iTRAQ-based proteomics reveals SOD2 as a potential salivary biomarker in liver cancer
Show all authors
Feng Ding, Kehuan Sun, Ningning Sun, ...
First Published May 1, 2019 Research Article
https://doi.org/10.1177/1724600819841619
Article information
Open Access Creative Commons Attribution, Non Commercial 4.0 License
Abstract
Background:
Salivary proteomic analysis has been extensively used in a wide range of cancer, but not in hepatocellular carcinoma. The aim of this study was to identify potential salivary biomarkers for hepatocellular carcinoma clinical screening.
Methods:
In this study, we performed isobaric tags for relative and absolute quantitation (iTRAQ)-based quantitative proteomics analysis to detect differentially expressed proteins between saliva samples from 15 hepatocellular carcinoma patients and 15 healthy controls. Enzyme-linked immunosorbent assay (ELISA) verification was undertaken in saliva samples from 14 hepatocellular carcinoma patients and 14 healthy controls.
Results:
Overall, 133 proteins with significant differential expression level (ratio > 1.5 or < 0.67) were detected. Using bioinformatic analysis, two candidate proteins were selected and subsequently verified by ELISA. The increased expression of superoxide dismutase 2, mitochondrial (SOD2) in hepatocellular carcinoma patients was confirmed by ELISA, with an area under the curve value of 0.9082.
Conclusions:
iTRAQ-based quantitative proteomics revealed that SOD2 might serve as a potential salivary biomarker for hepatocellular carcinoma detection. Our results indicated that a noninvasive and inexpensive salivary test might be established for hepatocellular carcinoma detection.
Keywords Hepatocellular carcinoma, iTRAQ, salivary biomarker, SOD2
Introduction
Global Cancer Statistics in 2012 show that primary liver cancer has been the fifth most common malignant tumor in men, and the sixth in women.1 The most frequent primary liver cancer is hepatocellular carcinoma (HCC). The leading cause of HCC is a viral infection with hepatitis virus B and/or hepatitis C.2 HCC affects approximately 1 million people annually, with the incidence equal to the mortality rate, and approximately half occur in China.1,3
Currently, only a limited number of drugs have been proven as effective treatment options for HCC, and the disease often recurs even after aggressive local therapy.4,5 One of the main reasons of the unsatisfactory curative effect is the delay in diagnosis due to the fact that early cancers exhibit non-specific symptoms.3 Thus, early diagnosis is crucial for improving the high mortality rate in HCC. In the last two decades, the increasing understanding of tumorigenesis and the development of high-throughput genomics and proteomics techniques have led to the identification of a number of biomarkers for HCC, including α-fetoprotein (AFP), lectin-bound AFP (AFP-L3%), Golgi protein 73, interleukin-6, des-γ carboxyl prothrombin, osteopontin, glypican-3, and squamous cell carcinoma antigen.3,6–8 However, with relatively low sensitivity and specificity, most single biomarkers are still unsatisfactory in the diagnosis of HCC. Besides, these biomarkers are mainly tissue and serum-based. Since blood sampling is a painful procedure, a non-invasive diagnostic test for cancer is urgently needed.
In recent years, it has been recognized that saliva offers an easy, fast, low-cost and non-invasive approach for the diagnosis of diseases.9 Human saliva contains proteins that can be informative for underlying pathogenetic process in oral and systemic diseases.10,11 Therefore, a rising number of proteomic analyses of saliva from several diseases (e.g. breast cancer, oral cancer, and lung cancer) were performed to identify salivary biomarkers in cancer.12–14 However, to the best of our knowledge, very few—if any—studies have been performed to investigate HCC salivary biomarkers. The exploration of high sensitivity and specificity biomarkers in saliva are crucial for improving the diagnostic rate, the treatment effect, and the curative satisfaction in HCC patients.3
Currently, isobaric tags for relative and absolute quantification (iTRAQ) has been widely used in a number of proteomic studies. As a powerful quantitative proteome technique, iTRAQ has relatively high sensitivity and allows the identification of numerous proteins compared with traditional proteome approaches.15 In this study, we performed iTRAQ-based quantitative proteomic analysis on saliva samples from HCC patients and healthy controls. We then explored the protein profile differences in HCC patients and healthy controls. We aimed to reveal a potential salivary biomarker for liver cancer screening.
Materials and methods
Patients and saliva samples collection
With ethically approved informed consent, saliva samples were collected from a test set of 30 subjects and a validation set of 28 subjects at the First Affiliated Hospital of Hunan University of Chinese Medicine (Supplementary Table 1). All of the patients were diagnosed as HCC and had not received any prior treatment in the form of chemotherapy, radiotherapy, or surgery before sample collection. A well-defined and standardized protocol was used for collection, storage, and processing of the saliva samples.11 Unstimulated whole saliva samples were collected between 7 a.m. and 8 a.m. with prior mouth rinsing with water. The donors were asked to abstain from eating, drinking, smoking, or using oral hygiene products for at least 1 hour before collection. The samples, once collected, were immediately placed on ice and were then centrifuged at 3000 rpm for 10 min at 4°C to remove debris and cells. The supernatant was then collected and PMSF (Sigma, St. Louis, MO, USA) were added in the collected samples to a final concentration of 1mM to ensure preservation of the protein integrity. The samples were immediately aliquoted into smaller volumes (200 μL) and stored at −80°C until further processing.
Protein preparation and iTRAQ labeling
Each saliva sample was dissolved in lysis buffer (7M urea, 2M thiourea, and 0.1% CHAPS) and mixed using vortex. After sonication, the sample was incubated for 30 minutes at room temperature and then centrifuged at 15,000 rpm at 4°C for 20 minutes. The supernatant was immediately aliquoted into smaller volumes and stored at −80°C. The protein was quantified using Bradford protein assay with bovine serum albumin as standard.16 The samples were pooled into four groups: HCC group 1 (n=5), HCC group 2 (n=5), HCC group 3 (n=5), and healthy controls (n=15). Each group has two technical replicates. In total, there are eight pooled samples for the iTRAQ labelling. For making the pool, an equal amount of protein was used from each sample to ensure that the total amount of protein was 100 μg. Protein reduction was then carried out by adding 2 μL reducing reagent (50 mM tris-(2-carboxyethyl) phosphine) (AB Sciex, PN:4390812) into 100 μg protein solution and incubated for 1 h at 60°C. Reduced protein was subsequently alkylated with 1 μL Cysteine-Blocking Reagent (200 mM methyl methanethiosulfonate in isopropanol) (AB Sciex, PN:4390812) for 10 minutes at room temperature. The alkylated protein was added to a 10K ultrafiltration tube and centrifuged at 12,000 rpm for 20 min. Finally, the protein samples were digested with 4 μg trypsin (AB Sciex, PN: 4370285) at 37°C for overnight. The reaction was terminated by adding 50 μL dissolution buffer (0.5 M triethylammonium bicarbonate) (AB Sciex, PN:4390812). The resulting peptides were subsequently labeled using iTRAQ 8-plex kit (AB Sciex, PN:4390812) according to the manufacturer’s instructions. The two technical replicates of healthy controls were labeled using 113 and 114 tags. For the technical replicates of the HCC groups, 115 and 116 tags (HCC group 1), 117 and 118 tags (HCC group 2), and 119 and 121 tags (HCC group 3) were used. Then the iTRAQ labeled peptides were mixed and dried using a vacuum centrifuge.
