iTRAQ‐Based Quantitative Proteomics Approach Identifies Novel Diagnostic Biomarkers That Were Essential for Glutamine Metabolism and Redox Homeostasis for Gastric Cancer

Jiang, Zhen; Zhang, Chenghua; Gan, Li; Jia, Yuewang; Xiong, Yu; Chen, Yujiang; Wang, Zhi; Wang, Linfeng; Luo, Hao; Li, Juexi; Zhu, Rui; Ji, Xingli; Yu, Qin; Wang, Li

doi:10.1002/prca.201800038

Cited by 27 publications

(20 citation statements)

References 39 publications

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“…With iTRAQ, the protein expression levels can be compared simultaneously [16]. iTRAQ technology has been applied to muchresearch: To identify prognostic biomarkers for gastric cancer [17]; to reveal potential novel biomarkers for the early diagnosis of acute myocardial infarction within three hours [18]; to identified SAA and ACTB as potential biomarkers in patients with severe hand, foot, and mouth disease (HFMD) [19], to discover glioblastoma serum markers [20], and so on.…”

Section: Discussionmentioning

confidence: 99%

Protein Expression Profile in Rat Silicosis Model Reveals Upregulation of PTPN2 and Its Inhibitory Effect on Epithelial-Mesenchymal Transition by Dephosphorylation of STAT3

Zhu

Yao

Duan

et al. 2020

IJMS

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Silicosis is a chronic occupational lung disease caused by long-term inhalation of crystalline silica particulates. We created a rat model that closely approximates the exposure and development of silicosis in humans. Isobaric tags for relative and absolute quantitation (iTRAQ) technologies we used to identify proteins differentially expressed in activated rat lung tissue. We constructed three lentiviral knockdown vectors and an overexpression vector for the protein tyrosine phosphatase non-receptor type 2 (PTPN2) gene to achieve stable long-term expression. A total of 471 proteins were differentially expressed in the silicosis group compared with controls. Twenty upregulated, and eight downregulated proteins exhibited a ≥1.5-fold change relative to controls. We next found that the PTPN2, Factor B, and VRK1 concentrations in silicotic rats silicosis and SiO2-stimulated MLE-12 cells were significantly higher than control groups. More importantly, we found that overexpression of PTPN2 simultaneously decreased the expression of phospho–signal transducer and activator of transcription 3 (p-STAT3) and Vimentin, while increasing E-cadherin expression. The opposite pattern was observed for PTPN2-gene silencing. We identified three proteins with substantially enhanced expression in silicosis. Our study also showed that PTPN2 can inhibit epithelial-mesenchymal transition by dephosphorylating STAT3 in silicosis fibrosis.

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Section: Discussionmentioning

confidence: 99%

Protein Expression Profile in Rat Silicosis Model Reveals Upregulation of PTPN2 and Its Inhibitory Effect on Epithelial-Mesenchymal Transition by Dephosphorylation of STAT3

Zhu

Yao

Duan

et al. 2020

IJMS

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“…Yang et al used targeted proteomics coupled with immunoaffinity enrichment to investigate epithelial ovarian cancer samples and found that the combined AUC for serum carbohydrate antigen 125 (CA125) and heat shock protein 27 was 0.88, which was significantly higher than that of CA125 alone [25]. Jiang et al performed iTRAQ labeling and LC-ESI-MS/MS to evaluate a discovery group of four GC samples and four adjacent healthy tissues [26], and found an AUC of 0.734 for GLS1 and GGCT co-expression, suggesting that the level of co-expression had high clinical value as a diagnostic biomarker for early GC.…”

Section: Discussionmentioning

confidence: 99%

Screening of Potential Biomarkers for Gastric Cancer with Diagnostic Value Using Label-free Global Proteome Analysis

