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MASS SPECTROMETRY‐BASED MITOCHONDRIAL PROTEOMICS IN HUMAN OVARIAN CANCERS
Author(s) -
Li Na,
Zhan Xianquan
Publication year - 2020
Publication title -
mass spectrometry reviews
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.035
H-Index - 126
eISSN - 1098-2787
pISSN - 0277-7037
DOI - 10.1002/mas.21618
Subject(s) - stable isotope labeling by amino acids in cell culture , proteomics , quantitative proteomics , phosphoproteomics , mitochondrion , proteome , chemistry , isobaric labeling , tandem mass tag , computational biology , biochemistry , biology , microbiology and biotechnology , protein phosphorylation , phosphorylation , protein kinase a , gene
The prominent characteristics of mitochondria are highly dynamic and regulatory, which have crucial roles in cell metabolism, biosynthetic, senescence, apoptosis, and signaling pathways. Mitochondrial dysfunction might lead to multiple serious diseases, including cancer. Therefore, identification of mitochondrial proteins in cancer could provide a global view of tumorigenesis and progression. Mass spectrometry‐based quantitative mitochondrial proteomics fulfils this task by enabling systems‐wide, accurate, and quantitative analysis of mitochondrial protein abundance, and mitochondrial protein posttranslational modifications (PTMs). Multiple quantitative proteomics techniques, including isotope‐coded affinity tag, stable isotope labeling with amino acids in cell culture, isobaric tags for relative and absolute quantification, tandem mass tags, and label‐free quantification, in combination with different PTM‐peptide enrichment methods such as TiO 2 enrichment of tryptic phosphopeptides and antibody enrichment of other PTM‐peptides, increase flexibility for researchers to study mitochondrial proteomes. This article reviews isolation and purification of mitochondria, quantitative mitochondrial proteomics, quantitative mitochondrial phosphoproteomics, mitochondrial protein‐involved signaling pathway networks, mitochondrial phosphoprotein‐involved signaling pathway networks, integration of mitochondrial proteomic and phosphoproteomic data with whole tissue proteomic and transcriptomic data and clinical information in ovarian cancers (OC) to in‐depth understand its molecular mechanisms, and discover effective mitochondrial biomarkers and therapeutic targets for predictive, preventive, and personalized treatment of OC. This proof‐of‐principle model about OC mitochondrial proteomics is easily implementable to other cancer types. © 2020 John Wiley & Sons Ltd. Mass Spec Rev