Liquid chromatography-mass spectometry/MS analysis
The iTRAQ labeled peptide mixtures were separated by high pH reversed-phase liquid chromatography. The fractions were re-dissolved in 100 μL of phase A (98% ddH2O, 2% acetonitrile, pH 10) and centrifuged at 14,000 rpm for 20 min. The supernatant was collected and loaded onto a Durashell C18 nano trap column (4.6 mm × 250 mm, 5 μm 100 Å; Agela, Catalog Number: DC952505-0) in an HPLC system (RIGOL L-3000). Peptides were eluted by running a 5% to 95% phase B gradient (98% acetonitrile, 2% ddH2O, pH 10) for 72 min at a flow rate of 700 nL/min. The eluted peptides were collected at a rate of 1 tube/min and later were pooled into 10 fractions according to the variations in peak intensity. Pooled peptides were then dried using a speed vacuum centrifuge. The dried labeled peptide fraction was re-dissolved in 20 μL 2% methanol (Sigma-Aldrich, 14262, USA) and 0.1 % formic acid (Sigma-Aldrich, 56302, USA) and analyzed using a Q-Exactive mass spectrometer (Thermo Scientific, USA) combined with a Thermo Scientific EASY-nLC 1000 System (Nano HPLC). The peptides were loaded onto an Acclaim PepMap100 column (2cm × 100μm, C18, 5μm) and eluted at 350 nL/min onto an EASY-Spray column (12 cm × 75 μm, C18, 3μm) over a 90 min gradient. The two mobile phases were phase A (100% dd H2O, 0.1% formic acid) and phase B (100% acetonitrile, 0.1% formic acid). Key parameters for Q-Exactive were set as: spray voltage: 2.1KV, capillary temperature: 250°C, ion source: EASY-Spray source, declustering potential (DP): 100 V; full MS: resolution: 70,000 full width at half mazimum (FWHM), full scan AGC target: 1e6, full scan max IT: 60 ms, scan range: 350–1800 m/z; dd-MS2: resolution: 17,500 FWHM, AGC target: 5e6, maximum IT: 70 ms, intensity threshold: 5E + 03, fragmentation methods: HCD, NCE: 29%, top N: 20. Raw mass data were processed using Proteome Discoverer 1.4 (Thermo Scientific, USA) and searched against the human database of UniProtKB/Swiss-Prot (release-2017_10/). The searching parameters were as follows: enzyme: trypsin, max missed cleavages: 2, static modification: carbamidomethyl (C), dynamic modification: iTRAQ8plex (N-term), iTRAQ8plex (K) and oxidation (M), precursor ion mass tolerance: 15 ppm, fragment ion mass tolerance: 20 mmu.
Data analysis
Peptides with at least 95% confidence threshold and a false discovery rate <0.01 were considered as identified. Proteins with at least one unique peptide were used for further quantification analysis. R programming language and student’s t test were used to perform statistical analysis. Mean and standard deviation was calculated to define the fold change ratio between technical replicates of HCC groups and healthy controls. Proteins with a fold-change ratio larger than 1.5 or less than 0.67 (average ratio of three HCC groups) and a P value <0.05 were deemed as significantly differentially expressed. Gene ontology annotations were completed using DAVID.17 Pathway analysis of the differentially expressed proteins was performed using KEGG. The protein–protein interaction network was further created using STRING functional protein association networks website (http://string-db.org). The threshold values for STRING network construction were as follows: meaning of network edges: evidence; minimum required interaction score: 0.7; display simplifications: hide disconnected nodes in the network. The receiver operating characteristic (ROC) curve was constructed and the area under the curve (AUC) value was calculated in Graphpad Prism. For all analysis, a P value < 0.05 was considered statistically significant.
Enzyme-linked immunosorbent assay
Enzyme-linked immunosorbent assay (ELISA) tests for superoxide dismutase 2, mitochondrial (SOD2) and haptoglobin (HP) were performed using Abcam ELISA kits, according to the manufacturer’s instructions. The concentrations of both proteins were assayed in saliva samples from 14 HCC patients and 14 healthy controls.
Results
Quantitative proteomic analysis of saliva by iTRAQ
To identify the differentially expressed proteins between HCC patients and healthy controls, we compared the saliva samples from 15 HCC patients and 15 healthy controls using iTRAQ. Since we consider the biological variability among the patients, we chose to divide the patients’ saliva into three groups in order to get a more reliable biomarker candidate with higher specificity and sensitivity. And each group consisted of 5 HCC patients. A total of 15 saliva samples from healthy controls were pooled into one group. Each group had two technical replicates. Finally, eight subgroups were compared in an 8-Plex iTRAQ experiment. A total of 1296 proteins with a P value <0.05 were detected. To identify the differentially expressed proteins in the saliva of HCC patients, the quantification data between HCC groups and healthy controls were compared, and we finally identified 133 significantly differentially expressed proteins (fold-change > 1.5 or < 0.67 and a P value <0.05) (Table 1). Among them, 77 proteins were found to be significantly up-regulated and 56 proteins were found to be significantly down-regulated in the replicates of all HCC groups .
Table
Table 1. List of 133 significantly differentially expressed proteins.
Table 1. List of 133 significantly differentially expressed proteins.
View larger version
Biological function, pathway, and network analysis
To obtain the functional characteristics of the differentially expressed proteins, the DAVID online analysis tool was used to determine enriched gene ontology (GO) terms.17 GO ontology covers three domains: biological process, molecular function and cellular component. Figure 1 shows the top 10 enriched GO terms of each domain (P < 0.05). In biological process, the differentially expressed proteins were enriched in retina homeostasis, positive regulation of B-cell activation, innate immune response, phagocytosis, and response to hydrogen peroxide. In molecular function, the main enriched GO terms were associated with immunoglobulin receptor binding, antigen binding, antioxidant activity, serine-type endopeptidase activity and protease binding. In the cellular component, the main enriched GO terms were related to specific extracellular exosome, immunoglobulin complex and blood microparticle. To identify relevant biology pathways of the differentially expressed proteins, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was performed (https://www.kegg.jp/). Figure 2 represents the enriched KEGG pathways of the differentially expressed proteins. The pathways with a P <0.05 were considered enriched pathways in which at least two differentially expressed proteins were involved. We next constructed the functional protein association network of the 133 differentially expressed proteins using String.18 The protein–protein interaction network is shown in Figure 3. Since proteins interacting with others were normally deemed as potential candidates of functional proteins, the disconnected nodes were then not displayed in the network. With interaction scores larger than 0.7, 39 proteins that formed 28 high confidence protein–protein associations constituted a complex, multi-centered interaction network (Figure 3). As shown in Figure 3, 12 genes (SOD2, SOD1, NME1, HNRNPA2B1, LSM5, NAA38, GRB2, YWHAH, OAT, ARG1, OTC, POTEJ) were significant hub genes interacting with at least two other genes in the interaction network.
figure
Figure 1. Top 10 enriched GO terms of differentially expressed proteins. P <0.05 was considered to be statistically significant.
GO: gene ontology.
figure
Figure 2. Statistically significantly enriched KEGG pathways of differentially expressed proteins. P <0.05 was considered statistically significant.
