BACKGROUND: To use the tandem mass tags (TMT) based quantitative proteomics technique and bioinformatics method to identify potential serum diagnostic biomarkers for gastric cancer (GC). METHODS: This study enrolled GC patients and healthy control subjects. Mixed serum samples were pooled with 10 individual samples. The high-abundance proteins depleted serum proteins were collected by removing abundance albumin and immunoglobulin G (IgG). After desalting and ultrafiltration, the trypsin digested proteins were analyzed using TMT based quantitative proteomics system. The differential proteins were screened using the cutoff value of 1.2-fold change for up-regulation or down-regulation. The gene ontology (GO) was further analyzed using the UniProtKB/Swiss-Prot database. Then the differentially expressed protein ITIH4, S100A8, CD59, and COF1 were conducted using western blot. RESULTS: A total of 594 distinct serum proteins were identified between the GC group and the healthy controls. Forty-eight proteins were up-regulated and 57 were down-regulated using the cutoff value of 1.2-fold change. Using bioinformatics analysis, we found that the differentially expressed proteins were mainly concentrated in the extracellular exosome, extracellular region, extracellular space, and plasma membrane; their biological process activities included antigen binding, calcium ion binding, and protein homodimerization. In addition, the western blotting results showed that four differentially expressed proteins were completely coincident with the TMT quantification trend. CONCLUSIONS: The results showed that we were able to successfully create the differentially expressed protein database of GC using TMT technology. These proteins are potential molecular markers that could help us understand the potential molecular mechanism of GC.
BACKGROUND: To use the tandem mass tags (TMT) based quantitative proteomics technique and bioinformatics method to identify potential serum diagnostic biomarkers for gastric cancer (GC). METHODS: This study enrolled GC patients and healthy control subjects. Mixed serum samples were pooled with 10 individual samples. The high-abundance proteins depleted serum proteins were collected by removing abundance albumin and immunoglobulin G (IgG). After desalting and ultrafiltration, the trypsin digested proteins were analyzed using TMT based quantitative proteomics system. The differential proteins were screened using the cutoff value of 1.2-fold change for up-regulation or down-regulation. The gene ontology (GO) was further analyzed using the UniProtKB/Swiss-Prot database. Then the differentially expressed protein ITIH4, S100A8, CD59, and COF1 were conducted using western blot. RESULTS: A total of 594 distinct serum proteins were identified between the GC group and the healthy controls. Forty-eight proteins were up-regulated and 57 were down-regulated using the cutoff value of 1.2-fold change. Using bioinformatics analysis, we found that the differentially expressed proteins were mainly concentrated in the extracellular exosome, extracellular region, extracellular space, and plasma membrane; their biological process activities included antigen binding, calcium ion binding, and protein homodimerization. In addition, the western blotting results showed that four differentially expressed proteins were completely coincident with the TMT quantification trend. CONCLUSIONS: The results showed that we were able to successfully create the differentially expressed protein database of GC using TMT technology. These proteins are potential molecular markers that could help us understand the potential molecular mechanism of GC.