Immune profiling of premalignant lesions in patients with Peutz-Jeghers Syndrome
RNA-seq (gene expression) · human · 29 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
- 29 samples: 9 × ConA vs 9 × FAP vs 6 × PJS vs 5 × ConBGroups 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
- Immune profiling of premalignant lesions in patients with Peutz-Jeghers syndromeUnited European gastroenterology journal · 2025
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.
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.
Supplementary files
The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE196711_RAW.tar (it can run to tens of gigabytes).
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.
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 PRJNA806947 and SRA study SRP359786. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 29 samples.
How we read this: 4 replicated groups read from 29 sample titles; they account for 29 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.
- GSM5899146 ConA1 ConA
- GSM5899147 ConA2 ConA
- GSM5899148 ConA3 ConA
- GSM5899149 ConA4 ConA
- GSM5899150 ConA5 ConA
- GSM5899151 ConA6 ConA
- GSM5899152 ConA7 ConA
- GSM5899153 ConA8 ConA
- GSM5899154 ConA9 ConA
- GSM5899155 ConB1 ConB
- GSM5899156 ConB2 ConB
- GSM5899157 ConB3 ConB
- GSM5899158 ConB4 ConB
- GSM5899159 ConB5 ConB
- GSM5899160 FAP1 FAP
- GSM5899161 FAP2 FAP
- GSM5899162 FAP3 FAP
- GSM5899163 FAP4 FAP
- GSM5899164 FAP5 FAP
- GSM5899165 FAP6 FAP
- GSM5899166 FAP7 FAP
- GSM5899167 FAP8 FAP
- GSM5899168 FAP9 FAP
- GSM5899169 PJS1 PJS
- GSM5899170 PJS2 PJS
- GSM5899171 PJS3 PJS
- GSM5899172 PJS4 PJS
- GSM5899173 PJS5 PJS
- GSM5899174 PJS6 PJS
- GSE320389 Attention-based deep learning for analysis of pathology images and gene expression data in lung squamous premalignant lesions 215 samples shares premalignant, lesions
- GSE319666 Archetype analysis of lung adenocarcinoma premalignancy links heterogeneity in premalignant lesions to diverging features of invasive disease 177 samples shares premalignant, lesions
- GSE320381 Attention-based deep learning for analysis of pathology images and gene expression data in lung squamous premalignant lesions [HTAN cohort] 145 samples shares premalignant, lesions
- GSE300258 Attention-based deep learning for analysis of pathology images and gene expression data in lung squamous premalignant lesions [DECAMP cohort] 70 samples shares premalignant, lesions
- GSE134520 Dissecting the single-cell transcriptome network underlying gastric premalignant lesions and early gastric cancer [10x genomics] 13 samples shares premalignant, lesions
- GSE253560 Transcriptome and microbiome-immune changes across preinvasive and invasive anal cancer lesions 70 samples shares lesions
- GSE245639 VEXAS syndrome, myelodysplasia cutis and sweet syndrome skin lesions share a common transcriptomic profile led by interferon signaling 25 samples shares lesions
- GSE330007 Transcriptomic Analysis Reveals Molecular Signatures Distinguishing Vocal Fold Leukoplakia from Benign Lesions: Synergistic Dysregulation of Chronic Inflammation, Immune Dysfunction, and Epithelial Barrier Disruption 19 samples shares lesions
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.