Science China Life Sciences

, Volume 56, Issue 7, pp 638–646 | Cite as

Secretory/releasing proteome-based identification of plasma biomarkers in HBV-associated hepatocellular carcinoma

  • Lei Yang
  • WeiQi Rong
  • Ting Xiao
  • Ying Zhang
  • Bin Xu
  • Yu Liu
  • LiMing Wang
  • Fan Wu
  • Jun Qi
  • XiuYing Zhao
  • HongXia Wang
  • NaiJun Han
  • SuPing Guo
  • JianXiong Wu
  • YanNing Gao
  • ShuJun Cheng
Open Access
Research Paper

Abstract

For successful therapy, hepatocellular carcinoma (HCC) must be detected at an early stage. Herein, we used a proteomic approach to analyze the secretory/releasing proteome of HCC tissues to identify plasma biomarkers. Serum-free conditioned media (CM) were collected from primary cultures of cancerous tissues and surrounding noncancerous tissues. Proteomic analysis of the CM proteins permitted the identification of 1365 proteins. The enriched molecular functions and biological processes of the CM proteins, such as hydrolase activity and catabolic processes, were consistent with the liver being the most important metabolic organ. Moreover, 19% of the proteins were characterized as extracellular or membrane-bound. For validation, secretory proteins involved in transforming growth factor-β signaling pathways were validated in plasma samples. Alphafetoprotein (AFP), metalloproteinase (MMP)1, osteopontin (OPN), and pregnancy-specific beta-1-glycoprotein (PSG)9 were significantly increased in HCC patients. The overall performance of MMP1 and OPN in the diagnosis of HCC remained greater than that of AFP. In addition, this study represents the first report of MMP1 as a biomarker with a higher sensitivity and specificity than AFP. Thus, this study provides a valuable resource of the HCC secretome with the potential to investigate serological biomarkers. MMP1 and OPN could be used as novel biomarkers for the early detection of HCC and to improve the sensitivity of biomarkers compared with AFP.

Keywords

hepatocellular carcinoma (HCC) secretome biomarker MMP1 OPN PSG9 

Supplementary material

11427_2013_4497_MOESM1_ESM.pdf (431 kb)
Supplementary material, approximately 430 KB.
11427_2013_4497_MOESM2_ESM.xls (672 kb)
Supplementary material, approximately 671 KB.

