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Identification of Differentially Expressed Genes and Associated Immune Cell Types In South African Gallbladder Cancer Patients

GSE308395 Homo sapiens (human) RNA-seq (gene expression) 10 samples Submitted 2025/12/04 Platform GPL15520
At a glance

RNA-seq (gene expression) · human · 10 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
Gallbladder cancerThis series is part of our curated briefing on it.
Samples
10 samples: 8 × Gallbladder tumour, patient vs 2 × Normal Gallbladder tissue, patientGroups 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
Identification of differentially expressed genes and associated immune cell types in South African gallbladder cancer patientsScientific reports · 2025
Summary (written by the submitters)
Introduction: Gallbladder cancer (GBC) is a highly aggressive malignancy with limited therapeutic options, particularly in underrepresented populations, including South Africa. Understanding the molecular landscape of GBC may provide novel insights into its pathogenesis and potential therapeutic targets. Molecular changes are known to be associated with GBC, however, there is a paucity of this information especially in African populations.
Read the full summary ↓
Furthermore, within the tumour microenvironment, different immune cells contribute to GBC progression. We investigated gene expression patterns in GBC tumours and their association with different immune cells in a cohort of South African patients. Methods: RNA sequencing was conducted on 2 normal and 8 gallbladder cancer tissues from South African patients (Ethics: M160640) to identify differentially expressed genes. Bioinformatics tools were used for pathway analysis, while immune cell quantification was performed using the quanTIseq software and presented as median [IQR]. Verification studies were further carried out using real-time PCR on an independent cohort comprising 7 gallstone samples and 26 gallbladder tumour samples. Results: A total of 65 genes were found to be significantly differentially expressed between the gallbladder tumours and gallstone controls. We also identified 37 upregulated and 28 downregulated genes in this cohort. Among the most upregulated genes, MUC16 was confirmed to be significantly overexpressed in tumours. Normal tissues exhibited a significantly higher proportion of dysregulated genes associated with B cells (17.132 [14.866–18.483], p<0.0001) and M1 macrophages (18.943 [1.097–36.790], p<0.0001) compared to tumours. In contrast, tumours showed a greater association with dysregulated genes linked to regulatory T cells (Tregs) (14.373 [9.696–20.162]) relative to normal tissues. Pathway analysis further revealed the upregulation of defective GALNT12, defective GALNT3, defective C1GALT1C1 and termination of O-glycan biosynthesis, highlighting key mechanisms potentially involved in tumour progression. Conclusion: The study has shown the dysregulation of key genes in South African gallbladder cancer patients. Specifically, MUC16 was verified to be significantly elevated in tumour samples. Furthermore, the association of these dysregulated genes with key immune cells in this patient group may further highlight their roles in dysfunctional immune processes linked with tumourigenesis.
Published in
Identification of differentially expressed genes and associated immune cell types in South African gallbladder cancer patients
Devar J, Baichan P, Mpangase PT et al. · Scientific reports 2025 · PMID 41219291 · doi:10.1038/s41598-025-23056-1
This dataset
Open GSE308395 in finder → Find more human RNA-seq datasets → Gallbladder 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 GSE308395_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 PRJNA1330335 and SRA study SRP621329. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41219291) ↗
Study design
8 × Gallbladder tumour, patient vs 2 × Normal Gallbladder tissue, patient

Supports a between-group comparison across 10 samples.

How we read this: 2 replicated groups read from 10 sample titles; they account for 10 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 Gallbladder cancer datasets

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