iTRAQ-based proteomics reveals SOD2 as a potential salivary biomarker in liver cancer

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 1.

List of 133 significantly differentially expressed proteins.

Uniprot ID	Protein description	MW [kDa]	Fold change^a				P value	Score	Coverage	Unique Peptides
Uniprot ID	Protein description	MW [kDa]	G1/C	G2/C	G3/C	G/C	P value	Score	Coverage	Unique Peptides
P58062	Serine protease inhibitor Kazal-type 7	9.2	1.66 ±0.38	1.88 ±0.19	2.39 ±0.76	1.98 ±0.31	2.39E-07	0	20	2
P02788	Lactotransferrin	78.1	1.54 ±0.03	1.64 ±0.03	3.08 ±0.38	2.09 ±0.70	1.39E-06	7600.3	74.65	47
P01767	Ig heavy chain V-III region BUT	12.4	1.56 ±0.12	1.77 ±0.11	1.85 ±0.24	1.73 ±0.12	1.81E-11	512.99	35.65	2
P59665	Neutrophil defensin 1	10.2	1.61 ±0.07	1.84 ±0.55	3.02 ±0.50	2.16 ±0.62	1.24E-06	97.46	20.21	4
P00738	Haptoglobin	45.2	1.62 ±0.06	1.82 ±0.07	2.44 ±0.15	1.96 ±0.35	1.71E-09	2019.59	56.4	26
P28799	Granulins	63.5	1.51 ±0.15	1.66 ±0.02	2.95 ±0.83	2.04 ±0.65	4.24E-06	463.07	26.98	11
Q8TED4	Sugar phosphate exchanger 2	54.4	1.51 ±0.18	2.41 ±0.16	1.80 ±0.17	1.91 ±0.38	9.50E-09	0	2.2	1
O43768	Alpha-endosulfine	13.4	1.59 ±0.10	1.56 ±0.17	1.81 ±0.21	1.65 ±0.11	2.00E-11	44.6	5.79	1
P01781	Ig heavy chain V-III region GAL	12.7	1.71 ±0.16	1.61 ±0.07	2.03 ±0.43	1.78 ±0.18	1.16E-09	788.3	40.52	3
P01859	Ig gamma-2 chain C region	35.9	1.53 ±0.18	1.90 ±0.07	1.76 ±0.20	1.73 ±0.15	3.42E-11	2533.59	48.47	5
P08236	Beta-glucuronidase	74.7	1.72 ±0.43	2.23 ±0.26	2.73 ±0.32	2.23 ±0.41	3.01E-08	99.86	5.38	4
P01776	Ig heavy chain V-III region WAS	13.1	1.54 ±0.24	1.65 ±0.05	1.96 ±0.35	1.72 ±0.18	1.13E-09	1273.29	23.93	2
P31942	Heterogeneous nuclear ribonucleoprotein H3	36.9	1.51 ±0.50	1.83 ±0.64	1.57 ±0.16	1.64 ±0.14	3.11E-07	40.55	4.91	1
O95071	E3 ubiquitin-protein ligase UBR5	309.2	1.59 ±0.35	1.77 ±0.26	1.63 ±0.37	1.66 ±0.08	4.94E-09	0	0.61	1
P80188	Neutrophil gelatinase-associated lipocalin	22.6	1.54 ±0.24	1.87 ±0.06	3.37 ±0.64	2.26 ±0.80	3.88E-06	680.65	62.63	13
P06870	Kallikrein-1	28.9	1.65 ±0.16	2.59 ±0.32	2.37 ±0.41	2.20 ±0.40	1.87E-08	2063	48.09	9
P01612	Ig kappa chain V-I region Mev	11.9	1.57 ±0.27	2.28 ±0.54	3.03 ±0.29	2.29 ±0.60	3.72E-07	222.09	36.7	3
P35326	Small proline-rich protein 2A	8	1.71 ±0.17	1.61 ±0.18	1.56 ±0.14	1.63 ±0.06	5.10E-12	128.15	90.28	1
