Glycoproteomics Method to Discover Reliable Biomarkers from Human Plasma of Lung Cancer Patients for MS‐based Clinical Studies

Abstract

In clinical and diagnostic proteomics, it is important to discover significant biomarkers from biosamples. Thus, a reliable proteomics methodology is required for the development and standardization of an MS‐based protein identification and quantification method in biosamples. In particular, plasma is one of the most complex fluids in the human body and is commonly used in hospitals to diagnose disease. The purpose of this study was to develop a reliable and reproducible proteomics method using lung cancer plasma for biomarker discovery and diagnostic kits for screening. Glycoproteins are well‐known to be associated with diseases (especially cancers) so in this study glycoproteomics was used for discovering biomarkers from lung cancer plasma. Thirty‐five lung cancer plasma were pooled, and glycoproteins were separated using lectin affinity chromatography (LAC). After affinity selection, trypsin‐digestion, and deglycosylation with PNGase F, the resulting deglycosylated peptides were analyzed with nLC–MS/MS runs twice. The corresponding parent proteins were identified separately through two database search engines and then analyzed individually. The identified proteins from each set were compared, combined, and then categorized for analysis. The combined total identified number of proteins were about 50% increased than in a single run and some proteins seemed to be more reproducible and reliable biomarker candidates because they were always identified in every run. From this research, method‐optimized proteomics can be applied to biomarker discovery for diagnosis and prognosis of disease such as cancer for better MS‐based clinical studies. The identified plasma proteins from this research will also be lung cancer biomarker candidates and can be utilized for the development of in vitro lung cancer diagnostic kits.