LIVER CANCER / BASIC RESEARCH
The isobaric tags for relative and absolute quantification-based quantitative proteomics of fresh tissue-derived secretome in hepatocellular carcinoma
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1
Department of General Surgery, HwaMei Hospital, University of Chinese Academy of Sciences, China
2
Ningbo Institute of Life and Health Industry, University of Chinese Academy of Sciences, China
3
Key Laboratory of Diagnosis and Treatment of Digestive System Tumors of Zhejiang Province, China
4
Department of Clinical Laboratory, HwaMei Hospital, University of Chinese Academy of Sciences, China
Submission date: 2019-12-11
Final revision date: 2020-03-10
Acceptance date: 2020-03-10
Online publication date: 2021-02-03
Corresponding author
Yun-Jie Chen
Department of General Surgery, HwaMei Hospital, University of Chinese Academy of Sciences; Ningbo Institute of Life and Health Industry, University of Chinese Academy of Sciences
Arch Med Sci 2025;21(4):1536-1555
KEYWORDS
TOPICS
ABSTRACT
Introduction:
Proteomics technology platforms offer an extremely useful tool for the discovery of new cancer biomarkers. Secreted proteins play important roles in signal transduction, cellular growth, proliferation, differentiation, and apoptosis. This study aimed to investigate the molecular signatures of the hepatocellular carcinoma (HCC) by quantitative proteomics using isobaric tags for relative and absolute quantification (iTRAQ) with liquid chromatography-tandem mass spectrometry (LC-MS/MS).
Material and methods:
In this study, we used an iTRAQ-based quantitative proteomic approach to analyse the secretome of HCC tissues to identify plasma biomarkers. Serum-free conditioned media (CM) were collected from the primary cultures of cancerous tissues, the surrounding noncancerous tissues, and distal noncancerous tissues.
Results:
A proteomic analysis of the CM proteins allowed for a total of 5214 identified proteins, of which 190 and 44 proteins were dysregulated in the HCC tissues/distal noncancerous tissues (HCC/DN group) and the adjacent noncancerous tissues/distal noncancerous tissues (AN/DN group) compared with the distal noncancerous tissues. The dysregulated proteins in the HCC/DN group were concentrated in mitogen-activated protein kinase (MAPK) signalling and Janus kinase-signal transducer and activator of the transcription (JAK-STAT) signalling, but the dysregulated proteins in the AN/DN group were more concentrated in the basal material metabolism.
Conclusions:
The secretome profile alternations and signalling pathways were associated with HCC incidence and development. The dysregulated proteins in the HCC/DN group were concentrated in the MAPK signalling and JAK-STAT signalling, but the dysregulated proteins in the AN/DN group were more concentrated in the basal material metabolism.
REFERENCES (49)
1.
Ferlay J, Soerjomataram I, Dikshit R, et al. Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int J Cancer. 2015; 136: E359-86.
2.
Wang FS, Fan JG, Zhang Z, Gao B, Wang HY. The global burden of liver disease: the major impact of China. Hepatology 2014; 60: 2099-108.
3.
Sterling RK, Wright EC, Morgan TR, et al. Frequency of elevated hepatocellular carcinoma (HCC) biomarkers in patients with advanced hepatitis C. Am J Gastroenterol 2012; 107: 64-74.
4.
Liu X, Cheng Y, Sheng W, et al. Clinicopathologic features and prognostic factors in alpha-fetoprotein-producing gastric cancers: analysis of 104 cases. J Surg Oncol 2010; 102: 249-55.
5.
El-Bahrawy M. Alpha-fetoprotein-producing non-germ cell tumours of the female genital tract. Eur J Cancer 2010; 46: 1317-22.
6.
Veenstra TD, Conrads TP, Hood BL, Avellino AM, Ellenbogen RG, Morrison RS. Biomarkers: mining the biofluid proteome. Mol Cell Proteomics 2005; 4: 409-18.
7.
Anderson NL, Anderson NG. The human plasma proteome: history, character, and diagnostic prospects. Mol Cell Proteomics 2002; 1: 845-67.
8.
Hanash SM, Pitteri SJ, Faca VM. Mining the plasma proteome for cancer biomarkers. Nature 2008; 452: 571-9.
9.
Paltridge JL, Belle L, Khew-Goodall Y. The secretome in cancer progression. Biochim Biophys Acta 2013; 1834: 2233-41.
