ReviewBriefings in bioinformatics2025
Integration of proteomics profiling data to facilitate discovery of cancer neoantigens: a survey.
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.
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.
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.
Who cites it
2 citing papers in PubMed.
- Review
- Screening Potential Diagnostic Biomarkers of Lung Adenocarcinoma Related to Immune Infiltration Based on Single-Cell Proteomic Integration Analysis.Canadian respiratory journal · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
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.
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Registered trials
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.