Evidence map›Paper›PMID 33928097›Full record

ArticleFrontiers in medicine2021

A Comprehensive Urine Proteome Database Generated From Patients With Various Renal Conditions and Prostate Cancer.

Adam C Swensen, Jingtang He, Alexander C Fang, Yinyin Ye, Carrie D Nicora, Tujin Shi, Alvin Y Liu, Tara K Sigdel, Minnie M Sarwal, Wei-Jun Qian

Open access · goldAbstract read
In one paragraph

Article in Frontiers in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed
3.5field-weighted citation impact, top 6% 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

23 citing papers in PubMed, 34 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

10 authors at 3 institutions in 1 country.

Adam C SwensenIntegrative Omics, Pacific Northwest National Laboratory, Biological Sciences Division, Richland, WA, United States.
Jingtang HeIntegrative Omics, Pacific Northwest National Laboratory, Biological Sciences Division, Richland, WA, United States.
Alexander C FangIntegrative Omics, Pacific Northwest National Laboratory, Biological Sciences Division, Richland, WA, United States.
Yinyin YeIntegrative Omics, Pacific Northwest National Laboratory, Biological Sciences Division, Richland, WA, United States.
Carrie D NicoraIntegrative Omics, Pacific Northwest National Laboratory, Biological Sciences Division, Richland, WA, United States.
Tujin ShiIntegrative Omics, Pacific Northwest National Laboratory, Biological Sciences Division, Richland, WA, United States.
Alvin Y LiuDepartment of Urology, University of Washington, Seattle, WA, United States.
Tara K SigdelDepartment of Surgery, University of California, San Francisco, San Francisco, CA, United States.
Minnie M SarwalDepartment of Surgery, University of California, San Francisco, San Francisco, CA, United States.
Wei-Jun QianIntegrative Omics, Pacific Northwest National Laboratory, Biological Sciences Division, Richland, WA, United States.
Pacific Northwest National Laboratory · USUniversity of California, San Francisco · USUniversity of Washington · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Urine proteins can serve as viable biomarkers for diagnosing and monitoring various diseases. A comprehensive urine proteome database, generated from a variety of urine samples with different disease conditions, can serve as a reference resource for facilitating discovery of potential urine protein biomarkers. Herein, we present a urine proteome database generated from multiple datasets using 2D LC-MS/MS proteome profiling of urine samples from healthy individuals (HI), renal transplant patients with acute rejection (AR) and stable graft (STA), patients with non-specific proteinuria (NS), and patients with prostate cancer (PC). A total of ~28,000 unique peptides spanning ~2,200 unique proteins were identified with a false discovery rate of <0.5% at the protein level. Over one third of the annotated proteins were plasma membrane proteins and another one third were extracellular proteins according to gene ontology analysis. Ingenuity Pathway Analysis of these proteins revealed 349 potential biomarkers in the literature-curated database. Forty-three percentage of all known cluster of differentiation (CD) proteins were identified in the various human urine samples. Interestingly, following comparisons with five recently published urine proteome profiling studies, which applied similar approaches, there are still ~400 proteins which are unique to this current study. These may represent potential disease-associated proteins. Among them, several proteins such as serpin B3, renin receptor, and periostin have been reported as pathological markers for renal failure and prostate cancer, respectively. Taken together, our data should provide valuable information for future discovery and validation studies of urine protein biomarkers for various diseases.

Indexed as

kidney diseaseLC-MS/MSprostate cancerproteomicsurinary biomarkersurine proteome

Identifiers

PMID33928097
PMCPMC8076675
OpenAlexW3128745808

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.