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Transcriptome and microbiome-immune changes across preinvasive and invasive anal cancer lesions

GSE253560 Homo sapiens (human) RNA-seq (gene expression) 70 samples Submitted 2024/08/04 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 70 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
Anal cancerThis series is part of our curated briefing on it.
Samples
70 samples
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
Transcriptome and microbiome-immune changes across preinvasive and invasive anal cancer lesionsJCI insight · 2024
Summary (written by the submitters)
Anal squamous cell carcinoma (ASCC) is a rare gastrointestinal malignancy that is linked to high-risk Human papillomavirus (HPV) infection. It is often preceded by precursor lesions like Low-Grade Squamous Intraepithelial Lesions (LGSIL) and High-Grade Squamous Intraepithelial Lesions (HGSIL). The incidence of ASCC varies across populations, with heightened risk in HIV-positive individuals.
Read the full summary ↓
In a previous study, we characterized the anal microbiome in high-risk HIV-exposed MSM and TGW subjects. We revealed oncogenic viromes and pertinent bacterial species associated with anal SILs. Our current investigation aimed to delineate transcriptomic and metatranscriptomic changes during the progression from precancerous lesions to ASCC. We collected 70 anal tissue samples across various lesion stages (LGSIL, HGSIL, and ASCC). Our metatranscriptomic analysis revealed that Fusobacterium nucleatum, F. gonidiaformans, Bacteroides fragilis, Campylobacter ureolyticus, and Cribacterium bergeronii were more prevalent in ASCC than in precancerous lesions. These bacterial species contributed with gene encoding enzymes (e.g.: Acca, glyQ, eno, pgk and por) and oncoproteins (FadA and dnaK) revealing potential new markers for diagnosis or treatment approaches. Our unsupervised transcriptome analysis identified two distinct sample clusters based on histological diagnosis, immune infiltrate, HIV and HPV status, and pathway activities such as immune activation, cell cycle, and antiviral signaling that recapitulate the natural history of anal cancer progression. Mutations were observed affecting KMT2C (30%), PIK3CA (21%), EP300 (21%) and NOTCH1 (13%) cancer driver genes among ASCC but also in precancerous lesions. Our study provides insights into the molecular mechanisms governing anal cancer progression, offering valuable information that may help to stratify HGSIL cases with low- or high-risk progression to the malignant stages.
Published in
Transcriptome and microbiome-immune changes across preinvasive and invasive anal cancer lesions
Lacunza E, Fink V, Salas ME et al. · JCI insight 2024 · PMID 39024554 · doi:10.1172/jci.insight.180907
This dataset
Open GSE253560 in finder → Find more human RNA-seq datasets → Anal 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 GSE253560_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 PRJNA1066280 and SRA study SRP484473. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39024554) ↗
Study design
A cohort of 70 individual samples

The grouping that defines any comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Sample titles run Sample_1 … Sample_70 and carry no group information.

Samples in this study

+ 30 more — browse all 70 samples with per-sample file links →

More Anal 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.