References

  1. 1.
    Jemal A, Bray F, Center M M, et al. Global cancer statistics. CA Cancer J Clin, 2011, 61: 69–90PubMedCrossRefGoogle Scholar
  2. 2.
    Liang X, Bi S, Yang W, et al. Epidemiological serosurvey of hepatitis B in China-declining HBV prevalence due to hepatitis B vaccination. Vaccine, 2009, 27: 6550–6557PubMedCrossRefGoogle Scholar
  3. 3.
    Portolani N, Coniglio A, Ghidoni S, et al. Early and late recurrence after liver resection for hepatocellular carcinoma: prognostic and therapeutic implications. Ann Surg, 2006, 243: 229–235PubMedPubMedCentralCrossRefGoogle Scholar
  4. 4.
    Shimada K, Sakamoto Y, Esaki M, et al. Analysis of prognostic factors affecting survival after initial recurrence and treatment efficacy for recurrence in patients undergoing potentially curative hepatectomy for hepatocellular carcinoma. Ann Surg Oncol, 2007, 14: 2337–2347PubMedCrossRefGoogle Scholar
  5. 5.
    Mao Y, Yang H, Xu H, et al. Golgi protein 73 (GOLPH2) is a valuable serum marker for hepatocellular carcinoma. Gut, 2010, 59: 1687–1693PubMedCrossRefGoogle Scholar
  6. 6.
    States D J, Omenn G S, Blackwell T W, et al. Challenges in deriving high-confidence protein identifications from data gathered by a HUPO plasma proteome collaborative study. Nat Biotechnol, 2006, 24: 333–338PubMedCrossRefGoogle Scholar
  7. 7.
    Meng R, Gormley M, Bhat V B, et al. Low abundance protein enrichment for discovery of candidate plasma protein biomarkers for early detection of breast cancer. J Proteomics, 2011, 75: 366–374PubMedCrossRefGoogle Scholar
  8. 8.
    Makridakis M, Vlahou A. Secretome proteomics for discovery of cancer biomarkers. J Proteomics, 2010, 73: 2291–2305PubMedCrossRefGoogle Scholar
  9. 9.
    Planque C, Kulasingam V, Smith C R, et al. Identification of five candidate lung cancer biomarkers by proteomics analysis of conditioned media of four lung cancer cell lines. Mol Cell Proteomics, 2009, 8: 2746–2758PubMedPubMedCentralCrossRefGoogle Scholar
  10. 10.
    Polisetty R V, Gupta M K, Nair S C, et al. Glioblastoma cell secretome: analysis of three glioblastoma cell lines reveal 148 non-redundant proteins. J Proteomics, 2011, 74: 1918–1925PubMedCrossRefGoogle Scholar
  11. 11.
    Xiao T, Ying W, Li L, et al. An approach to studying lung cancer-related proteins in human blood. Mol Cell Proteomics, 2005, 4: 1480–1486PubMedCrossRefGoogle Scholar
  12. 12.
    Zhang Y, Xu B, Liu Y, et al. The ovarian cancer-derived secretory/releasing proteome: a repertoire of tumor markers. Proteomics, 2012, 12: 1883–1891PubMedCrossRefGoogle Scholar
  13. 13.
    Lechner J F, LaVeck M A. A serum-free method for culturing normal human bronchial epithelial cells at clonal density. J Tissue Cult Methods, 1985, 9: 43–48CrossRefGoogle Scholar
  14. 14.
    Shen Z, Li P, Ni R J, et al. Label-free quantitative proteomics analysis of etiolated maize seedling leaves during greening. Mol Cell Proteomics, 2009, 8: 2443–2460PubMedPubMedCentralCrossRefGoogle Scholar
  15. 15.
    Sun H, Fang H, Chen T, et al. GOFFA: gene ontology for functional analysis—a FDA gene ontology tool for analysis of genomic and proteomic data. BMC Bioinformatics, 2006, 7(Suppl 2): S23PubMedPubMedCentralCrossRefGoogle Scholar
  16. 16.
    Wu S, Wan P, Li J, et al. Multi-modality of pI distribution in whole proteome. Proteomics, 2006, 6: 449–455PubMedCrossRefGoogle Scholar
  17. 17.
    Omenn G S, States D J, Adamski M, et al. Overview of the HUPO Plasma Proteome Project: results from the pilot phase with 35 collaborating laboratories and multiple analytical groups, generating a core dataset of 3020 proteins and a publicly-available database. Proteomics, 2005, 5: 3226–3245PubMedCrossRefGoogle Scholar
  18. 18.
    Anderson N L, Polanski M, Pieper R, et al. The human plasma proteome: a nonredundant list developed by combination of four separate sources. Mol Cell Proteomics, 2004, 3: 311–326PubMedCrossRefGoogle Scholar
  19. 19.
    Chinese Human Liver Proteome Profiling Consortium. First insight into the human liver proteome from PROTEOME(SKY)-LIVER(Hu) 1.0, a publicly available database. J Proteome Res, 2010, 9: 79–94CrossRefGoogle Scholar
  20. 20.
    Lin L, Amin R, Gallicano G I, et al. The STAT3 inhibitor NSC 74859 is effective in hepatocellular cancers with disrupted TGF-beta signaling. Oncogene, 2009, 28: 961–972PubMedPubMedCentralCrossRefGoogle Scholar
  21. 21.
    Pavlou M P, Diamandis E P. The cancer cell secretome: a good source for discovering biomarkers? J Proteomics, 2010, 73: 1896–1906PubMedCrossRefGoogle Scholar
  22. 22.
    Sun S, Xu M Z, Poon R T, et al. Circulating Lamin B1 (LMNB1) biomarker detects early stages of liver cancer in patients. J Proteome Res, 2010, 9: 70–78PubMedCrossRefGoogle Scholar
  23. 23.
    Luk J M, Lam C T, Siu A F, et al. Proteomic profiling of hepatocellular carcinoma in Chinese cohort reveals heat-shock proteins (Hsp27, Hsp70, GRP78) up-regulation and their associated prognostic values. Proteomics, 2006, 6: 1049–1057PubMedCrossRefGoogle Scholar
  24. 24.
    Murata M, Matsuzaki K, Yoshida K, et al. Hepatitis B virus X protein shifts human hepatic transforming growth factor (TGF)-beta signaling from tumor suppression to oncogenesis in early chronic hepatitis B. Hepatology, 2009, 49: 1203–1217PubMedCrossRefGoogle Scholar
  25. 25.
    Takafuji V, Forgues M, Unsworth E, et al. An osteopontin fragment is essential for tumor cell invasion in hepatocellular carcinoma. Oncogene, 2007, 26: 6361–6371PubMedCrossRefGoogle Scholar
  26. 26.
    Shang S, Plymoth A, Ge S, et al. Identification of osteopontin as a novel marker for early hepatocellular carcinoma. Hepatology, 2012, 55: 483–490PubMedPubMedCentralCrossRefGoogle Scholar
  27. 27.
    Sun J, Xu H M, Zhou H J, et al. The prognostic significance of preoperative plasma levels of osteopontin in patients with TNM stage-I of hepatocellular carcinoma. J Cancer Res Clin Oncol, 2010, 136: 1–7PubMedCrossRefGoogle Scholar
  28. 28.
    Salahshor S, Goncalves J, Chetty R, et al. Differential gene expression profile reveals deregulation of pregnancy specific beta1 glycoprotein 9 early during colorectal carcinogenesis. BMC Cancer, 2005, 5: 66PubMedPubMedCentralCrossRefGoogle Scholar
  29. 29.
    Lisboa F A, Warren J, Sulkowski G, et al. Pregnancy-specific glycoprotein 1 induces endothelial tubulogenesis through interaction with cell surface proteoglycans. J Biol Chem, 2011, 286: 7577–7586PubMedPubMedCentralCrossRefGoogle Scholar
  30. 30.
    Kessenbrock K, Plaks V, Werb Z. Matrix metalloproteinases: regulators of the tumor microenvironment. Cell, 2010, 141: 52–67PubMedPubMedCentralCrossRefGoogle Scholar
  31. 31.
    Asselah T, Bieche I, Laurendeau I, et al. Liver gene expression signature of mild fibrosis in patients with chronic hepatitis C. Gastroenterology, 2005, 129: 2064–2075PubMedCrossRefGoogle Scholar
  32. 32.
    Liu D, Guo H, Li Y, et al. Association between polymorphisms in the promoter regions of matrix metalloproteinases (MMPs) and risk of cancer metastasis: a meta-analysis. PLoS ONE, 2012, 7: e31251PubMedPubMedCentralCrossRefGoogle Scholar
  33. 33.
    Decock J, Hendrickx W, Vanleeuw U, et al. Plasma MMP1 and MMP8 expression in breast cancer: protective role of MMP8 against lymph node metastasis. BMC Cancer, 2008, 8: 77PubMedPubMedCentralCrossRefGoogle Scholar
  34. 34.
    Li M, Xiao T, Zhang Y, et al. Prognostic significance of matrix metalloproteinase-1 levels in peripheral plasma and tumour tissues of lung cancer patients. Lung Cancer, 2010, 69: 341–347PubMedCrossRefGoogle Scholar

