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IVT-Free, in silico-synthesized protein-encoding RNA Oligonucleotides for rapid production of personalized cancer vaccines

GSE305709 Homo sapiens (human) RNA-seq (gene expression) 21 samples Submitted 2026/07/03 Platform GPL24247Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 21 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
21 samples: 21 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)
Personalized mRNA neoantigen vaccines demonstrate great potential in cancer therapy, but their customization typically requires more than three months, risking the loss of the optimal therapeutic window for patients. This delay is primarily due to the reliance of current mRNA vaccine production on plasmid fermentation and in vitro transcription (IVT), which involve multiple complex steps.
Read the full summary ↓
Chemically synthesized RNA oligonucleotides, such as antisense oligonucleotides (ASOs), are produced sparing DNA templates or IVT, thus enabling rapid manufacturing. However, ASOs are limited in length, which precludes their ability to encode proteins. Here, we introduced a 39 nucleotides cap-independent translation enhancer (CITE) element termed BBV that can drive RNA translation. Furthermore, BBV was compatible with efficient rolling circle translation (RCT). We chemically synthesized RNA oligonucleotides containing BBV and gene of interest (GOI) with characteristic 5’-OH and 3’-P termini, which could undergo circularization by endogenous RtcB RNA ligases and efficiently encode proteins through RCT in mammalian cells. We designated these RNA oligonucleotides as Protein-Encoding RNA Oligonucleotides (PEOs). Notably, compared with IVT-produced RNAs, PEOs contained undetectable levels of proinflammatory double-stranded RNAs and exhibited minimal immunogenicity. We further demonstrated that PEO-OVA (encoding OVA antigens) significantly inhibited tumor growth comparable to mRNA vaccine. In an orthotopic glioma model, PEO vaccine also exhibited therapeutic benefits with checkpoint blockade therapy. This study establishes an IVT-free RNA vaccine platform that enables rapid, safe, and highly druggable manufacturing of personalized cancer vaccines, offering substantial potential for clinical application.
This dataset
Open GSE305709 in finder → Find more 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 GSE305709_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 PRJNA1307504 and SRA study SRP609763. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
21 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 21 sample titles: 21 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
Datasets on a similar topic

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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.