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Quantitative Proteomics and Weighted Correlation Network Analysis of Tear Samples in Type 2 Diabetes Patients Complicated with Dry Eye
Author(s) -
Zou Xinrong,
Zhang Pei,
Xu Yi,
Lu Lina,
Zou Haidong
Publication year - 2020
Publication title -
proteomics – clinical applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.948
H-Index - 54
eISSN - 1862-8354
pISSN - 1862-8346
DOI - 10.1002/prca.201900083
Subject(s) - diabetes mellitus , proteome , proteomics , type 2 diabetes , type 1 diabetes , computational biology , biology , biomarker , bioinformatics , medicine , gene , genetics , endocrinology
Purpose Diabetic patients are more likely to experience dry eye (DE). TMT‐based proteomics and WGCNA are used to identify the differentially expressed proteins in tear proteome of type 2 diabetes with DE. The aim is to provide a molecular basis for exploring possible mechanisms underlying the pathogenesis of diabetic DE. Experimental design Subjects are divided into four groups (ten in each): type 2 diabetes with DE; type 2 diabetes without DE; non‐diabetes with DE and normal controls. All subjects undergo DE tests. Total proteins are extracted and quantitatively labeled with TMT, then analyzed using liquid chromatography‐mass spectrometry. WGCNA is used to identify the hub genes. Finally, differentially expressed proteins are validated by ELISA. Results A total of 1922 proteins are identified, of which 1814 contain quantitative information. Ultimately, 650 of these proteins yield quantitative values. WGCNA performed on these 650 proteins reveal four distinct hub genes of diabetic DE. Conclusions and clinical relevance DE is associated with the differential expression of tear proteins in type 2 diabetes. Inflammation, immune factors, and lipid metabolism may play a role in the development of diabetic DE. LTF, LYZ, ZAG, and DNAJC3 have the potential to be the biomarkers of DE in diabetes.