KEGG: Kyoto Encyclopedia of Genes and Genomes.
figure
Figure 3. The protein–protein interaction network determined by STRING on differentially expressed proteins. Interactions of the differentially expressed proteins are obtained by searching the STRING database with a confidence cutoff of 0.7. Disconnected nodes are not displayed.
Candidate biomarker verification
Besides liver cancer, we have performed salivary proteomics analyses of multiple diseases, including diabetes mellitus, gastric cancer, colon cancer, and nephritis (data not shown). Proteins (i.e. TSPAN1, TFF3) that were differentially expressed in two or more diseases were excluded for further verification. According to the average fold change ratio, the availability of ELISA kits and association with HCC in previous studies, two proteins were finally selected for verification, including SOD2 and HP. Based on the iTRAQ result, the expression levels of SOD2 and HP were significantly higher in all three HCC groups (Supplementary Table 2). We then assessed the expression level of these two proteins by ELISA to verify the iTRAQ result using a new sample set (14 HCC saliva samples and 14 healthy control saliva samples). As shown in box plot diagrams for SOD2 and HP (Figure 4A), statistically significant differences of the concentrations of SOD2 and HP were seen between the HCC group and the control group. We further focused on ROC curves for evaluation. The ROC curves for both proteins are shown in Figure 4B. The AUC values for SOD2 and HP were 0.9082 and 0.6939, respectively.
figure
Figure 4. Salivary biomarker evaluation and validation. (a) ELISA quantification data of SOD2 and HP in the saliva of 14 HCC patients and 14 healthy controls. (b) ROC curves for SOD2 and HP.
ELISA: enzyme-linked immunosorbent assay; HCC: hepatocellular carcinoma; HP: haptoglobin; ROC: receiver operating characteristic; SOD2: superoxide dismutase 2, mitochondrial.
Discussion
Currently, to our certain knowledge, a number of serum/tissue biomarkers have been identified for possible early detection of HCC. Although AFP has been widely used as a significant biomarker in HCC detection over the last two decades, the diagnostic accuracy of AFP is questioned, and the sensitivity of AFP is restricted to 25% when tumors size <3 cm.19 AFP was excluded from the surveillance criteria in the HCC guidelines published by the American Association for the Study of Liver Diseases in 2010. Available studies have shown that the diagnostic performance of other biomarkers alone or combined with AFP only moderately increased in the detection of HCC compared to AFP alone.20
Human saliva has become an attractive early detection biofluid in recent years. The present study sought to conduct an in-depth analysis of the salivary proteomics of HCC and explore potential biomarkers for HCC detection. To our knowledge, this is the first study of salivary proteomics in HCC using the iTRAQ technique. In this study, we obtained a total of 133 proteins that showed significant expression difference between HCC patients and healthy controls. In a few of the proteins with a relatively high fold-change ratio, high variability of the protein ratio was seen among the three HCC groups versus the control comparisons (Table 1). The variability might be due to the limitation of the iTRAQ technique. iTRAQ was proved to be more sensitive for proteins with small fold-change value, and greater discrepancies were often seen for the higher ratios measured by iTRAQ.21,22 Although high variability of the protein ratio was observed, the expression trends of the same protein were consistent among the three HCC groups (all up-regulated or down-regulated), rather than an opposite expression pattern. After reviewing the association of differentially expressed proteins with HCC in the published literature, and checking the availability of ELISA kits, two proteins (SOD2, HP) were further validated using ELISA experiments, and the ELISA results were consistent with the iTRAQ experiment. We then created the ROC curve for SOD2 and HP, yielding an AUC value of 0.9082 for SOD2 and 0.6627 for HP. Thus, combining computational prediction and experimental verification, we finally identified that SOD2 might be a potential salivary biomarker for HCC detection.
Superoxide dismutase (SOD) is an antioxidant enzyme that catalyzes the dismutation of the super-oxide anion to molecular oxygen and H2O2. In humans, there are three forms of SOD: the copper/zinc SOD (Cu/ZnSOD or SOD1), the manganese SOD (MnSOD or SOD2), and the extracellular SOD (EcSOD or SOD3, also a Cu/ZnSOD).23 SOD1 is expressed mainly in the cytoplasm, and SOD3 is the major SOD in the vascular extracellular space, whereas SOD2 is exclusively localized in the mitochondrial matrix.24,25 Mitochondria has been shown to have important roles in cancer26 and SOD2 plays a significant role in maintaining a balance between reactive oxygen species generation and oxidative defenses for the integrity of mitochondria and the maintenance of its function.27 SOD2 expression varies in different tumor types. Usually, as a tumor suppressor, the decreased expression of SOD2 is often observed during tumor initiation, while the SOD2 level increases during tumor progression to the metastatic stage, and seems to act as an oncogene.28,29 The elevated level of SOD2 has been found in patients with lung, ovarian, pancreatic, prostate, and colon cancer.30–33 The expression of SOD2 in the serum and liver tissue of HCC patients has also been investigated recently. Although Wang et al.34 found that both SOD2 mRNA and protein expression decreased in the HCC patients compared with matching non-cancerous liver tissues, most studies have shown increased SOD2 levels in HCC patients.35 As serum MnSOD levels were significantly elevated in patients with HCV-related HCC than in patients without HCC, Tamai et al.36 suggested serum MnSOD may be a useful serum biomarker for HCC detection. In our present study, we revealed significantly higher SOD2 levels in the saliva samples of HCC patients (2.55-fold change), in comparison with controls. In particular, we performed a ROC curve analysis for the evaluation of biomarker accuracy, and the AUC value of SOD2 was 0.9082. The AUC value is a measure to evaluate and compare different biomarkers in the clinical diagnosis of diseases. An AUC value > 0.9 indicated significant diagnostic accuracy; thus there might be a great possibility for SOD2 to serve as a diagnostic biomarker for HCC.
The present study is the first to perform a quantitative proteomic study by iTRAQ to profile differentially expressed proteins in the saliva samples of HCC patients. We identified SOD2 as a potential salivary biomarker for HCC screening and detection, due to its high-level expression and sensitivity in HCC patients. Validation in a large number of HCC cases would be necessary to determine more rigorous sensitivity and specificity values. Our results provide a potential target for the diagnosis of liver cancer in a non-invasive and cost-effective way.
Author contributions
Feng Ding and Kehuan Sun contributed equally to this study.
Declaration of conflicting interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by grants from National Natural Science Foundation of China (81701071), China Postdoctoral Science Foundation (2017M622915), Shenzhen Science and Technology Program (JCYJ20170306171013613), the Science and Technology Project of Guangdong (2016A020226033), and the Sanming Project of Medicine in Shenzhen (SZSM201612049).
Supplemental material
Supplemental material for this article is available online.
ORCID iD
Feng Ding https://orcid.org/0000-0001-6397-5532
References
1. Torre, LA, Bray, F, Siegel, RL. Global cancer statistics, 2012. CA Cancer J Clin 2015; 65: 87–108.
Google Scholar | Medline | ISI
2. Han, C, Liao, X, Qin, W. EGFR and SYNE2 are associated with p21 expression and SYNE2 variants predict post-operative clinical outcomes in HBV-related hepatocellular carcinoma. Sci Rep 2016; 6: 31237.
Google Scholar | Medline
3. Zhao, YJ, Ju, Q, Li, GC. Tumor markers for hepatocellular carcinoma. Mol Clin Oncol 2013; 1: 593–598.
Google Scholar | Medline
4. Zhu, AX, Duda, DG, Sahani, DV, Jain, RK. HCC and angiogenesis: possible targets and future directions. Nat Rev Clin Oncol 2011; 8: 292–301.
