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The PKCα/ZFP64/CSF1 Axis restricts Tumor Response to Anti-PD1 by driving Macrophage M2 Polarization in Hepatocellular Carcinoma [RNA-seq 1]

GSE181946 Homo sapiens (human) RNA-seq (gene expression) 17 samples Submitted 2024/08/04 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 17 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
Hepatocellular carcinomaThis series is part of our curated briefing on it.
Samples
17 samples: 13 distinct titles, 1 of them repeatedGroups 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)
Despite remarkable achievements, majority of hepatocellular carcinoma (HCC) patients fail to respond to anti-PD1 therapy. Here, we showed that ZFP64 was frequently upregulated in HCC tissues of anti-PD1 resistance patients. Elevated ZFP64 levels triggered tumor progression and induced an immunosuppressive microenvironment. Mechanistically, ZFP64 transcriptionally activated colony-stimulating factor 1 (CSF1) by directly binding to its promoter, and secreted CSF1 driving the shift of macrophages into an alternatively activated phenotype.
Read the full summary ↓
Importantly, the PKCα was revealed to phosphorylate ZFP64 at S226, resulting in its nuclear translocation to transcribe the CSF1 gene. Particularly, we proposed firstly that the PKCα/ZFP64/CSF1 axis was a critical pathway in fostering immune evasion and anti-PD1 tolerance and inhibiting this axis with lenvatinib or Gö6976 surmounted the anti-PD1 resistance in HCC. Our study indicates that the ZFP64 is an emerging indicator in predicting anti-PD1 efficacy, and reveals the PKCα/ZFP64/CSF1 axis is a suitable target for anti-PD1 combination therapy in HCC.
This dataset
Open GSE181946 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 GSE181946_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 PRJNA754024 and SRA study SRP332219. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
13 distinct titles, 1 of them repeated
T ×5

How we read this: Read from 17 sample titles: 13 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 Hepatocellular 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.