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RNA sequencing data showing influence of p62 mRNA knockout on gene and miRNA expression

GSE294764 Homo sapiens (human) Non-coding RNA profiling 24 samples Submitted 2025/07/09 Platform GPL24676
At a glance

Non-coding RNA profiling · human · 24 samples

What it is
Non-coding RNA profilingLevels of RNAs that do not code for proteins, such as miRNA or lncRNA.
Organism
Homo sapiens (human)
Topic
Cutaneous squamous cell carcinomaThis 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
p62 mRNA suppresses NLRP1 expression in cutaneous SCC cells through miR-34a-5pCell death & disease · 2025
Summary (written by the submitters)
The inflammasome sensor NLRP1 is mainly expressed by epithelial cells including keratinocytes of human skin. Germline gain-of-function mutations in NLRP1 cause inflammatory skin syndromes and predispose patients to the development of cutaneous squamous cell carcinomas (cSCCs), a major type of skin cancer originating from keratinocytes.
Read the full summary ↓
However, expression of NLRP1 is strongly reduced in cSCCs suggesting a complex role of the NLRP1 inflammasome in the development of this type of skin cancer. Suppression of NLRP1 expression in SCC cells is partially caused by an increase in p62 (SQSTM1), a cargo receptor for autophagy-dependent protein degradation. p62 is upregulated in numerous types of cancer and plays key roles in tumor development by activating different pathways. Here, we characterized the molecular mechanisms underlying suppression of NLRP1 expression by p62 in cSCCs. In SCC cells, NLRP1 activation is rescued by a knockdown or knockout of p62 mRNA and, consequently, protein expression, rather than by a knockout of p62 protein expression only. As these experiments suggest a regulation of NLRP1 by the p62 mRNA, we characterized p62 mRNA-regulated gene expression in SCC cells through RNA sequencing. In addition to mRNAs, we identified several differentially regulated microRNAs (miRs), including miR-34a-5p. These short non-coding RNAs regulate the stability or translation of mRNAs in a dynamic manner and a single miR can target multiple mRNAs. miR-34a-5p is an established tumor suppressor in different types of cancer and its expression is also downregulated in cSCCs. Although miR-34a-5p seems to bind neither p62 nor NLRP1 mRNA directly, it increases NLRP1 expression, most likely through a complex mechanism at the RNA level. In summary, our findings revealed a novel pathway regulating suppression of the inflammasome sensor NLRP1 in SCC cells by p62, which occurs at the mRNA level and is mediated by miRs, including the tumor suppressive miR-34a-5p. p62 mRNA, but not protein, regulates expression of other mRNAs and miRNAs. Differential expression analysis of RNA-seq data from SCC12 cells with a protein (p62.1) or mRNA (p62.2K) knockout, and control cells were performed. CRISPR/Cas9 knockout (p62.1 - protein only) or CRISPR/dCas9-KRAB knockout (p62.2K - mRNA and protein) of p62 in SCC12 cells
Published in
p62 mRNA suppresses NLRP1 expression in cutaneous SCC cells through miR-34a-5p
Hennig P, Turko P, Di Filippo M et al. · Cell death & disease 2025 · PMID 40593496 · doi:10.1038/s41419-025-07785-9
This dataset
Open GSE294764 in finder → Find more human datasets → Cutaneous squamous cell carcinoma 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 GSE294764_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 ↗

Also filed as BioProject PRJNA1251576 and SRA study SRP579200. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40593496) ↗
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
More Cutaneous squamous cell carcinoma datasets

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