Evidence map›Paper›PMID 39148720›Full record

ArticleExploratory research and hypothesis in medicine

Advances in the Clinical Application of High-throughput Proteomics.

Miao Cui, Fei Deng, Mary L Disis, Chao Cheng, Lanjing Zhang

Abstract read
In one paragraph

Article in Exploratory research and hypothesis in medicine. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing 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

18 citing papers in PubMed.

  1. Article
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  5. Review
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  8. Introduction to the Immune System.Methods in molecular biology (Clifton, N.J.) · 2026
    Review
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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

5 authors.

Miao CuiDepartment of Pathology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Fei DengDepartment of Chemical Biology, Ernest Mario School of Pharmacy, Rutgers University, Piscataway, NJ, USA.
Mary L DisisUW Medicine Cancer Vaccine Institute, University of Washington, Seattle, WA, USA.
Chao ChengDepartment of Medicine, Baylor College of Medicine, Houston, TX, USA.
Lanjing ZhangDepartment of Chemical Biology, Ernest Mario School of Pharmacy, Rutgers University, Piscataway, NJ, USA.ORCID 0000-0001-5436-887X

Funding

Screening and confirmatory machine learning for explainable modeling of non-cancer deaths in cancer patientsR37CA277812 · NCI · RUTGERS BIOMEDICAL AND HEALTH SCIENCES · PI Lanjing Zhang · 2022 to 2026
$1.6M
NCI NIH HHS R37 CA277812
6 · The paper itself

Abstract

High-throughput proteomics has become an exciting field and a potential frontier of modern medicine since the early 2000s. While significant progress has been made in the technical aspects of the field, translating proteomics to clinical applications has been challenging. This review summarizes recent advances in clinical applications of high-throughput proteomics and discusses the associated challenges, advantages, and future directions. We focus on research progress and clinical applications of high-throughput proteomics in breast cancer, bladder cancer, laryngeal squamous cell carcinoma, gastric cancer, colorectal cancer, and coronavirus disease 2019. The future application of high-throughput proteomics will face challenges such as varying protein properties, limitations of statistical modeling, technical and logistical difficulties in data deposition, integration, and harmonization, as well as regulatory requirements for clinical validation and considerations. However, there are several noteworthy advantages of high-throughput proteomics, including the identification of novel global protein networks, the discovery of new proteins, and the synergistic incorporation with other omic data. We look forward to participating in and embracing future advances in high-throughput proteomics, such as proteomics-based single-cell biology and its clinical applications, individualized proteomics, pathology informatics, digital pathology, and deep learning models for high-throughput proteomics. Several new proteomic technologies are noteworthy, including data-independent acquisition mass spectrometry, nanopore-based proteomics, 4-D proteomics, and secondary ion mass spectrometry. In summary, we believe high-throughput proteomics will drastically shift the paradigm of translational research, clinical practice, and public health in the near future.

Indexed as

BiomarkersChromatographyIon-mobility spectrometryLiquidMass spectrometryNeoplasmsProtein-protein interaction domainProteomics

Identifiers

PMID39148720
PMCPMC11326426

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.