GEO series

Dysregulation of mRNA expression by miR-186 overexpression in arsenic-induced skin carcinogenesis

GSE276586 Homo sapiens (human) RNA-seq (gene expression) 24 samples Submitted 2025/01/23 Platform GPL18573

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: 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: Dysregulation of mRNA expression by hsa-miR-186 overexpression in arsenic-induced skin carcinogenesisToxicology and applied pharmacology · 2025

Summary (written by the submitters)

Dysregulated miRNA expression contributes to development of arsenic-induced cutaneous squamous cell carcinoma (cSCC). hsa-miR-186 (called “miR-186” henceforth) is overexpressed in arsenical cSCC tissues as well as in preclinical cell line model of arsenical cSCC. Simultaneous miR-186 overexpression and chronic inorganic trivalent arsenite (iAs; 100 nM) exposure transformed human HaCaT cell line preferentially over miR-186 exposure or iAs exposure alone.

Read the full summary ↓

Both iAs and miR-186 regulate the expression of wide range of mRNA targets. However, how their interaction impact the transcriptome-wide mRNA expression landscape ushering in cancer is unknown. We performed longitudinal RNA-seq analysis in passage-matched HaCaT cells clones (±miR-186 overexpression) with simultaneous chronic iAs exposure (0/100 nM; 29 weeks) at 12 and 29 weeks. We determined the impact of each factor and their interaction towards differential gene expression and pathways dysregulation employing to different statistical approaches (t-statistic and 2-factor ANOVA). We show that a core set of pathways are dysregulated deterministically irrespective of the statistical approach chosen, possibly representing necessary changes for transformation. Our data suggests that each clonal line could take unique routes to dysregulate these core set of pathways necessary for transformation, highlighting the possible role of stochasticity in cancer development. We provide evidence to sift the strengths of weaknesses of each statistical methodology in providing biological understanding of events that play crucial role in carcinogenesis in large datasets with multiple contributing variables.

Published in

Dysregulation of mRNA expression by hsa-miR-186 overexpression in arsenic-induced skin carcinogenesis

Banerjee M, Lykoudi A, Hwang JY et al. · Toxicology and applied pharmacology 2025 · PMID 39719251 · doi:10.1016/j.taap.2024.117209

This dataset

Open GSE276586 in finder → Find more human RNA-seq 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 GSE276586_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 PRJNA1157884 and SRA study SRP531206. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39719251) ↗

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

GSM8502642 Vector+0nM replicate 1, 12 week
GSM8502643 Vector+100 nM replicate 1, 12 week
GSM8502644 Vector+0nM replicate 2, 12 week
GSM8502645 Vector+100 nM replicate 2, 12 week
GSM8502646 Vector+0nM replicate 3, 12 week
GSM8502647 Vector+100 nM replicate 3, 12 week
GSM8502648 miR-186+0 nM replicate 1, 12 week
GSM8502649 miR-186+100 nM replicate 1, 12 week
GSM8502650 miR-186+0 nM replicate 2, 12 week
GSM8502651 miR-186+100 nM replicate 2, 12 week
GSM8502652 miR-186+0 nM replicate 3, 12 week
GSM8502653 miR-186+100 nM replicate 3, 12 week
GSM8502654 Vector+0nM replicate 1, 29 week
GSM8502655 Vector+100 nM replicate 1, 29 week
GSM8502656 Vector+0nM replicate 2, 29 week
GSM8502657 Vector+100 nM replicate 2, 29 week
GSM8502658 Vector+0nM replicate 3, 29 week
GSM8502659 Vector+100 nM replicate 3, 29 week
GSM8502660 miR-186+0 nM replicate 1, 29 week
GSM8502661 miR-186+100 nM replicate 1, 29 week
GSM8502662 miR-186+0 nM replicate 2, 29 week
GSM8502663 miR-186+100 nM replicate 2, 29 week
GSM8502664 miR-186+0 nM replicate 3, 29 week
GSM8502665 miR-186+100 nM replicate 3, 29 week

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GSE271108 PRAME-mediated retinoid resistance in keratinocyte carcinomas is overcome by EZH2 inhibition 36 samples same disease briefing
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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.