Evidence map›Paper›PMID 41208966›Full record

ArticleFrontiers in immunology2025

A novel multi-epitope mRNA vaccine against colorectal cancer: in silico design and immune efficacy profiling.

Lin Wang, Xiaofei Zhou, Yu Wei, Jianping Lin

Abstract read
In one paragraph

Article in Frontiers in immunology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Lin WangState Key Laboratory of Medicinal Chemical Biology, Nankai University, Tianjin, China.
Xiaofei ZhouTianjin BioAI-global Technology Co., Ltd, Tianjin, China.
Yu WeiTianjin BioAI-global Technology Co., Ltd, Tianjin, China.
Jianping LinState Key Laboratory of Medicinal Chemical Biology, Nankai University, Tianjin, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Colorectal cancer (CRC) has emerged as a growing global health challenge, while immunotherapy, particularly mRNA-based cancer vaccines, has emerged as a promising approach due to its ability to induce targeted immune responses with minimal systemic toxicity. This study aimed to design a multi-epitope mRNA vaccine targeting tumor-specific antigens (TSAs) as a cancer therapeutic regimen. Results: We chose six CRC-specific TSAs and selected their appropriate epitopes with immunoinformatic tools. In order to enhance the vaccine stability, we subsequently optimized the open reading frame (ORF) sequences, which demonstrated the highest structural stability among all evaluated approaches. Furthermore, we built a CNN model combined with RNA large language model (RNA-FM) embeddings to screen 212 candidate 5'UTR sequences and identify variants that boost the vaccine's translational efficiency. Finally, in silico immune simulations confirmed the vaccine's ability to elicit robust humoral and cellular immune responses. Conclusion: This study presents an in silico designed mRNA vaccine against colorectal cancer (CRC). Immune simulations demonstrated that this mRNA vaccine can elicit strong antitumor immune responses, indicating it is an effective and promising candidate that warrants further

Indexed as

Antigens, NeoplasmCancer VaccinesColorectal NeoplasmsEpitopesmRNA VaccinesRNA, MessengerAnimalsComputer SimulationEpitopes, T-LymphocyteHumansMiceVaccines, SyntheticAntigens, NeoplasmCancer VaccinesEpitopesEpitopes, T-LymphocytemRNA VaccinesRNA, MessengerVaccines, Syntheticcodon optimizationcolorectal cancerepitopesMHC allelemRNA vaccine

Identifiers

PMID41208966
PMCPMC12589000

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.