GEO series

Dual function of DOT1L suppresses tumor intrinsic immunogenicity in Hepatocellular carcinoma [RNA-seq]

GSE294355 Mus musculus (mouse) RNA-seq (gene expression) 36 samples Submitted 2026/04/16 Platform GPL24676Platform GPL24247

At a glance

RNA-seq (gene expression) · mouse · 36 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: Mus musculus (mouse)
Topic: Hepatocellular carcinomaThis series is part of our curated briefing on it.
Samples: 36 samples: 36 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: Dual function of DOT1L suppresses tumor cell-intrinsic immunogenicity in hepatocellular carcinomaOncogene · 2026

Summary (written by the submitters)

Checkpoint inhibitor therapy for Hepatocellular carcinoma (HCC) remains limited in patients’ overall response rate. Discovery and development of more effective combinatorial approaches in HCC checkpoint immunotherapy is urgent. Here, through CPlSPR/Cas9 genetic screens, we identify DOT1L as a versatile epigenetic factor that functions to suppress anti-tumor immunity through a dual mechanism.

Read the full summary ↓

Depletion of DOT1L induces the expression of transposable elements and subsequent type-I interferon response, and meanwhile lowers ZEB1 levels to further unleash the expression of interferon simulated genes. In turn, we demonstrate that DOT1L loss or treatment with the selective DOT1L inhibitor EPZ-5676 sensitizes tumors to immune checkpoint blockade by increased immune infiltration in mouse. More importantly, EPZ-5676 treatment alone is sufficient to enhance antitumor immunity in humanized mouse model. These findings provide a rationale for targeting DOT1L to improve tumor immunogenicity and overcome immunotherapy resistance.

Published in

Dual function of DOT1L suppresses tumor cell-intrinsic immunogenicity in hepatocellular carcinoma

Xu S, Gong R, Liu S et al. · Oncogene 2026 · PMID 41912776 · doi:10.1038/s41388-026-03744-6

This dataset

Open GSE294355 in finder → Find more RNA-seq datasets → Hepatocellular 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 GSE294355_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 PRJNA1249077 and SRA study SRP578085. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41912776) ↗

Study design

36 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 36 sample titles: 36 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

GSM8902875 HuH-7, sgNeg2_rep1, RNA-seq
GSM8902876 HuH-7, sgNeg2_rep2, RNA-seq
GSM8902877 HuH-7, DOT1L_sgRNA#1_rep1, RNA-seq
GSM8902878 HuH-7, DOT1L_sgRNA#1_rep2, RNA-seq
GSM8902879 HuH-7, DOT1L_sgRNA#2_rep1, RNA-seq
GSM8902880 HuH-7, DOT1L_sgRNA#2_rep2, RNA-seq
GSM8902881 HuH-7, IFN, sgNeg2_rep1, RNA-seq
GSM8902882 HuH-7, IFN, sgNeg2_rep2, RNA-seq
GSM8902883 HuH-7, IFN, DOT1L_sgRNA#1_rep1, RNA-seq
GSM8902884 HuH-7, IFN, DOT1L_sgRNA#1_rep2, RNA-seq
GSM8902885 HuH-7, IFN, DOT1L_sgRNA#2_rep1, RNA-seq
GSM8902886 HuH-7, IFN, DOT1L_sgRNA#2_rep2, RNA-seq
GSM8902887 HuH-7, DMSO_rep1, RNA-seq
GSM8902888 HuH-7, DMSO_rep2, RNA-seq
GSM8902889 HuH-7, DMSO_rep3, RNA-seq
GSM8902890 HuH-7, EPZ-5676_rep1, RNA-seq
GSM8902891 HuH-7, EPZ-5676_rep2, RNA-seq
GSM8902892 HuH-7, EPZ-5676_rep3, RNA-seq
GSM8902893 Hepa1-6, sgNeg2_rep1, RNA-seq
GSM8902894 Hepa1-6, sgNeg2_rep2, RNA-seq
GSM8902895 Hepa1-6, Dot1l_sgRNA#1_rep1, RNA-seq
GSM8902896 Hepa1-6, Dot1l_sgRNA#1_rep2, RNA-seq
GSM8902897 Hepa1-6, Dot1l_sgRNA#2_rep1, RNA-seq
GSM8902898 Hepa1-6, Dot1l_sgRNA#2_rep2, RNA-seq
GSM8902899 Hep53.4, sgNeg2_rep1, RNA-seq
GSM8902900 Hep53.4, sgNeg2_rep2, RNA-seq
GSM8902901 Hep53.4, Dot1l_sgRNA#1_rep1, RNA-seq
GSM8902902 Hep53.4, Dot1l_sgRNA#1_rep2, RNA-seq
GSM8902903 Hep53.4, Dot1l_sgRNA#2_rep1, RNA-seq
GSM8902904 Hep53.4, Dot1l_sgRNA#2_rep2, RNA-seq
GSM8902905 LLC, sgNeg2_rep1, RNA-seq
GSM8902906 LLC, sgNeg2_rep2, RNA-seq
GSM8902907 LLC, Dot1l_sgRNA#1_rep1, RNA-seq
GSM8902908 LLC, Dot1l_sgRNA#1_rep2, RNA-seq
GSM8902909 LLC, Dot1l_sgRNA#2_rep1, RNA-seq
GSM8902910 LLC, Dot1l_sgRNA#2_rep2, RNA-seq

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