Evidence map›Paper›PMID 40052441›Full record

ReviewBriefings in bioinformatics2025

Integration of proteomics profiling data to facilitate discovery of cancer neoantigens: a survey.

Shifu Luo, Hui Peng, Ying Shi, Jiaxin Cai, Songming Zhang, Ningyi Shao, Jinyan Li

Abstract readReview
In one paragraph

Review in Briefings in bioinformatics, 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. Article
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

7 authors.

Shifu LuoFaculty of Computer Science and Control Engineering, Shenzhen University of Advanced Technology, Shenzhen, 518107, Guangdong, China.ORCID 0000-0001-9735-4670
Hui PengFaculty of Computer Science and Control Engineering, Shenzhen University of Advanced Technology, Shenzhen, 518107, Guangdong, China.ORCID 0000-0002-5137-1827
Ying ShiFaculty of Computer Science and Control Engineering, Shenzhen University of Advanced Technology, Shenzhen, 518107, Guangdong, China.
Jiaxin CaiFaculty of Computer Science and Control Engineering, Shenzhen University of Advanced Technology, Shenzhen, 518107, Guangdong, China.
Songming ZhangFaculty of Computer Science and Control Engineering, Shenzhen University of Advanced Technology, Shenzhen, 518107, Guangdong, China.ORCID 0000-0001-7695-5880
Ningyi ShaoFaculty of Health Sciences, University of Macau, Taipa, Macao SAR 999078, China.
Jinyan LiFaculty of Computer Science and Control Engineering, Shenzhen University of Advanced Technology, Shenzhen, 518107, Guangdong, China.ORCID 0000-0003-1833-7413

Funding

Shenzhen University of Advanced Technology
6 · The paper itself

Abstract

Cancer neoantigens are peptides that originate from alterations in the genome, transcriptome, or proteome. These peptides can elicit cancer-specific T-cell recognition, making them potential candidates for cancer vaccines. The rapid advancement of proteomics technology holds tremendous potential for identifying these neoantigens. Here, we provided an up-to-date survey about database-based search methods and de novo peptide sequencing approaches in proteomics, and we also compared these methods to recommend reliable analytical tools for neoantigen identification. Unlike previous surveys on mass spectrometry-based neoantigen discovery, this survey summarizes the key advancements in de novo peptide sequencing approaches that utilize artificial intelligence. From a comparative study on a dataset of the HepG2 cell line and nine mixed hepatocellular carcinoma proteomics samples, we demonstrated the potential of proteomics for the identification of cancer neoantigens and conducted comparisons of the existing methods to illustrate their limits. Understanding these limits, we suggested a novel workflow for neoantigen discovery as perspectives.

Indexed as

Antigens, NeoplasmNeoplasmsProteomicsCarcinoma, HepatocellularHep G2 CellsHumansLiver NeoplasmsAntigens, Neoplasmcancer neoantigensdatabase-based search methodsdeep learningde novo peptide sequencingproteomics

Identifiers

PMID40052441
PMCPMC11886573

What OpenQuestion holds

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LicenceCC BY-NC
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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.