10.
Slany A, Haudek-Prinz V, Zwickl H, Stättner S, Grasl-Kraupp B, Gerner C. Myofibroblasts are important contributors to human hepatocellular carcinoma: evidence for tumor promotion by proteome profiling. Electrophoresis 2013; 34: 3315-25.
11.
Yu Y, Pan X, Ding Y, et al. An iTRAQ based quantitative proteomic strategy to explore novel secreted proteins in metastatic hepatocellular carcinoma cell lines. Analyst 2013; 138: 4505-11.
12.
Cao J, Hu Y, Shen C, et al. Nanozeolite-driven approach for enrichment of secretory proteins in human hepatocellular carcinoma cells. Proteomics 2009; 9: 4881-8.
13.
Xiao T, Ying W, Li L, et al. An approach to studying lung cancer-related proteins in human blood. Mol Cell Proteomics 2005; 4: 1480-6.
14.
Yang L, Rong W, Xiao T, et al. Secretory/releasing proteome-based identification of plasma biomarkers in HBV-associated hepatocellular carcinoma. Sci China Life Sci 2013; 56: 638-46.
15.
Zhu J, Warner E, Parikh ND, Lubman DM. Glycoproteomic markers of hepatocellular carcinoma-mass spectrometry based approaches. Mass Spectrom Rev 2019; 38: 265-90.
16.
Kim KH, Kim JY, Yoo JS. Mass spectrometry analysis of glycoprotein biomarkers in human blood of hepatocellular carcinoma. Expert Rev Proteomics 2019; 16: 553-68.
17.
Zhang J, Liang R, Wei J, et al. Identification of candidate biomarkers in malignant ascites from patients with hepatocellular carcinoma by iTRAQ-based quantitative proteomic analysis. Biomed Res Int 2018; 2018: 5484976.
18.
Guo J, Jing R, Zhong JH, et al. Identification of CD14 as a potential biomarker of hepatocellular carcinoma using iTRAQ quantitative proteomics. Oncotarget 2017; 8: 62011-28.
19.
Wang Y, Liu H, Liang D, et al. Reveal the molecular signatures of hepatocellular carcinoma with different sizes by iTRAQ based quantitative proteomics. J Proteomics 2017; 150: 230-41.
20.
Song C, Ye M, Han G, et al. Reversed-phase-reversed-phase liquid chromatography approach with high orthogonality for multidimensional separation of phosphopeptides. Anal Chem 2010; 82: 53-6.
21.
Solaini G, Sgarbi G, Baracca A. Oxidative phosphorylation in cancer cells. Biochim Biophys Acta 2011; 1807: 534-42.
22.
Yu M. Somatic mitochondrial DNA mutations in human cancers. Adv Clin Chem 2012; 57: 99-138.
23.
Larman TC, DePalma SR, Hadjipanayis AG, et al. Spectrum of somatic mitochondrial mutations in five cancers. Proc Natl Acad Sci USA 2012; 109: 14087-91.
24.
Deschênes-Simard X, Lessard F, Gaumont-Leclerc MF, Bardeesy N, Ferbeyre G. Cellular senescence and protein degradation: breaking down cancer. Cell Cycle 2014; 13: 1840-58.
25.
Adhikary A, Chakraborty S, Mazumdar M, et al. Inhibition of epithelial to mesenchymal transition by E-cadherin up-regulation via repression of slug transcription and inhibition of E-cadherin degradation: dual role of scaffold/matrix attachment region-binding protein 1 (SMAR1) in breast cancer cells. J Biol Chem 2014; 289: 25431-44.
26.
Zhang S, Wang X, Iqbal S, et al. Epidermal growth factor promotes protein degradation of epithelial protein lost in neoplasm (EPLIN), a putative metastasis suppressor, during epithelial-mesenchymal transition. J Biol Chem 2013; 288: 1469-79.
27.
Sabit I, Hashimoto N, Matsumoto Y, Yamaji T, Furukawa K, Furukawa K. Binding of a sialic acid-recognizing lectin Siglec-9 modulates adhesion dynamics of cancer cells via calpain-mediated protein degradation. J Biol Chem 2013; 288: 35417-27.
28.
