title: An iTRAQ based quantitative proteomic strategy to explore novel secreted proteins in metastatic hepatocellular carcinoma cell lines.
abstract: Secretomics is receiving more and more considerable attention due to the key roles of secreted proteins in cancer. Most of the potential biomarkers for clinical diagnosis and treatment of cancer are secreted proteins. However, the low concentration of secreted proteins and contaminants released from dead cells are a great challenge to secretomic profiling studies. Although some bioinformatics tools such as SecretomeP and SignalP can help to annotate or predict secreted proteins, they also cause false positive or negative rates of identification especially for nonclassical secreted proteins. Therefore, an iTRAQ based quantitative proteomics strategy was set up in this work and applied in the secretomics study of metastatic HCC cell lines. A total of 94 proteins were identified as secreted and 31 of them were newly found in our data. Compared with the known secreted proteins participating in inter-cellular signalling, most of the newly identified secreted proteins were metabolic enzymes, such as PKM2 and EHHADH, whose functions focused on the synthesis/metabolism of glucose, fatty acids and amino acids. Exploring their secretion would help to further study their bio-functions in conditioned media and the effects on the interactions of cancer cells and the microenvironment. Differences between the secretomes of the two metastatic HCC cell lines were also explored in the same experiment. This strategy showed its superiority in accurately identifying secreted proteins as well as monitoring their variation under different biological conditions.
authors.affiliation: Department of Chemistry and Institutes of Biomedical Sciences of Shanghai Medical School, Fudan University, 138 Yi Xueyuan Road, Shanghai, 200032, PR China.
authors.forename: Yanyan
authors.initials: Y
authors.lastname: Yu
authors.forename: Xiuwen
authors.initials: X
authors.lastname: Pan
authors.forename: Ying
authors.initials: Y
authors.lastname: Ding
authors.forename: Xiaohui
authors.initials: X
authors.lastname: Liu
authors.forename: Hailin
authors.initials: H
authors.lastname: Tang
authors.forename: Chengpin
authors.initials: C
authors.lastname: Shen
authors.forename: Huali
authors.initials: H
authors.lastname: Shen
authors.forename: Pengyuan
authors.initials: P
authors.lastname: Yang
mesh_terms: D014408:Biomarkers, Tumor / Q000378:metabolism*; D006528:Carcinoma, Hepatocellular / Q000378:metabolism*; D002352:Carrier Proteins / Q000378:metabolism*; D045744:Cell Line, Tumor; D006801:Humans; D008113:Liver Neoplasms / Q000378:metabolism*; D008565:Membrane Proteins / Q000378:metabolism*; D009363:Neoplasm Proteins / Q000378:metabolism*; D040901:Proteomics / Q000379:methods*; D013963:Thyroid Hormones / Q000378:metabolism*; D000097810:Thyroid Hormone-Binding Proteins
chemical_list: D014408:Biomarkers, Tumor; D002352:Carrier Proteins; D008565:Membrane Proteins; D009363:Neoplasm Proteins; D013963:Thyroid Hormones; D000097810:Thyroid Hormone-Binding Proteins