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Identification of differentially expressed proteins in the locoregional recurrent esophageal squamous cell carcinoma by quantitative proteomics. | LitMetric
Background : This study aimed to identify potential biomarkers associated with locoregional recurrence in patients with esophageal squamous cell carcinoma (ESCC) after radical resection.Methods : We performed a quantitative proteomics analysis using isobaric tags for relative and absolute quantification (iTRAQ) with reversed-phase liquid chromatography-mass spectrometry (RPLC-MS) to identify differential expression proteins (DEPs) between a locoregional recurrence group and good prognosis group of ESCC after radical esophagectomy. The bioinformatics analysis was performed with ingenuity pathway analysis software (IPA) and Gene Ontology (GO) database using the software of MAS 3.0. Kaplan-Meier (KM) Plotter Online Tool (http://www.kmplot.com) was used to evaluate the relationship between the differential expression of proteins and survival in patients with ESCC.Results : More than 400 proteins were quantitated of which 27 proteins had upregulated expression and 55 proteins had downregulated expression in the locoregional recurrence group compared to the good prognosis group. These 82 DEPs were associated with biological procession of cancer development including cellular movement, cellular assembly and organization, cellular function and maintenance, cellular growth and proliferation, cell death and survival, DNA replication recombination and repair, and so on. Of these DEPs, SPTAN1 and AGT proteins were identified to be associated with RFS in ESCC. SPTAN1 was positively associated with RFS and AGT was negatively associated with RFS. Expression of SPTAN1 tended to have favorable OS while expression of AGT tended to have poor OS.Conclusions : Our results demonstrated that quantitative proteomics is an effective discovery tool to identify biomarkers for prognosis prediction in ESCC. However, it needs more studies with large populations of ESCC to validate these potential biomarkers.
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