S
GSM8865479 M69,S3L
GSM8865480 M73,S4S
GSM8865481 M79,S3S
GSM8865482 M82,S3S
GSM8865483 M86,S4S
GSM8865484 M87,S4S
GSM8865485 M91,S3S
GSM8865486 M95,S4S
GSM8865487 M97,S3S
GSM8865488 M101,S4L
GSM8865489 M102,S4L

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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.