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Inhibition of PCSK9 with evolocumab modulates lipoproteins and monocyte activation in high-risk ASCVD subjects

GSE254732 Homo sapiens (human) RNA-seq (gene expression) 215 samples Submitted 2025/01/11 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 215 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)
Samples
215 samples: 215 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)
Abstract: BACKGROUND: Mechanistic studies suggest that proprotein convertase subtilisin/kexin type 9 monoclonal antibody inhibitors (PCSK9mAb) not only reduce low density lipoprotein cholesterol (LDL-C) but can also modulate inflammation. OBJECTIVES: This trial was designed to define the impact of evolocumab on circulating immune cell properties in high-risk ASCVD subjects.
Read the full summary ↓
METHODS: Double-blind, placebo-controlled trial randomized 41 ASCVD subjects with type 2 diabetes with microalbuminuria and LDL-C level of >70 mg/dL on maximum tolerated statin therapy to receive either subcutaneous evolocumab 420 mg every 4 weeks or matching placebo. The primary outcomes were the change in lipoproteins, blood viscosity, and circulating immune cell transcriptional response at 2 weeks and 12 weeks with evolocumab compared with matching placebo. Safety was assessed in all subjects who received at least one dose of assigned treatment and analyses were conducted in the intention-to-treat population. RESULTS: All 41 randomized subjects completed the 2-week visit. Six subjects (4 evolocumab, 2 placebo) did not receive study medication consistently after the 2-week visit due to suspension of research activities during the COVID-19 pandemic. The groups were well-matched with respect to age, comorbidities, baseline LDL-C, white blood cell counts, and markers of systemic inflammation. Evolocumab reduced LDL-C by -68.8% (p<0.0001) and -52.8% (p<0.0001) at 2 and 12 weeks respectively. There were no differences in blood viscosity at baseline nor at 2 and 12 weeks. RNA-seq was performed on peripheral blood mononuclear cells (PBMC) with and without TLR4 stimulation (“Stress” transcriptomics). There were no group differences in transcriptional profiles in unstimulated PBMC; however, “stress” transcriptomics unmasked immune cell phenotypic differences between evolocumab and placebo groups at 2 and 12 weeks. CONCLUSIONS: This trial is the first to demonstrate that PCSK9 mAB with evolocumab can modulate circulating immune cell properties and highlights the importance of “stress” profiling of circulating immune cells that more clearly define immune contributions to ASCVD. (Funded by Amgen Inc.; ClinicalTrials.gov number, NCT03829046).
This dataset
Open GSE254732 in finder → Find more human RNA-seq datasets →
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 GSE254732_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 PRJNA1071588 and SRA study SRP487173. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
215 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 215 sample titles: 215 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

+ 175 more — browse all 215 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

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