Song¹,

Wang²,

Sun³

et al. 2020

Preprint

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Gastric cancer (GC) is one of the most malignant tumors worldwide. Despite the recent decrease in mortality rates, the prognosis remains poor. Therefore, it is necessary to find novel biomarkers with early diagnostic value for GC. In this study, we present a large-scale proteomic analysis of 30 GC tissues and 30 matched healthy tissues using label-free global proteome profiling. Our results identified 537 differentially expressed proteins, including 280 upregulated and 257 downregulated proteins. The ingenuity pathway analysis (IPA) results indicated that the sirtuin signaling pathway was the most activated pathway in GC tissues whereas oxidative phosphorylation was the most inhibited. Moreover, the most activated molecular function was cellular movement, including tissue invasion by tumor cell lines. Based on IPA results, 15 hub proteins were screened. Using the receiver operating characteristic curve, most of hub proteins showed a high diagnostic power in distinguishing between tumors and healthy controls. A four-protein (ATP5B-ATP5O-NDUFB4-NDUFB8) diagnostic signature was built using a random forest model. The area under the curve (AUC) of this model was 0.996 and 0.886 for the training and testing set, respectively, suggesting that the four-protein signature has a high diagnostic power. This signature was further tested with independent datasets using plasma enzyme-linked immune sorbent assays, resulted in an AUC of 0.778 for distinguishing GC tissues from healthy controls, and using immunohistochemical tissue microarray analysis, resulting in an AUC of 0.805. In conclusion, this study identifies potential biomarkers and improves our understanding of the pathogenesis, providing novel therapeutic targets for GC.

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“…Stable isotope labeling uses the mass increase caused by the mass tags with incorporated stable isotopes to quantify peptides at the MS1 full scan (precursor ion) level or peptide fragments at the MS2 (production ion) scan level. There are several strategies to label peptides or proteins with stable isotopes: chemical labeling such as Isobaric Tags for Relative and Absolute Quantitation (iTRAQ) [ 97 , 100 , 101 , 102 ] and Tandem Mass Tags (TMT) [ 103 , 104 , 105 , 106 ], and metabolic labeling strategies such as Stable Isotope Labeling by Amino acids in Cell culture (SILAC) [ 107 , 108 , 109 , 110 ]. Data independent acquisition (DIA) methods can also be used for quantitative studies ( Figure 1 ).…”

Section: Mass Spectrometry For Biomarker Developmentmentioning

confidence: 99%

Quantitative Mass Spectrometry-Based Proteomics for Biomarker Development in Ovarian Cancer

Ryu

Thomas

2021

Molecules

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Ovarian cancer is the most lethal gynecologic malignancy among women. Approximately 70–80% of patients with advanced ovarian cancer experience relapse within five years and develop platinum-resistance. The short life expectancy of patients with platinum-resistant or platinum-refractory disease underscores the need to develop new and more effective treatment strategies. Early detection is a critical step in mitigating the risk of disease progression from early to an advanced stage disease, and protein biomarkers have an integral role in this process. The best biological diagnostic tool for ovarian cancer will likely be a combination of biomarkers. Targeted proteomics methods, including mass spectrometry-based approaches, have emerged as robust methods that can address the chasm between initial biomarker discovery and the successful verification and validation of these biomarkers enabling their clinical translation due to the robust sensitivity, specificity, and reproducibility of these versatile methods. In this review, we provide background information on the fundamental principles of biomarkers and the need for improved treatment strategies in ovarian cancer. We also provide insight into the ways in which mass spectrometry-based targeted proteomics approaches can provide greatly needed solutions to many of the challenges related to ovarian cancer biomarker development.

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iTRAQ‐Based Quantitative Proteomics Approach Identifies Novel Diagnostic Biomarkers That Were Essential for Glutamine Metabolism and Redox Homeostasis for Gastric Cancer

Cited by 27 publications

References 39 publications

Protein Expression Profile in Rat Silicosis Model Reveals Upregulation of PTPN2 and Its Inhibitory Effect on Epithelial-Mesenchymal Transition by Dephosphorylation of STAT3

Protein Expression Profile in Rat Silicosis Model Reveals Upregulation of PTPN2 and Its Inhibitory Effect on Epithelial-Mesenchymal Transition by Dephosphorylation of STAT3

Screening of Potential Biomarkers for Gastric Cancer with Diagnostic Value Using Label-free Global Proteome Analysis

Quantitative Mass Spectrometry-Based Proteomics for Biomarker Development in Ovarian Cancer

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