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Single-cell RNA sequencing reveals B cell-related immunosuppressive landscape and a potential suppressor in hepatocellular carcinoma

GSE299340 Homo sapiens (human) Single-cell RNA-seq 10 samples Submitted 2025/08/06 Platform GPL11154
At a glance

Single-cell RNA-seq · human · 10 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism
Homo sapiens (human)
Topic
Hepatocellular carcinomaThis series is part of our curated briefing on it.
Samples
10 samples: 9 × CR-HlgGroups 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
Single-cell RNA sequencing reveals a B cell-related immunosuppressive landscape and a potential suppressor in hepatocellular carcinomaJournal of translational medicine · 2025
Summary (written by the submitters)
Background Sophisticated tumor microenvironment is responsible for malignant progression and poor prognosis of hepatocellular carcinoma (HCC) patients. Discovering new therapeutic targets was desired for preferable treatments of HCC patients. Methods To uncover the HCC microenvironment, single-cell transcriptomes of HCC tissues and corresponding non-cancerous tissues were analyzed.
Read the full summary ↓
Differentially expressed genes (DEGs) and enriched pathways were analyzed in B cells. Moreover, heterogeneity between malignant and normal hepatocytes was further investigated, which revealed potential biomarkers in HCC progression. The candidate biomarkers were further explored in datasets from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases. Of which, serum amyloid A2 (SAA2) was found out and further validated in HCC tissues by immunohistochemistry (IHC) and western blot. The biological roles of SAA2 were further investigations in HCC cells. Results The results suggested that B cells in HCC tissues was significantly decreased compared to non-cancerous tissues, which might result in the immunosuppressive status of HCC microenvironment. Following differentially expressed genes (DEGs) and functional enriched analysis indicated B cells might participate in immunosuppression of HCC by regulating lipid metabolism. Further analysis on hepatocytes found out highly expressed genes in normal hepatocytes derived from non-cancerous liver tissues, which were validated in datasets from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases. Interestingly, we found serum amyloid A2 (SAA2) was highly expressed in normal liver tissues compared to HCC tissues. The results were validated in clinical HCC specimens by immunohistochemistry (IHC) and western blot assays. Moreover, investigations in HCC cells revealed SAA2 acted as a tumor suppressor in HCC progression. Conclusions Taken together, our study might be of great significance for revealing B cell-related immunosuppressive landscape in HCC, as well as discovering SAA2 as a novel suppressor in HCC, which made great sense for better understanding of HCC landscape and offered a promising therapeutic target for HCC patients. Keywords: B cell-related immunosuppressive landscape, hepatocellular carcinoma, serum amyloid A2, single-cell RNA sequencing, suppressor, tumor microenvironment
Published in
Single-cell RNA sequencing reveals a B cell-related immunosuppressive landscape and a potential suppressor in hepatocellular carcinoma
Hou W, Jiang J, Zhu L et al. · Journal of translational medicine 2025 · PMID 40721811 · doi:10.1186/s12967-025-06869-6
This dataset
Open GSE299340 in finder → Find more human 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 GSE299340_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: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

Also filed as BioProject PRJNA1273939 and SRA study SRP590980. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40721811) ↗
Study design
9 × CR-Hlg

Supports a between-group comparison across 9 samples.

How we read this: 1 replicated groups read from 10 sample titles; they account for 9 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.

Samples in this study
More Hepatocellular carcinoma datasets

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