Google Scholar | Medline | ISI
5. Bupathi, M, Kaseb, A, Meric-Bernstam, F. Hepatocellular carcinoma: Where there is unmet need. Mol Oncol 2015; 9: 1501–1509.
Google Scholar | Medline
6. Chauhan, R, Lahiri, N. Tissue- and serum-associated biomarkers of hepatocellular carcinoma. Biomarkers Canc 2016; 8: 37–55.
Google Scholar | Medline
7. Zhang, B, Finn, RS. Personalized clinical trials in hepatocellular carcinoma based on biomarker selection. Liver Cancer 2016; 5: 221–232.
Google Scholar | Medline
8. Reichl, P, Mikulits, W. Accuracy of novel diagnostic biomarkers for hepatocellular carcinoma: An update for clinicians (Review). Oncol Rep 2016; 36: 613–625.
Google Scholar | Medline
9. Zhang, A, Sun, H, Wang, P. Salivary proteomics in biomedical research. Clinica Chimica Acta 2013; 415: 261–265.
Google Scholar | Medline
10. Hu, S, Arellano, M, Boontheung, P. Salivary proteomics for oral cancer biomarker discovery. Clin Canc Res 2008; 14: 6246–6252.
Google Scholar | Medline | ISI
11. Zhang, L, Xiao, H, Karlan, S. Discovery and preclinical validation of salivary transcriptomic and proteomic biomarkers for the non-invasive detection of breast cancer. PloS One. 2010; 5: e15573.
Google Scholar | Medline | ISI
12. Mishra, S, Saadat, D, Kwon, O. Recent advances in salivary cancer diagnostics enabled by biosensors and bioelectronics. Biosens Bioelectron 2016; 81: 181–197.
Google Scholar | Medline
13. Miller, CS, Foley, JD, Bailey, AL. Current developments in salivary diagnostics. Biomark Med 2010; 4: 171–189.
Google Scholar | Medline | ISI
14. Yeh, CK, Christodoulides, NJ, Floriano, PN. Current development of saliva/oral fluid-based diagnostics. Tex Dent J 2010; 127: 651–661.
Google Scholar | Medline
15. Wiese, S, Reidegeld, KA, Meyer, HE. Protein labeling by iTRAQ: a new tool for quantitative mass spectrometry in proteome research. Proteomics. 2007; 7: 340–350.
Google Scholar | Medline | ISI
16. Bradford, MM. A rapid and sensitive method for the quantitation of microgram quantities of protein utilizing the principle of protein–dye binding. Anal Biochem 1976; 72: 248–254.
Google Scholar | Medline | ISI
17. Huang da, W, Sherman, BT, Lempicki, RA. Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res 2009; 37: 1–13.
Google Scholar | Medline | ISI
18. Szklarczyk, D, Franceschini, A, Wyder, S. STRING v10: protein–protein interaction networks, integrated over the tree of life. Nucleic Acids Res 2015; 43: D447–452.
Google Scholar | Medline | ISI
19. Wang, CS, Lin, CL, Lee, HC. Usefulness of serum des-gamma-carboxy prothrombin in detection of hepatocellular carcinoma. WorldJ Gastroenterol 2005; 11: 6115–6119.
Google Scholar | Medline | ISI
20. Hu, B, Tian, X, Sun, J. Evaluation of individual and combined applications of serum biomarkers for diagnosis of hepatocellular carcinoma: a meta-analysis. Int J Mol Sci 2013; 14: 23559–23580.
Google Scholar | Medline
21. Shirran, SL, Botting, CH. A comparison of the accuracy of iTRAQ quantification by nLC-ESI MSMS and nLC-MALDI MSMS methods. J Proteomics. 2010; 73: 1391–1403.
Google Scholar | Medline
22. Evans, C, Noirel, J, Ow, SY. An insight into iTRAQ: where do we stand now? Anal Bioanal Chem 2012; 404: 1011–1027.
Google Scholar | Medline
23. Che, M, Wang, R, Li, X. Expanding roles of superoxide dismutases in cell regulation and cancer. Drug Discov Today 2016; 21: 143–149.
Google Scholar | Medline
24. Fukai, T, Ushio-Fukai, M. Superoxide dismutases: role in redox signaling, vascular function, and diseases. Antioxid Redox Signal 2011; 15: 1583–1606.
Google Scholar | Medline
25. Ivanov, AV, Valuev-Elliston, VT, Tyurina, DA. Oxidative stress, a trigger of hepatitis C and B virus-induced liver carcinogenesis. Oncotarget. 2017; 8: 3895–3932.
Google Scholar | Medline
26. Wallace, DC. Mitochondria and cancer. Nat Rev Cnc 2012; 12: 685–698.
Google Scholar | Medline | ISI
27. Ansenberger-Fricano, K, Ganini, D, Mao, M. The peroxidase activity of mitochondrial superoxide dismutase. Free Radic Biol Med 2013; 54: 116–124.
Google Scholar | Medline
28. Kim, YS, Gupta Vallur, P, Phaeton, R. Insights into the dichotomous regulation of SOD2 in cancer. Antioxidants. 2017; 6: 86.
Google Scholar
29. Miriyala, S, Spasojevic, I, Tovmasyan, A. Manganese superoxide dismutase, MnSOD and its mimics. Biochimica et Biophysica Acta. 2012; 1822: 794–814.
Google Scholar | Medline | ISI
30. Hart, PC, Mao, M, de Abreu, AL. MnSOD upregulation sustains the Warburg effect via mitochondrial ROS and AMPK-dependent signalling in cancer. Nat Comm. 2015; 6: 6053.
Google Scholar | Medline
31. Hemachandra, LP, Shin, DH, Dier, U. Mitochondrial superoxide dismutase has a protumorigenic role in ovarian clear cell carcinoma. Canc Res 2015; 75: 4973–4984.
Google Scholar | Medline
32. Lewis, A, Du, J, Liu, J. Metastatic progression of pancreatic cancer: changes in antioxidant enzymes and cell growth. Clin Exp Metastasis 2005; 22: 523–532.
Google Scholar | Medline
33. Miar, A, Hevia, D, Munoz-Cimadevilla, H. Manganese superoxide dismutase (SOD2/MnSOD)/catalase and SOD2/GPx1 ratios as biomarkers for tumor progression and metastasis in prostate, colon, and lung cancer. Free Radic Biol Med 2015; 85: 45–55.
Google Scholar | Medline
34. Wang, R, Yin, C, Li, XX. Reduced SOD2 expression is associated with mortality of hepatocellular carcinoma patients in a mutant p53-dependent manner. Aging. 2016; 8: 1184–1200.
Google Scholar | Medline
35. Kottas, M, Kuss, O, Zapf, A. A modified Wald interval for the area under the ROC curve (AUC) in diagnostic case-control studies. BMC Med Res Meth 2014; 14: 26.
Google Scholar | Medline | ISI
36. Tamai, T, Uto, H, Takami, Y. Serum manganese superoxide dismutase and thioredoxin are potential prognostic markers for hepatitis C virus-related hepatocellular carcinoma. World J Gastroenterol 2011; 17: 4890–4898.