P08758	Annexin A5	35.9	1.59 ±0.37	1.58 ±0.20	2.38 ±0.72	1.85 ±0.37	6.95E-07	58.58	13.44	3
P29034	Protein S100-A2	11.1	1.88 ±0.09	1.54 ±0.19	2.39 ±0.71	1.94 ±0.35	1.56E-07	129.04	27.55	3
Q6UW32	Insulin growth factor-like family member 1	12.4	2.03 ±0.09	1.92 ±0.11	4.91 ±0.23	2.95 ±1.38	2.17E-05	117.88	39.09	3
P02763	Alpha-1-acid glycoprotein 1	23.5	1.73 ±0.25	2.12 ±0.12	3.38 ±0.52	2.41 ±0.70	5.94E-07	1132.52	40.3	5
P13473	Lysosome-associated membrane glycoprotein 2	44.9	1.77 ±0.21	4.39 ±1.09	2.94 ±0.28	3.03 ±1.07	6.43E-06	125.55	9.27	4
P01770	Ig heavy chain V-III region NIE	13.2	2.06 ±0.09	2.76 ±0.68	3.43 ±0.37	2.75 ±0.56	6.75E-08	109.97	15.97	2
P00441	Superoxide dismutase [Cu-Zn]	15.9	1.50 ±0.41	1.89 ±0.33	2.25 ±0.09	1.88 ±0.31	1.72E-08	556.83	64.94	6
P15328	Folate receptor alpha	29.8	1.92 ±0.09	2.21 ±0.10	1.82 ±0.20	1.98 ±0.17	5.86E-12	215.78	33.46	6
P30048	Thioredoxin-dependent peroxide reductase, mitochondrial	27.7	1.77 ±0.29	1.65 ±0.05	1.55 ±0.24	1.66 ±0.09	1.19E-10	198.11	24.22	6
P01623	Ig kappa chain V-III region WOL	11.7	1.83 ±0.20	1.82 ±0.23	2.33 ±0.16	1.99 ±0.24	2.67E-10	1733.17	52.29	1
Q5VV41	Rho guanine nucleotide exchange factor 16	80.1	2.23 ±0.74	1.60 ±0.94	2.28 ±1.17	2.04 ±0.31	3.62E-05	0	2.12	1
P22532	Small proline-rich protein 2D	7.9	2.24 ±0.10	2.51 ±0.35	2.81 ±0.76	2.52 ±0.23	8.04E-09	112.1	90.28	2
Q9UGM3	Deleted in malignant brain tumors 1 protein	260.6	1.85 ±0.30	1.52 ±0.10	2.11 ±0.35	1.83 ±0.24	4.08E-09	10205.8	42.52	37
P01771	Ig heavy chain V-III region HIL	13.6	1.96 ±0.51	1.87 ±0.32	2.82 ±0.45	2.22 ±0.43	1.17E-07	30.47	15.7	2
O60701	UDP-glucose 6-dehydrogenase	55	1.62 ±0.64	1.95 ±0.31	2.59 ±0.53	2.05 ±0.40	4.60E-07	0	4.66	2
P01598	Ig kappa chain V-I region EU	11.8	1.99 ±0.27	3.11 ±0.41	2.31 ±0.13	2.47 ±0.47	1.38E-08	890.75	32.41	1
Q9UL25	Ras-related protein Rab-21	24.3	2.32 ±0.74	2.88 ±1.00	4.31 ±0.98	3.17 ±0.84	3.75E-06	27.63	4.89	1
O75594	Peptidoglycan recognition protein 1	21.7	1.78 ±0.53	1.68 ±0.38	2.96 ±0.90	2.14 ±0.58	5.19E-06	529.03	48.98	5
P01834	Ig kappa chain C region	11.6	1.95 ±0.37	1.99 ±0.40	2.22 ±0.15	2.05 ±0.12	6.24E-10	14902.45	97.17	11
P01040	Cystatin-A	11	1.92 ±0.36	2.12 ±0.19	4.16 ±0.93	2.73 ±1.01	8.58E-06	565.67	68.37	6
P06727	Apolipoprotein A-IV	45.4	1.55 ±0.81	4.58 ±2.54	2.21 ±0.71	2.78 ±1.30	9.28E-04	0	2.78	1
