Literature DB >> 31433754

Advances in Current Diabetes Proteomics: From the Perspectives of Label- free Quantification and Biomarker Selection.

Jianbo Fu1, Yongchao Luo1, Minjie Mou1, Hongning Zhang1, Jing Tang1,2, Yunxia Wang1, Feng Zhu1,2.   

Abstract

BACKGROUND: Due to its prevalence and negative impacts on both the economy and society, the diabetes mellitus (DM) has emerged as a worldwide concern. In light of this, the label-free quantification (LFQ) proteomics and diabetic marker selection methods have been applied to elucidate the underlying mechanisms associated with insulin resistance, explore novel protein biomarkers, and discover innovative therapeutic protein targets.
OBJECTIVE: The purpose of this manuscript is to review and analyze the recent computational advances and development of label-free quantification and diabetic marker selection in diabetes proteomics.
METHODS: Web of Science database, PubMed database and Google Scholar were utilized for searching label-free quantification, computational advances, feature selection and diabetes proteomics.
RESULTS: In this study, we systematically review the computational advances of label-free quantification and diabetic marker selection methods which were applied to get the understanding of DM pathological mechanisms. Firstly, different popular quantification measurements and proteomic quantification software tools which have been applied to the diabetes studies are comprehensively discussed. Secondly, a number of popular manipulation methods including transformation, pretreatment (centering, scaling, and normalization), missing value imputation methods and a variety of popular feature selection techniques applied to diabetes proteomic data are overviewed with objective evaluation on their advantages and disadvantages. Finally, the guidelines for the efficient use of the computationbased LFQ technology and feature selection methods in diabetes proteomics are proposed.
CONCLUSION: In summary, this review provides guidelines for researchers who will engage in proteomics biomarker discovery and by properly applying these proteomic computational advances, more reliable therapeutic targets will be found in the field of diabetes mellitus. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.

Entities:  

Keywords:  Label free quantification; antidiabetic drug; computation; diabetes proteomics; mass spectrometry; target discovery.

Year:  2020        PMID: 31433754     DOI: 10.2174/1389450120666190821160207

Source DB:  PubMed          Journal:  Curr Drug Targets        ISSN: 1389-4501            Impact factor:   3.465


  5 in total

1.  Longitudinal proteomics analysis in the immediate microenvironment of islet allografts during progression of rejection.

Authors:  Oscar Alcazar; Luis F Hernandez; Ernesto S Nakayasu; Paul D Piehowski; Charles Ansong; Midhat H Abdulreda; Peter Buchwald
Journal:  J Proteomics       Date:  2020-05-20       Impact factor: 4.044

2.  Identifying Potential Mitochondrial Proteome Signatures Associated with the Pathogenesis of Pulmonary Arterial Hypertension in the Rat Model.

Authors:  Jie Wang; Md Nazim Uddin; Qian Li; Alidan Aierken; Ming-Yuan Li; Rui Wang; Qian-Zhi Yan; Dilare Adi; Ming-Tao Gai; Yun Wu
Journal:  Oxid Med Cell Longev       Date:  2022-02-21       Impact factor: 6.543

3.  Functional annotation and enrichment analysis of differentially expressed serum proteins in patients with type 2 diabetes after dapagliflozin.

Authors:  Yan-Xue Zhao; Sarul Borjigin; Zhao-Li Yan
Journal:  World J Diabetes       Date:  2022-03-15

Review 4.  Oxidative Stress in Type 2 Diabetes: The Case for Future Pediatric Redoxomics Studies.

Authors:  Stephanie N Alu; Evan A Los; George A Ford; William L Stone
Journal:  Antioxidants (Basel)       Date:  2022-07-07

5.  Multiple Imputation Approaches Applied to the Missing Value Problem in Bottom-Up Proteomics.

Authors:  Miranda L Gardner; Michael A Freitas
Journal:  Int J Mol Sci       Date:  2021-09-06       Impact factor: 5.923

  5 in total

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