Evidence map›Paper›PMID 30894400›Full record

ArticleMolecular & cellular proteomics : MCP2019

Comprehensive Analysis of Individual Variation in the Urinary Proteome Revealed Significant Gender Differences.

Chen Shao, Mindi Zhao, Xizhao Chen, Haidan Sun, Yehong Yang, Xiaoping Xiao, Zhengguang Guo, Xiaoyan Liu, Yang Lv, Xiangmei Chen and 3 more

Open access · hybridAbstract read
In one paragraph

Article in Molecular & cellular proteomics : MCP, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.

0numbers the graph read from it
0cells of the map it votes in
37citing papers in PubMed
5.8field-weighted citation impact, top 3% 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

37 citing papers in PubMed, 97 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

13 authors at 3 institutions in 1 country.

Chen ShaoFrom the ‡Center for Bioinformatics, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine, Peking Union Medical College, Beijing, China.
Mindi Zhao¶Department of Laboratory Medicine, Beijing Hospital, National Center of Gerontology, Beijing, 100730, China.
Xizhao Chen**Department of Nephrology, Chinese PLA General Hospital, Chinese PLA Institute of Nephrology, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing, China.
Haidan Sun‡‡Core Facility of Instrument, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine, Peking Union Medical College.
Yehong Yang‡‡Core Facility of Instrument, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine, Peking Union Medical College.
Xiaoping Xiao‡‡Core Facility of Instrument, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine, Peking Union Medical College.
Zhengguang Guo‡‡Core Facility of Instrument, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine, Peking Union Medical College.
Xiaoyan Liu‡‡Core Facility of Instrument, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine, Peking Union Medical College.
Yang Lv**Department of Nephrology, Chinese PLA General Hospital, Chinese PLA Institute of Nephrology, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing, China.
Xiangmei Chen**Department of Nephrology, Chinese PLA General Hospital, Chinese PLA Institute of Nephrology, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing, China.
Wei Sun‡‡Core Facility of Instrument, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine, Peking Union Medical College; sunwei1018@hotmail.com.
Di Wu**Department of Nephrology, Chinese PLA General Hospital, Chinese PLA Institute of Nephrology, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing, China; wudi@301hospital.com.cn.
Youhe Gao¶¶Department of Biochemistry and Molecular Biology, Beijing Normal University, Gene Engineering Drug and Biotechnology Beijing Key Laboratory, Beijing, China gaoyouhe@bnu.edu.cn.
Chinese Academy of Medical Sciences & Peking Union Medical College · CNChinese PLA General Hospital · CNBeijing Normal University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Disease biomarkers are the measurable changes associated with a pathophysiological process. Without homeostatic control, urine accumulates systematic changes in the body. Thus, urine is an attractive biological material for the discovery of disease biomarkers. One of the major bottlenecks in urinary biomarker discovery is that the concentration and composition of urinary proteins are influenced by many physiological factors. To elucidate the individual variation and related factors influencing the urinary proteome, we comprehensively analyzed the urine samples from healthy adult donors (aged 20-69 years). Co-expression network analysis revealed protein clusters representing the metabolic status, gender-related differences and age-related differences in urinary proteins. In particular, we demonstrated that gender is a crucial factor contributing to individual variation. Proteins that were increased in the male urine samples include prostate-secreted proteins and TIMP1, a protein whose abundance alters under various cancers and renal diseases; however, the proteins that were increased in the female urine samples have known functions in the immune system. Nine gender-related proteins were validated on 85 independent samples by multiple reaction monitoring. Five of these proteins were further used to build a model that could accurately distinguish male and female urine samples with an area under curve value of 0.94. Based on the above results, we strongly suggest that future biomarker investigations should consider gender as a crucial factor in experimental design and data analysis. Finally, reference intervals of each urinary protein were estimated, providing a baseline for the discovery of abnormalities.

Indexed as

Sex CharacteristicsAdultAgedFemaleHumansMaleMiddle AgedProtein Interaction MapsProteinuriaProteomeReference StandardsStaining and LabelingYoung AdultProteomeBiofluids*Label-free quantificationMass SpectrometryProtein Identification*Urine analysis

Identifiers

PMID30894400
PMCPMC6553935
OpenAlexW2923275290

What OpenQuestion holds

Textmetadata
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.