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EBV infection in epithelial malignancies induces resistance to the anti-tumor function of natural killer cells via CD73-mediated metabolism of AMP [cell line]

GSE309452 Homo sapiens (human) RNA-seq (gene expression) 12 samples Submitted 2026/07/23 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 12 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
Nasopharyngeal carcinomaThis series is part of our curated briefing on it.
Samples
12 samples: 12 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.
Summary (written by the submitters)
CD73 is a metabolic immune checkpoint that regulates the homeostatic balance of adenosine levels in the tumor microenvironment (TME). However, the regulation of CD73 abnormal expression by Epstein-Barr virus (EBV) in EBV-associated malignancies and its immune editing mechanism on natural killer (NK) cells remains unclear. In this study, we demonstrate that CD73 is upregulated in EBV-associated malignancies and is negatively correlated with the anti-tumor activity of NK cells.
Read the full summary ↓
Mechanistically, the EBV latent protein LMP2A upregulates CD73 expression through the PI3K/AKT signaling pathway, and the metabolic product of CD73, adenosine, negatively regulates the anti-tumor function of NK cells through multiple signaling pathways. Furthermore, targeted knockout of CD73, small molecule inhibitor, or neutralizing antibody combined with adoptive transfer of NK cells significantly improved the treatment efficacy for EBV-positive nasopharyngeal carcinoma (NPC). Importantly, we also found abnormal upregulation of CD73 in EBV-positive lymphoma, gastric cancer and lung cancer. In summary, our research identifies the mechanism of immune evasion of NK cell anti-tumor function in EBV-associated malignancies through the abnormal expression of CD73, and targeting CD73 combined with adoptive transfer of NK cells may provide a new strategy for immunotherapy of EBV-related malignancies.
This dataset
Open GSE309452 in finder → Find more human RNA-seq datasets → Nasopharyngeal 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 GSE309452_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 PRJNA1336105 and SRA study SRP629518. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
12 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 12 sample titles: 12 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 Nasopharyngeal carcinoma datasets

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.