P62158	Calmodulin	16.8	2.06 ±0.62	1.67 ±0.22	1.61 ±0.32	1.78 ±0.20	6.87E-08	220.65	50.34	5
P04181	Ornithine aminotransferase, mitochondrial	48.5	2.85 ±0.34	2.01 ±0.19	2.79 ±0.44	2.55 ±0.38	3.96E-09	81.05	18.91	7
Q14508	WAP four-disulfide core domain protein 2	13	2.41 ±0.06	1.81 ±0.30	4.18 ±0.27	2.80 ±1.01	2.19E-06	1383.58	45.16	5
P01624	Ig kappa chain V-III region POM	11.9	2.10 ±0.22	2.41 ±0.18	2.63 ±0.37	2.38 ±0.22	1.35E-10	234.73	24.77	1
P19404	NADH dehydrogenase [ubiquinone] flavoprotein 2, mitochondrial	27.4	1.84 ±0.67	2.21 ±0.05	2.10 ±0.59	2.05 ±0.16	7.09E-08	0	6.83	1
Q9Y5Y6	Suppressor of tumorigenicity 14 protein	94.7	1.85 ±0.66	2.37 ±0.42	1.55 ±0.34	1.92 ±0.34	3.98E-07	0	1.4	1
P31151	Protein S100-A7	11.5	1.86 ±0.54	5.08 ±0.15	2.74 ±1.03	3.23 ±1.36	2.11E-05	1093.07	55.45	5
Q07654	Trefoil factor 3	8.6	1.57 ±0.84	3.35 ±0.88	1.74 ±0.35	2.22 ±0.80	3.03E-05	70.59	38.75	3
P61457	Pterin-4-alpha-carbinolamine dehydratase	12	1.79 ±0.57	1.55 ±0.27	2.75 ±0.57	2.03 ±0.52	1.33E-06	63.08	46.15	5
P06702	Protein S100-A9	13.2	2.41 ±0.16	1.50 ±0.14	7.72 ±0.59	3.88 ±2.74	7.01E-04	9353.11	93.86	13
P01876	Ig alpha-1 chain C region	37.6	2.12 ±0.36	1.96 ±0.50	1.85 ±0.24	1.98 ±0.11	4.04E-09	21897.01	87.54	10
Q96L46	Calpain small subunit 2	27.6	2.17 ±0.40	1.70 ±0.16	2.65 ±0.29	2.17 ±0.39	1.39E-08	0	9.68	1
P01877	Ig alpha-2 chain C region	36.5	2.15 ±0.31	1.79 ±0.62	2.11 ±0.12	2.02 ±0.16	9.48E-09	20445.49	80.59	6
P00480	Ornithine carbamoyltransferase, mitochondrial	39.9	2.06 ±0.59	2.81 ±0.45	1.72 ±0.30	2.20 ±0.46	2.21E-07	19.69	2.26	1
Q96JC1	Vam6/Vps39-like protein	101.7	2.09 ±0.56	1.84 ±0.33	7.21 ±1.73	3.71 ±2.47	8.07E-04	0	2.03	1
P49221	Protein-glutamine gamma-glutamyltransferase 4	77.1	2.24 ±0.43	3.07 ±0.11	2.77 ±0.59	2.69 ±0.34	4.78E-09	46.72	1.02	1
P81133	Single-minded homolog 1	85.5	1.79 ±0.88	1.67 ±0.32	1.57 ±0.10	1.68 ±0.09	6.63E-07	0	1.96	1
P04220	Ig mu heavy chain disease protein	43	1.91 ±0.73	3.10 ±0.95	2.04 ±0.64	2.35 ±0.53	5.09E-06	1142.02	56.01	2
P04179	Superoxide dismutase [Mn], mitochondrial	24.7	2.17 ±0.55	2.09 ±0.41	3.39 ±0.32	2.55 ±0.59	1.83E-07	407.04	33.33	8
P83593	Ig kappa chain V-IV region STH (Fragment)	12.1	3.45 ±0.75	4.80 ±1.73	3.41 ±0.36	3.89 ±0.65	6.96E-07	40.77	19.27	2