Google Scholar | Medline
View Abstract
Show all authors
Feng Ding, Kehuan Sun, Ningning Sun, ...
First Published May 1, 2019 Research Article
https://doi.org/10.1177/1724600819841619
Article information
Open Access Creative Commons Attribution, Non Commercial 4.0 License
Abstract
Background:
Salivary proteomic analysis has been extensively used in a wide range of cancer, but not in hepatocellular carcinoma. The aim of this study was to identify potential salivary biomarkers for hepatocellular carcinoma clinical screening.
Methods:
In this study, we performed isobaric tags for relative and absolute quantitation (iTRAQ)-based quantitative proteomics analysis to detect differentially expressed proteins between saliva samples from 15 hepatocellular carcinoma patients and 15 healthy controls. Enzyme-linked immunosorbent assay (ELISA) verification was undertaken in saliva samples from 14 hepatocellular carcinoma patients and 14 healthy controls.
Results:
Overall, 133 proteins with significant differential expression level (ratio > 1.5 or < 0.67) were detected. Using bioinformatic analysis, two candidate proteins were selected and subsequently verified by ELISA. The increased expression of superoxide dismutase 2, mitochondrial (SOD2) in hepatocellular carcinoma patients was confirmed by ELISA, with an area under the curve value of 0.9082.
Conclusions:
iTRAQ-based quantitative proteomics revealed that SOD2 might serve as a potential salivary biomarker for hepatocellular carcinoma detection. Our results indicated that a noninvasive and inexpensive salivary test might be established for hepatocellular carcinoma detection.
Keywords Hepatocellular carcinoma, iTRAQ, salivary biomarker, SOD2
Introduction
Global Cancer Statistics in 2012 show that primary liver cancer has been the fifth most common malignant tumor in men, and the sixth in women.1 The most frequent primary liver cancer is hepatocellular carcinoma (HCC). The leading cause of HCC is a viral infection with hepatitis virus B and/or hepatitis C.2 HCC affects approximately 1 million people annually, with the incidence equal to the mortality rate, and approximately half occur in China.1,3
Currently, only a limited number of drugs have been proven as effective treatment options for HCC, and the disease often recurs even after aggressive local therapy.4,5 One of the main reasons of the unsatisfactory curative effect is the delay in diagnosis due to the fact that early cancers exhibit non-specific symptoms.3 Thus, early diagnosis is crucial for improving the high mortality rate in HCC. In the last two decades, the increasing understanding of tumorigenesis and the development of high-throughput genomics and proteomics techniques have led to the identification of a number of biomarkers for HCC, including α-fetoprotein (AFP), lectin-bound AFP (AFP-L3%), Golgi protein 73, interleukin-6, des-γ carboxyl prothrombin, osteopontin, glypican-3, and squamous cell carcinoma antigen.3,6–8 However, with relatively low sensitivity and specificity, most single biomarkers are still unsatisfactory in the diagnosis of HCC. Besides, these biomarkers are mainly tissue and serum-based. Since blood sampling is a painful procedure, a non-invasive diagnostic test for cancer is urgently needed.
In recent years, it has been recognized that saliva offers an easy, fast, low-cost and non-invasive approach for the diagnosis of diseases.9 Human saliva contains proteins that can be informative for underlying pathogenetic process in oral and systemic diseases.10,11 Therefore, a rising number of proteomic analyses of saliva from several diseases (e.g. breast cancer, oral cancer, and lung cancer) were performed to identify salivary biomarkers in cancer.12–14 However, to the best of our knowledge, very few—if any—studies have been performed to investigate HCC salivary biomarkers. The exploration of high sensitivity and specificity biomarkers in saliva are crucial for improving the diagnostic rate, the treatment effect, and the curative satisfaction in HCC patients.3
Currently, isobaric tags for relative and absolute quantification (iTRAQ) has been widely used in a number of proteomic studies. As a powerful quantitative proteome technique, iTRAQ has relatively high sensitivity and allows the identification of numerous proteins compared with traditional proteome approaches.15 In this study, we performed iTRAQ-based quantitative proteomic analysis on saliva samples from HCC patients and healthy controls. We then explored the protein profile differences in HCC patients and healthy controls. We aimed to reveal a potential salivary biomarker for liver cancer screening.
Materials and methods
Patients and saliva samples collection
With ethically approved informed consent, saliva samples were collected from a test set of 30 subjects and a validation set of 28 subjects at the First Affiliated Hospital of Hunan University of Chinese Medicine (Supplementary Table 1). All of the patients were diagnosed as HCC and had not received any prior treatment in the form of chemotherapy, radiotherapy, or surgery before sample collection. A well-defined and standardized protocol was used for collection, storage, and processing of the saliva samples.11 Unstimulated whole saliva samples were collected between 7 a.m. and 8 a.m. with prior mouth rinsing with water. The donors were asked to abstain from eating, drinking, smoking, or using oral hygiene products for at least 1 hour before collection. The samples, once collected, were immediately placed on ice and were then centrifuged at 3000 rpm for 10 min at 4°C to remove debris and cells. The supernatant was then collected and PMSF (Sigma, St. Louis, MO, USA) were added in the collected samples to a final concentration of 1mM to ensure preservation of the protein integrity. The samples were immediately aliquoted into smaller volumes (200 μL) and stored at −80°C until further processing.
Protein preparation and iTRAQ labeling
Each saliva sample was dissolved in lysis buffer (7M urea, 2M thiourea, and 0.1% CHAPS) and mixed using vortex. After sonication, the sample was incubated for 30 minutes at room temperature and then centrifuged at 15,000 rpm at 4°C for 20 minutes. The supernatant was immediately aliquoted into smaller volumes and stored at −80°C. The protein was quantified using Bradford protein assay with bovine serum albumin as standard.16 The samples were pooled into four groups: HCC group 1 (n=5), HCC group 2 (n=5), HCC group 3 (n=5), and healthy controls (n=15). Each group has two technical replicates. In total, there are eight pooled samples for the iTRAQ labelling. For making the pool, an equal amount of protein was used from each sample to ensure that the total amount of protein was 100 μg. Protein reduction was then carried out by adding 2 μL reducing reagent (50 mM tris-(2-carboxyethyl) phosphine) (AB Sciex, PN:4390812) into 100 μg protein solution and incubated for 1 h at 60°C. Reduced protein was subsequently alkylated with 1 μL Cysteine-Blocking Reagent (200 mM methyl methanethiosulfonate in isopropanol) (AB Sciex, PN:4390812) for 10 minutes at room temperature. The alkylated protein was added to a 10K ultrafiltration tube and centrifuged at 12,000 rpm for 20 min. Finally, the protein samples were digested with 4 μg trypsin (AB Sciex, PN: 4370285) at 37°C for overnight. The reaction was terminated by adding 50 μL dissolution buffer (0.5 M triethylammonium bicarbonate) (AB Sciex, PN:4390812). The resulting peptides were subsequently labeled using iTRAQ 8-plex kit (AB Sciex, PN:4390812) according to the manufacturer’s instructions. The two technical replicates of healthy controls were labeled using 113 and 114 tags. For the technical replicates of the HCC groups, 115 and 116 tags (HCC group 1), 117 and 118 tags (HCC group 2), and 119 and 121 tags (HCC group 3) were used. Then the iTRAQ labeled peptides were mixed and dried using a vacuum centrifuge.
