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Comparative membrane proteomic analysis between lung adenocarcinoma and normal tissue by iTRAQ labeling mass spectrometry

  • Xuede Zhang
  • , Wei Li
  • , Yanli Hou
  • , Zequn Niu
  • , Yujie Zhong
  • , Yuping Zhang
  • , Shuanying Yang
  • Xi'an Jiaotong University

Research output: Contribution to journalArticlepeer-review

17 Scopus citations

Abstract

Lung adenocarcinoma, the most common type of lung cancer, has increased in recent years. Prognosis is still poor, and pathogenesis remains unclear. This study aimed to investigate the membrane protein profile differences between lung adenocarcinoma and normal tissue. Manual microdissection was used to isolate the target cells from tumor tissue and normal tissue. iTRAQ labeling combined with 2D-LC-MS/MS yielded a differential expression profile of membrane proteins. Bioinformatic analysis was performed using Gene Ontology, WEGO, PID, and KEGG. S100A14 protein was selectively verified by Western blotting. The relationship of S100A14 expression with clinicopathological features in lung cancer patients was evaluated using immunohistochemistry. As a result, 568 differential proteins were identified; 257 proteins were upregulated and 311 were downregulated. Of these proteins, 48% were found to be membrane bound or membrane associated. These proteins enable the physiological functions of binding, catalysis, molecular transduction, transport, and molecular structure. For these differential proteins, 35 pathways were significantly enriched through the Pathway Interaction Database, whereas 19 pathways were enriched via KEGG. The overexpression and cellular distribution of S100A14 in lung cancer were confirmed. We found that upregulation of S100A14 was associated with well or moderate differentiation. The iTRAQ-coupled 2D-LC-MS/MS technique is a potential method for comparing membrane protein profiles between tumor and normal tissue. Such analysis may also help in identifying novel biomarkers and the mechanisms underlying carcinogenesis.

Original languageEnglish
Pages (from-to)267-280
Number of pages14
JournalAmerican Journal of Translational Research
Volume6
Issue number3
StatePublished - 2014

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • Lung adenocarcinoma
  • Mass spectrometry
  • Membrane protein
  • Proteomic profile
  • iTRAQ

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