Evidence map›Paper›PMID 38282471›Full record

ReviewHuman vaccines & immunotherapeutics2024

Recent progress in mRNA cancer vaccines.

Ruhui Yao, Chunyuan Xie, Xiaojun Xia

Open access · goldAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Human vaccines & immunotherapeutics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers.

0numbers the graph read from it
0cells of the map it votes in
50citing papers in PubMed
16.8field-weighted citation impact, top 1% of its field
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

50 citing papers in PubMed, 72 citations in OpenAlex.

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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

3 authors at 2 institutions in 1 country.

Ruhui YaoState Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China.
Chunyuan XieState Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China.
Xiaojun XiaState Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China.
Sun Yat-sen University Cancer Center · CNSun Yat-sen University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The research and development of messenger RNA (mRNA) cancer vaccines have gradually overcome numerous challenges through the application of personalized cancer antigens, structural optimization of mRNA, and the development of alternative RNA-based vectors and efficient targeted delivery vectors. Clinical trials are currently underway for various cancer vaccines that encode tumor-associated antigens (TAAs), tumor-specific antigens (TSAs), or immunomodulators. In this paper, we summarize the optimization of mRNA and the emergence of RNA-based expression vectors in cancer vaccines. We begin by reviewing the advancement and utilization of state-of-the-art targeted lipid nanoparticles (LNPs), followed by presenting the primary classifications and clinical applications of mRNA cancer vaccines. Collectively, mRNA vaccines are emerging as a central focus in cancer immunotherapy, offering the potential to address multiple challenges in cancer treatment, either as standalone therapies or in combination with current cancer treatments.

Indexed as

Cancer VaccinesNanoparticlesNeoplasmsAntigens, NeoplasmHumansmRNA VaccinesRNARNA, MessengerAntigens, NeoplasmCancer VaccinesmRNA VaccinesRNARNA, Messengercancer vaccineslipid nanoparticlesmRNA vaccinepersonalized therapytumor antigens

Identifiers

PMID38282471
PMCPMC10826636
OpenAlexW4391319242

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

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.