Liquid chromatography-mass spectometry/MS analysis
The iTRAQ labeled peptide mixtures were separated by high pH reversed-phase liquid chromatography. The fractions were re-dissolved in 100 μL of phase A (98% ddH2O, 2% acetonitrile, pH 10) and centrifuged at 14,000 rpm for 20 min. The supernatant was collected and loaded onto a Durashell C18 nano trap column (4.6 mm × 250 mm, 5 μm 100 Å; Agela, Catalog Number: DC952505-0) in an HPLC system (RIGOL L-3000). Peptides were eluted by running a 5% to 95% phase B gradient (98% acetonitrile, 2% ddH2O, pH 10) for 72 min at a flow rate of 700 nL/min. The eluted peptides were collected at a rate of 1 tube/min and later were pooled into 10 fractions according to the variations in peak intensity. Pooled peptides were then dried using a speed vacuum centrifuge. The dried labeled peptide fraction was re-dissolved in 20 μL 2% methanol (Sigma-Aldrich, 14262, USA) and 0.1 % formic acid (Sigma-Aldrich, 56302, USA) and analyzed using a Q-Exactive mass spectrometer (Thermo Scientific, USA) combined with a Thermo Scientific EASY-nLC 1000 System (Nano HPLC). The peptides were loaded onto an Acclaim PepMap100 column (2cm × 100μm, C18, 5μm) and eluted at 350 nL/min onto an EASY-Spray column (12 cm × 75 μm, C18, 3μm) over a 90 min gradient. The two mobile phases were phase A (100% dd H2O, 0.1% formic acid) and phase B (100% acetonitrile, 0.1% formic acid). Key parameters for Q-Exactive were set as: spray voltage: 2.1KV, capillary temperature: 250°C, ion source: EASY-Spray source, declustering potential (DP): 100 V; full MS: resolution: 70,000 full width at half mazimum (FWHM), full scan AGC target: 1e6, full scan max IT: 60 ms, scan range: 350–1800 m/z; dd-MS2: resolution: 17,500 FWHM, AGC target: 5e6, maximum IT: 70 ms, intensity threshold: 5E + 03, fragmentation methods: HCD, NCE: 29%, top N: 20. Raw mass data were processed using Proteome Discoverer 1.4 (Thermo Scientific, USA) and searched against the human database of UniProtKB/Swiss-Prot (release-2017_10/). The searching parameters were as follows: enzyme: trypsin, max missed cleavages: 2, static modification: carbamidomethyl (C), dynamic modification: iTRAQ8plex (N-term), iTRAQ8plex (K) and oxidation (M), precursor ion mass tolerance: 15 ppm, fragment ion mass tolerance: 20 mmu.
Data analysis
Peptides with at least 95% confidence threshold and a false discovery rate <0.01 were considered as identified. Proteins with at least one unique peptide were used for further quantification analysis. R programming language and student’s t test were used to perform statistical analysis. Mean and standard deviation was calculated to define the fold change ratio between technical replicates of HCC groups and healthy controls. Proteins with a fold-change ratio larger than 1.5 or less than 0.67 (average ratio of three HCC groups) and a P value <0.05 were deemed as significantly differentially expressed. Gene ontology annotations were completed using DAVID.17 Pathway analysis of the differentially expressed proteins was performed using KEGG. The protein–protein interaction network was further created using STRING functional protein association networks website (http://string-db.org). The threshold values for STRING network construction were as follows: meaning of network edges: evidence; minimum required interaction score: 0.7; display simplifications: hide disconnected nodes in the network. The receiver operating characteristic (ROC) curve was constructed and the area under the curve (AUC) value was calculated in Graphpad Prism. For all analysis, a P value < 0.05 was considered statistically significant.
Enzyme-linked immunosorbent assay
Enzyme-linked immunosorbent assay (ELISA) tests for superoxide dismutase 2, mitochondrial (SOD2) and haptoglobin (HP) were performed using Abcam ELISA kits, according to the manufacturer’s instructions. The concentrations of both proteins were assayed in saliva samples from 14 HCC patients and 14 healthy controls.
Results
Quantitative proteomic analysis of saliva by iTRAQ
To identify the differentially expressed proteins between HCC patients and healthy controls, we compared the saliva samples from 15 HCC patients and 15 healthy controls using iTRAQ. Since we consider the biological variability among the patients, we chose to divide the patients’ saliva into three groups in order to get a more reliable biomarker candidate with higher specificity and sensitivity. And each group consisted of 5 HCC patients. A total of 15 saliva samples from healthy controls were pooled into one group. Each group had two technical replicates. Finally, eight subgroups were compared in an 8-Plex iTRAQ experiment. A total of 1296 proteins with a P value <0.05 were detected. To identify the differentially expressed proteins in the saliva of HCC patients, the quantification data between HCC groups and healthy controls were compared, and we finally identified 133 significantly differentially expressed proteins (fold-change > 1.5 or < 0.67 and a P value <0.05) (Table 1). Among them, 77 proteins were found to be significantly up-regulated and 56 proteins were found to be significantly down-regulated in the replicates of all HCC groups .
Table
Table 1. List of 133 significantly differentially expressed proteins.
Table 1. List of 133 significantly differentially expressed proteins.
View larger version
Biological function, pathway, and network analysis
To obtain the functional characteristics of the differentially expressed proteins, the DAVID online analysis tool was used to determine enriched gene ontology (GO) terms.17 GO ontology covers three domains: biological process, molecular function and cellular component. Figure 1 shows the top 10 enriched GO terms of each domain (P < 0.05). In biological process, the differentially expressed proteins were enriched in retina homeostasis, positive regulation of B-cell activation, innate immune response, phagocytosis, and response to hydrogen peroxide. In molecular function, the main enriched GO terms were associated with immunoglobulin receptor binding, antigen binding, antioxidant activity, serine-type endopeptidase activity and protease binding. In the cellular component, the main enriched GO terms were related to specific extracellular exosome, immunoglobulin complex and blood microparticle. To identify relevant biology pathways of the differentially expressed proteins, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was performed (https://www.kegg.jp/). Figure 2 represents the enriched KEGG pathways of the differentially expressed proteins. The pathways with a P <0.05 were considered enriched pathways in which at least two differentially expressed proteins were involved. We next constructed the functional protein association network of the 133 differentially expressed proteins using String.18 The protein–protein interaction network is shown in Figure 3. Since proteins interacting with others were normally deemed as potential candidates of functional proteins, the disconnected nodes were then not displayed in the network. With interaction scores larger than 0.7, 39 proteins that formed 28 high confidence protein–protein associations constituted a complex, multi-centered interaction network (Figure 3). As shown in Figure 3, 12 genes (SOD2, SOD1, NME1, HNRNPA2B1, LSM5, NAA38, GRB2, YWHAH, OAT, ARG1, OTC, POTEJ) were significant hub genes interacting with at least two other genes in the interaction network.
figure
Figure 1. Top 10 enriched GO terms of differentially expressed proteins. P <0.05 was considered to be statistically significant.
GO: gene ontology.
figure
Figure 2. Statistically significantly enriched KEGG pathways of differentially expressed proteins. P <0.05 was considered statistically significant.
KEGG: Kyoto Encyclopedia of Genes and Genomes.
figure
Figure 3. The protein–protein interaction network determined by STRING on differentially expressed proteins. Interactions of the differentially expressed proteins are obtained by searching the STRING database with a confidence cutoff of 0.7. Disconnected nodes are not displayed.
