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Bioinformatic-experimental screening uncovers multiple targets for increase of MHC-I expression through activating the interferon response in breast cancer

GSE275464 Homo sapiens (human) RNA-seq (gene expression) 12 samples Submitted 2025/09/01 Platform GPL29480
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
Breast cancerThis series is part of our curated briefing on it.
Samples
12 samples: 3 × MFC7, CYFIP1 knockout vs 3 × MFC7, NCKAP1 knockout vs 3 × MFC7, PIP5K1A knockout vs 3 × MFC7, NCGroups 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)
Expression of major histocompatibility complex I (MHC-I) on tumor cells is extremely important for antitumor immune response for its essential role in activating various immune cells including tumor-specific CD8+ T cells. Cancers of low MHC-I expression are commonly of little immune cell infiltration and exhibit poor prognosis in clinic.
Read the full summary ↓
In this study, we conducted a bioinformatic-experimental screening to identify potential gene targets to enhance MHC-I expression in breast cancer. Through combination of MHC-I scoring, gene expression correlation analysis, and survival prognostication, we identify 243 genes negatively correlated with MHC-I expression in breast cancer, of which 144 genes were further identified as negative correlation factors for tumor-infiltrated lymphocytes. Finally, we verified partially according to KEGG or gene-dependency analysis in breast cancer MCF7 cells, and figured out multiple genes including PIP5K1A, NCKAP1, CYFIP1, DIS3, TBP and EXOC1 as potent gene targets for increase of MHC-I expression in breast cancer. Mechanistically, knockout each of these genes activates the intrinsic interferon response in breast cancer cells, which not only promoted MHC-I expression but also caused immunogenic death of breast cancer cells. Collectively, we identified multiple gene targets for increase of MHC-I expression in breast cancer.
This dataset
Open GSE275464 in finder → Find more human RNA-seq datasets → Breast 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 GSE275464_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 PRJNA1150990 and SRA study SRP529263. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
3 × MFC7, CYFIP1 knockout vs 3 × MFC7, NCKAP1 knockout vs 3 × MFC7, PIP5K1A knockout vs 3 × MFC7, NC

Supports a between-group comparison across 12 samples.

How we read this: 4 replicated groups read from 12 sample titles; they account for 12 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 Breast cancer 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.