Copyright information

© The Author(s) 2013

Authors and Affiliations

  • Lei Yang
    • 1
  • WeiQi Rong
    • 2
  • Ting Xiao
    • 1
  • Ying Zhang
    • 1
  • Bin Xu
    • 3
  • Yu Liu
    • 1
  • LiMing Wang
    • 2
  • Fan Wu
    • 2
  • Jun Qi
    • 4
  • XiuYing Zhao
    • 5
  • HongXia Wang
    • 3
  • NaiJun Han
    • 1
  • SuPing Guo
    • 1
  • JianXiong Wu
    • 2
  • YanNing Gao
    • 1
  • ShuJun Cheng
    • 1
  1. 1.State Key Laboratory of Molecular Oncology, Beijing Key Laboratory for Carcinogenesis and Cancer Prevention, Cancer Institute (Hospital)Peking Union Medical College & Chinese Academy of Medical SciencesBeijingChina
  2. 2.Department of Abdominal Surgery, Cancer Institute (Hospital)Peking Union Medical College & Chinese Academy of Medical SciencesBeijingChina
  3. 3.National Center of Biomedical AnalysisBeijingChina
  4. 4.Clinical Laboratory, Cancer Institute (Hospital)Peking Union Medical College & Chinese Academy of Medical SciencesBeijingChina
  5. 5.Clinical Laboratory, Beijing You’an HospitalCapital Medical UniversityBeijingChina

Personalised recommendations