Development of Parallel Reaction Monitoring Assays for the Detection of Aggressive Prostate Cancer Using Urinary Glycoproteins-Reference-Cited by-同舟云学术

Development of Parallel Reaction Monitoring Assays for the Detection of Aggressive Prostate Cancer Using Urinary Glycoproteins

Published:2021-06-09 Issue:7 Volume:20 Page:3590-3599
ISSN:1535-3893
Container-title:Journal of Proteome Research
language:en
Short-container-title:J. Proteome Res.

Author:

Dong Mingming¹^ORCID,Lih Tung-Shing Mamie¹,Höti Naseruddin¹,Chen Shao-Yung¹²,Ponce Sean¹²,Partin Alan³,Zhang Hui¹²^ORCID

Affiliation:

1. Department of Pathology, School of Medicine, Johns Hopkins University, 400 N. Broadway Street, Smith Building, Room 4011, Baltimore, Maryland 21231, United States

2. Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States

3. The Brady Urological Institute, The Johns Hopkins School of Medicine, Baltimore, Maryland 21287, United States

Funder

National Cancer Institute

Patrick C. Walsh Prostate Cancer Research Fund

Publisher

American Chemical Society (ACS)

Subject

General Chemistry,Biochemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jproteome.1c00162

Reference35 articles.

1. Cancer statistics, 2020

2. Systematic identification of functionally relevant risk alleles to stratify aggressive versus indolent prostate cancer

3. Integrating biomarkers across omic platforms: an approach to improve stratification of patients with indolent and aggressive prostate cancer

4. Prostate cancer - major changes in the American Joint Committee on Cancer eighth edition cancer staging manual

5. Approaches to urinary detection of prostate cancer

Cited by 14 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Differential expression of N‐glycopeptides derived from serum glycoproteins in mild cognitive impairment (MCI) patients;PROTEOMICS;2024-04-11

2. Identification of a 9-gene signature to enhance biochemical recurrence prediction in primary prostate cancer: A benchmarking study using ten machine learning methods and twelve patient cohorts;Cancer Letters;2024-04

3. Recent progress in mass spectrometry-based urinary proteomics;Clinical Proteomics;2024-02-22

4. Recent developments in mass-spectrometry-based targeted proteomics of clinical cancer biomarkers;Clinical Proteomics;2024-01-30

5. Multiplexed quantitative proteomics in prostate cancer biomarker development;Advances in Cancer Research;2024