4.3 Article

Proteomic analysis of plasma exosomes to differentiate malignant from benign pulmonary nodules

期刊

CLINICAL PROTEOMICS
卷 16, 期 -, 页码 -

出版社

BMC
DOI: 10.1186/s12014-019-9225-5

关键词

-

资金

  1. National Natural Science Foundation of China [81422029, 81572264, 81372525]
  2. [2016YFA0501800]
  3. [2017YFA0505500]

向作者/读者索取更多资源

BackgroundIt is difficult to distinguish benign pulmonary nodules (PNs) from malignant PNs by conventional examination. Therefore, novel biomarkers that can identify the nature of PNs are needed. Exosomes have recently been identified as an attractive alternative approach since tumor-specific molecules can be found in exosomes isolated from biological fluids.MethodsPlasma exosomes were extracted via the exoEasy reagent method. The major proteins from plasma exosomes in patients with PNs were identified via labelfree analysis and screened for differentially expressed proteins. A GO classification analysis and KEGG pathway analysis were performed on plasma exosomal protein from patients with benign and malignant PNs.ResultsWestern blot confirmed that protein expression of CD63 and CD9 could be detected in the exosome extract. Via a search of the human Uniprot database, 736 plasma exosome proteins from patients with PNs were detected using high-confidence peptides. There were 33 differentially expressed proteins in the benign and malignant PNs. Of these, 12 proteins were only expressed in the benign PNs group, while 9 proteins were only expressed in the malignant PNs group. We further obtained important information on signaling pathways and nodal proteins related to differential benign and malignant PNs via bioinformatic analysis methods such as GO, KEGG, and String.ConclusionsThis study provides a new perspective on the identification of novel detection strategies for benign and malignant PNs. We hope our findings can provide clues for the identification of benign and malignant PNs.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.3
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据