Wang F, Weaver VM, Petersen OW, et al. Reciprocal interactions between beta1-integrin and epidermal growth factor receptor in three-dimensional basement membrane breast cultures: a different perspective in epithelial biology. Proc Natl Acad Sci USA 1998; 95: 14821-6.
29.
Pikarsky E, Porat RM, Stein I, et al. NF-kappaB functions as a tumour promoter in inflammation-associated cancer. Nature 2004; 431: 461-6.
30.
Robichaud N, Hsu BE, Istomine R, et al. Translational control in the tumor microenvironment promotes lung metastasis: phosphorylation of eIF4E in neutrophils. Proc Natl Acad Sci USA 2018; 115: E2202-9.
31.
Tahmasebi-Birgani M, Ansari H, Carloni V. Defective mitosis-linked DNA damage response and chromosomal instability in liver cancer. Biochim Biophys Acta Rev Cancer 2019; 1872: 60-5.
32.
Yang SF, Chang CW, Wei RJ, Shiue YL, Wang SN, Yeh YT. Involvement of DNA damage response pathways in hepatocellular carcinoma. Biomed Res Int 2014; 2014: 153867.
33.
Chang L, Karin M. Mammalian MAP kinase signalling cascades. Nature 2001; 410: 37-40.
34.
Burotto M, Chiou VL, Lee JM, Kohn EC. The MAPK pathway across different malignancies: a new perspective. Cancer 2014; 120: 3446-56.
35.
Pencik J, Pham HT, Schmoellerl J, et al. JAK-STAT signaling in cancer: from cytokines to non-coding genome. Cytokine 2016; 87: 26-36.
36.
Moresi V, Adamo S, Berghella L. The JAK/STAT pathway in skeletal muscle pathophysiology. Front Physiol 2019; 10: 500.
37.
Blaj C, Schmidt EM, Lamprecht S, et al. Oncogenic effects of high MAPK activity in colorectal cancer mark progenitor cells and persist irrespective of RAS mutations. Cancer Res 2017; 77: 1763-74.
38.
McCubrey JA, Steelman LS, Chappell WH, et al. Roles of the Raf/MEK/ERK pathway in cell growth, malignant transformation and drug resistance. Biochim Biophys Acta 2007; 1773: 1263-84.
39.
Vasuri F, Visani M, Acquaviva G, et al. Role of microRNAs in the main molecular pathways of hepatocellular carcinoma. World J Gastroenterol 2018; 24: 2647-60.
40.
Noch E, Khalili K. Oncogenic viruses and tumor glucose metabolism: like kids in a candy store. Mol Cancer Ther 2012; 11: 14-23.
41.
Grasmann G, Smolle E, Olschewski H, Leithner K. Gluconeogenesis in cancer cells – repurposing of a starvation-induced metabolic pathway? Biochim Biophys Acta Rev Cancer 2019; 1872: 24-36.
42.
Rodrigues JG, Balmaña M, Macedo JA, et al. Glycosylation in cancer: selected roles in tumour progression, immune modulation and metastasis. Cell Immunol 2018; 333: 46-57.
43.
Cheng WK, Oon CE. How glycosylation aids tumor angiogenesis: an updated review. Biomed Pharmacother 2018; 103: 1246-52.
44.
Mehta A, Herrera H, Block T. Glycosylation and liver cancer. Adv Cancer Res 2015; 126: 257-79.
45.
Zhang S, Cao X, Gao Q, Liu Y. Protein glycosylation in viral hepatitis-related HCC: characterization of heterogeneity, biological roles, and clinical implications. Cancer Lett 2017; 406: 64-70.
46.
Albini A, Bruno A, Noonan DM, Mortara L. Contribution to tumor angiogenesis from innate immune cells within the tumor microenvironment: implications for immunotherapy. Front Immunol 2018; 9: 527.
47.
Huang L, Xu H, Peng G. TLR-mediated metabolic reprogramming in the tumor microenvironment: potential novel strategies for cancer immunotherapy. Cell Mol Immunol 2018; 15: 428-37.
48.
Reina-Campos M, Shelton PM, Diaz-Meco MT, Moscat J. Metabolic reprogramming of the tumor microenvironment by p62 and its partners. Biochim Biophys Acta Rev Cancer 2018; 1870: 88-95.
49.
Jiang X, Wang J, Deng X, et al. Role of the tumor microenvironment in PD-L1/PD-1-mediated tumor immune escape. Mol Cancer 2019; 18: 10.