Candidate biomarker verification
Besides liver cancer, we have performed salivary proteomics analyses of multiple diseases, including diabetes mellitus, gastric cancer, colon cancer, and nephritis (data not shown). Proteins (i.e. TSPAN1, TFF3) that were differentially expressed in two or more diseases were excluded for further verification. According to the average fold change ratio, the availability of ELISA kits and association with HCC in previous studies, two proteins were finally selected for verification, including SOD2 and HP. Based on the iTRAQ result, the expression levels of SOD2 and HP were significantly higher in all three HCC groups (Supplementary Table 2). We then assessed the expression level of these two proteins by ELISA to verify the iTRAQ result using a new sample set (14 HCC saliva samples and 14 healthy control saliva samples). As shown in box plot diagrams for SOD2 and HP (Figure 4A), statistically significant differences of the concentrations of SOD2 and HP were seen between the HCC group and the control group. We further focused on ROC curves for evaluation. The ROC curves for both proteins are shown in Figure 4B. The AUC values for SOD2 and HP were 0.9082 and 0.6939, respectively.
figure
Figure 4. Salivary biomarker evaluation and validation. (a) ELISA quantification data of SOD2 and HP in the saliva of 14 HCC patients and 14 healthy controls. (b) ROC curves for SOD2 and HP.
ELISA: enzyme-linked immunosorbent assay; HCC: hepatocellular carcinoma; HP: haptoglobin; ROC: receiver operating characteristic; SOD2: superoxide dismutase 2, mitochondrial.
Discussion
Currently, to our certain knowledge, a number of serum/tissue biomarkers have been identified for possible early detection of HCC. Although AFP has been widely used as a significant biomarker in HCC detection over the last two decades, the diagnostic accuracy of AFP is questioned, and the sensitivity of AFP is restricted to 25% when tumors size <3 cm.19 AFP was excluded from the surveillance criteria in the HCC guidelines published by the American Association for the Study of Liver Diseases in 2010. Available studies have shown that the diagnostic performance of other biomarkers alone or combined with AFP only moderately increased in the detection of HCC compared to AFP alone.20
Human saliva has become an attractive early detection biofluid in recent years. The present study sought to conduct an in-depth analysis of the salivary proteomics of HCC and explore potential biomarkers for HCC detection. To our knowledge, this is the first study of salivary proteomics in HCC using the iTRAQ technique. In this study, we obtained a total of 133 proteins that showed significant expression difference between HCC patients and healthy controls. In a few of the proteins with a relatively high fold-change ratio, high variability of the protein ratio was seen among the three HCC groups versus the control comparisons (Table 1). The variability might be due to the limitation of the iTRAQ technique. iTRAQ was proved to be more sensitive for proteins with small fold-change value, and greater discrepancies were often seen for the higher ratios measured by iTRAQ.21,22 Although high variability of the protein ratio was observed, the expression trends of the same protein were consistent among the three HCC groups (all up-regulated or down-regulated), rather than an opposite expression pattern. After reviewing the association of differentially expressed proteins with HCC in the published literature, and checking the availability of ELISA kits, two proteins (SOD2, HP) were further validated using ELISA experiments, and the ELISA results were consistent with the iTRAQ experiment. We then created the ROC curve for SOD2 and HP, yielding an AUC value of 0.9082 for SOD2 and 0.6627 for HP. Thus, combining computational prediction and experimental verification, we finally identified that SOD2 might be a potential salivary biomarker for HCC detection.
Superoxide dismutase (SOD) is an antioxidant enzyme that catalyzes the dismutation of the super-oxide anion to molecular oxygen and H2O2. In humans, there are three forms of SOD: the copper/zinc SOD (Cu/ZnSOD or SOD1), the manganese SOD (MnSOD or SOD2), and the extracellular SOD (EcSOD or SOD3, also a Cu/ZnSOD).23 SOD1 is expressed mainly in the cytoplasm, and SOD3 is the major SOD in the vascular extracellular space, whereas SOD2 is exclusively localized in the mitochondrial matrix.24,25 Mitochondria has been shown to have important roles in cancer26 and SOD2 plays a significant role in maintaining a balance between reactive oxygen species generation and oxidative defenses for the integrity of mitochondria and the maintenance of its function.27 SOD2 expression varies in different tumor types. Usually, as a tumor suppressor, the decreased expression of SOD2 is often observed during tumor initiation, while the SOD2 level increases during tumor progression to the metastatic stage, and seems to act as an oncogene.28,29 The elevated level of SOD2 has been found in patients with lung, ovarian, pancreatic, prostate, and colon cancer.30–33 The expression of SOD2 in the serum and liver tissue of HCC patients has also been investigated recently. Although Wang et al.34 found that both SOD2 mRNA and protein expression decreased in the HCC patients compared with matching non-cancerous liver tissues, most studies have shown increased SOD2 levels in HCC patients.35 As serum MnSOD levels were significantly elevated in patients with HCV-related HCC than in patients without HCC, Tamai et al.36 suggested serum MnSOD may be a useful serum biomarker for HCC detection. In our present study, we revealed significantly higher SOD2 levels in the saliva samples of HCC patients (2.55-fold change), in comparison with controls. In particular, we performed a ROC curve analysis for the evaluation of biomarker accuracy, and the AUC value of SOD2 was 0.9082. The AUC value is a measure to evaluate and compare different biomarkers in the clinical diagnosis of diseases. An AUC value > 0.9 indicated significant diagnostic accuracy; thus there might be a great possibility for SOD2 to serve as a diagnostic biomarker for HCC.
The present study is the first to perform a quantitative proteomic study by iTRAQ to profile differentially expressed proteins in the saliva samples of HCC patients. We identified SOD2 as a potential salivary biomarker for HCC screening and detection, due to its high-level expression and sensitivity in HCC patients. Validation in a large number of HCC cases would be necessary to determine more rigorous sensitivity and specificity values. Our results provide a potential target for the diagnosis of liver cancer in a non-invasive and cost-effective way.
Author contributions
Feng Ding and Kehuan Sun contributed equally to this study.
Declaration of conflicting interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by grants from National Natural Science Foundation of China (81701071), China Postdoctoral Science Foundation (2017M622915), Shenzhen Science and Technology Program (JCYJ20170306171013613), the Science and Technology Project of Guangdong (2016A020226033), and the Sanming Project of Medicine in Shenzhen (SZSM201612049).
Supplemental material
Supplemental material for this article is available online.
ORCID iD
Feng Ding https://orcid.org/0000-0001-6397-5532
References
1. Torre, LA, Bray, F, Siegel, RL. Global cancer statistics, 2012. CA Cancer J Clin 2015; 65: 87–108.
Google Scholar | Medline | ISI
2. Han, C, Liao, X, Qin, W. EGFR and SYNE2 are associated with p21 expression and SYNE2 variants predict post-operative clinical outcomes in HBV-related hepatocellular carcinoma. Sci Rep 2016; 6: 31237.
Google Scholar | Medline
3. Zhao, YJ, Ju, Q, Li, GC. Tumor markers for hepatocellular carcinoma. Mol Clin Oncol 2013; 1: 593–598.
Google Scholar | Medline
4. Zhu, AX, Duda, DG, Sahani, DV, Jain, RK. HCC and angiogenesis: possible targets and future directions. Nat Rev Clin Oncol 2011; 8: 292–301.
Google Scholar | Medline | ISI
5. Bupathi, M, Kaseb, A, Meric-Bernstam, F. Hepatocellular carcinoma: Where there is unmet need. Mol Oncol 2015; 9: 1501–1509.
Google Scholar | Medline
6. Chauhan, R, Lahiri, N. Tissue- and serum-associated biomarkers of hepatocellular carcinoma. Biomarkers Canc 2016; 8: 37–55.
Google Scholar | Medline
7. Zhang, B, Finn, RS. Personalized clinical trials in hepatocellular carcinoma based on biomarker selection. Liver Cancer 2016; 5: 221–232.
Google Scholar | Medline
8. Reichl, P, Mikulits, W. Accuracy of novel diagnostic biomarkers for hepatocellular carcinoma: An update for clinicians (Review). Oncol Rep 2016; 36: 613–625.
Google Scholar | Medline
9. Zhang, A, Sun, H, Wang, P. Salivary proteomics in biomedical research. Clinica Chimica Acta 2013; 415: 261–265.
Google Scholar | Medline
10. Hu, S, Arellano, M, Boontheung, P. Salivary proteomics for oral cancer biomarker discovery. Clin Canc Res 2008; 14: 6246–6252.
Google Scholar | Medline | ISI
11. Zhang, L, Xiao, H, Karlan, S. Discovery and preclinical validation of salivary transcriptomic and proteomic biomarkers for the non-invasive detection of breast cancer. PloS One. 2010; 5: e15573.
Google Scholar | Medline | ISI
12. Mishra, S, Saadat, D, Kwon, O. Recent advances in salivary cancer diagnostics enabled by biosensors and bioelectronics. Biosens Bioelectron 2016; 81: 181–197.
Google Scholar | Medline
13. Miller, CS, Foley, JD, Bailey, AL. Current developments in salivary diagnostics. Biomark Med 2010; 4: 171–189.
Google Scholar | Medline | ISI
14. Yeh, CK, Christodoulides, NJ, Floriano, PN. Current development of saliva/oral fluid-based diagnostics. Tex Dent J 2010; 127: 651–661.
Google Scholar | Medline
15. Wiese, S, Reidegeld, KA, Meyer, HE. Protein labeling by iTRAQ: a new tool for quantitative mass spectrometry in proteome research. Proteomics. 2007; 7: 340–350.
Google Scholar | Medline | ISI
16. Bradford, MM. A rapid and sensitive method for the quantitation of microgram quantities of protein utilizing the principle of protein–dye binding. Anal Biochem 1976; 72: 248–254.
Google Scholar | Medline | ISI
17. Huang da, W, Sherman, BT, Lempicki, RA. Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res 2009; 37: 1–13.
Google Scholar | Medline | ISI
18. Szklarczyk, D, Franceschini, A, Wyder, S. STRING v10: protein–protein interaction networks, integrated over the tree of life. Nucleic Acids Res 2015; 43: D447–452.
Google Scholar | Medline | ISI
19. Wang, CS, Lin, CL, Lee, HC. Usefulness of serum des-gamma-carboxy prothrombin in detection of hepatocellular carcinoma. WorldJ Gastroenterol 2005; 11: 6115–6119.
Google Scholar | Medline | ISI
20. Hu, B, Tian, X, Sun, J. Evaluation of individual and combined applications of serum biomarkers for diagnosis of hepatocellular carcinoma: a meta-analysis. Int J Mol Sci 2013; 14: 23559–23580.
Google Scholar | Medline
21. Shirran, SL, Botting, CH. A comparison of the accuracy of iTRAQ quantification by nLC-ESI MSMS and nLC-MALDI MSMS methods. J Proteomics. 2010; 73: 1391–1403.
Google Scholar | Medline
22. Evans, C, Noirel, J, Ow, SY. An insight into iTRAQ: where do we stand now? Anal Bioanal Chem 2012; 404: 1011–1027.
Google Scholar | Medline
23. Che, M, Wang, R, Li, X. Expanding roles of superoxide dismutases in cell regulation and cancer. Drug Discov Today 2016; 21: 143–149.
Google Scholar | Medline
24. Fukai, T, Ushio-Fukai, M. Superoxide dismutases: role in redox signaling, vascular function, and diseases. Antioxid Redox Signal 2011; 15: 1583–1606.
Google Scholar | Medline
25. Ivanov, AV, Valuev-Elliston, VT, Tyurina, DA. Oxidative stress, a trigger of hepatitis C and B virus-induced liver carcinogenesis. Oncotarget. 2017; 8: 3895–3932.
Google Scholar | Medline
26. Wallace, DC. Mitochondria and cancer. Nat Rev Cnc 2012; 12: 685–698.
Google Scholar | Medline | ISI
27. Ansenberger-Fricano, K, Ganini, D, Mao, M. The peroxidase activity of mitochondrial superoxide dismutase. Free Radic Biol Med 2013; 54: 116–124.
Google Scholar | Medline
28. Kim, YS, Gupta Vallur, P, Phaeton, R. Insights into the dichotomous regulation of SOD2 in cancer. Antioxidants. 2017; 6: 86.
Google Scholar
29. Miriyala, S, Spasojevic, I, Tovmasyan, A. Manganese superoxide dismutase, MnSOD and its mimics. Biochimica et Biophysica Acta. 2012; 1822: 794–814.
Google Scholar | Medline | ISI
30. Hart, PC, Mao, M, de Abreu, AL. MnSOD upregulation sustains the Warburg effect via mitochondrial ROS and AMPK-dependent signalling in cancer. Nat Comm. 2015; 6: 6053.
Google Scholar | Medline
31. Hemachandra, LP, Shin, DH, Dier, U. Mitochondrial superoxide dismutase has a protumorigenic role in ovarian clear cell carcinoma. Canc Res 2015; 75: 4973–4984.
Google Scholar | Medline
32. Lewis, A, Du, J, Liu, J. Metastatic progression of pancreatic cancer: changes in antioxidant enzymes and cell growth. Clin Exp Metastasis 2005; 22: 523–532.
Google Scholar | Medline
33. Miar, A, Hevia, D, Munoz-Cimadevilla, H. Manganese superoxide dismutase (SOD2/MnSOD)/catalase and SOD2/GPx1 ratios as biomarkers for tumor progression and metastasis in prostate, colon, and lung cancer. Free Radic Biol Med 2015; 85: 45–55.
Google Scholar | Medline
34. Wang, R, Yin, C, Li, XX. Reduced SOD2 expression is associated with mortality of hepatocellular carcinoma patients in a mutant p53-dependent manner. Aging. 2016; 8: 1184–1200.
Google Scholar | Medline
35. Kottas, M, Kuss, O, Zapf, A. A modified Wald interval for the area under the ROC curve (AUC) in diagnostic case-control studies. BMC Med Res Meth 2014; 14: 26.
Google Scholar | Medline | ISI
36. Tamai, T, Uto, H, Takami, Y. Serum manganese superoxide dismutase and thioredoxin are potential prognostic markers for hepatitis C virus-related hepatocellular carcinoma. World J Gastroenterol 2011; 17: 4890–4898.
Google Scholar | Medline
View Abstract
Δεν υπάρχουν σχόλια:
Δημοσίευση σχολίου