Q9BPV8	P2Y purinoceptor 13	40.8	3.58 ±0.56	2.06 ±0.24	2.48 ±0.41	2.71 ±0.64	1.52E-07	0	4.52	1
P49643	DNA primase large subunit	58.8	2.03 ±0.68	1.51 ±0.18	6.00 ±4.44	3.18 ±2.01	8.26E-03	0	1.57	1
P35321	Cornifin-A	9.9	2.15 ±0.92	4.68 ±0.21	1.51 ±0.23	2.78 ±1.37	6.44E-05	321.94	78.65	2
P04632	Calpain small subunit 1	28.3	2.84 ±0.17	1.98 ±0.46	2.91 ±0.20	2.58 ±0.42	4.65E-09	222.65	23.13	4
Q7L0X2	Glutamate-rich protein 6	75.2	2.07 ±1.07	2.24 ±0.70	3.18 ±0.09	2.50 ±0.49	1.47E-06	0	1.51	1
P01743	Ig heavy chain V-I region HG3	12.9	2.79 ±0.42	2.44 ±0.24	2.94 ±0.31	2.72 ±0.21	1.21E-10	188.57	28.21	2
Q6ZP82	Coiled-coil domain-containing protein 141	166.2	2.42 ±1.07	2.26 ±0.56	21.83 ±14.84	8.84 ±9.19	3.99E-02	0	0.97	1
P01764	Ig heavy chain V-III region 23	12.6	3.03 ±0.57	2.25 ±0.10	2.03 ±0.14	2.44 ±0.43	1.39E-08	318.67	37.61	2
O60635	Tetraspanin-1	26.3	3.92 ±0.14	2.56 ±0.45	3.07 ±0.39	3.18 ±0.56	5.82E-09	188.86	5.39	1
O75629	Protein CREG1	24.1	2.50 ±0.96	3.17 ±1.07	3.02 ±0.61	2.90 ±0.29	6.33E-07	43.57	9.55	1
Q96PF2	Testis-specific serine/threonine-protein kinase 2	40.9	2.91 ±1.74	2.66 ±0.57	1.90 ±0.80	2.49 ±0.43	3.29E-05	0	7.54	1
Q9Y3D6	Mitochondrial fission 1 protein	16.9	3.25 ±1.88	2.10 ±0.16	1.58 ±0.14	2.31 ±0.70	1.02E-04	66.02	7.24	1
P31949	Protein S100-A11	11.7	3.67 ±1.59	2.78 ±0.78	3.74 ±1.96	3.40 ±0.44	1.99E-05	547.31	42.86	4
P0CG39	POTE ankyrin domain family member J	117.3	4.84 ±1.09	1.98 ±0.28	1.90 ±0.48	2.91 ±1.37	6.21E-05	508.11	6.74	1
Q86VH2	Kinesin-like protein KIF27	160.2	6.52 ±4.98	7.81 ±0.53	2.57 ±0.47	5.63 ±2.23	3.40E-04	0	0.5	1
Q9ULD9	Zinc finger protein 608	162.1	7.36 ±6.46	16.84 ±10.85	4.63 ±2.52	9.61 ±5.23	4.92E-03	0	0.6	1
Q66K66	Transmembrane protein 198	39.4	12.64 ±9.84	25.92 ±17.96	4.65 ±1.97	14.40 ±8.77	7.93E-03	39.38	2.5	1
Q9Y661	Heparan sulfate glucosamine 3-O-sulfotransferase 4	49.8	16.77 ±13.27	1.56 ±0.21	5.23 ±4.09	7.85 ±6.48	2.82E-02	0	2.41	1
P08754	Guanine nucleotide-binding protein G(k) subunit alpha	40.5	0.49 ±0.42	0.23 ±0.05	0.52 ±0.13	0.41 ±0.13	5.85E-04	58.18	16.1	1
P15374	Ubiquitin carboxyl-terminal hydrolase isozyme L3	26.2	0.23 ±0.17	0.50 ±0.06	0.40 ±0.03	0.38 ±0.11	4.99E-06	0	6.96	1
Q15493	Regucalcin	33.2	0.14 ±0.05	0.30 ±0.03	0.43 ±0.11	0.29 ±0.12	2.62E-05	35.37	1.67	1
Q7Z5M8	Abhydrolase domain-containing protein 12B	40.7	0.23 ±0.13	0.30 ±0.01	0.37 ±0.11	0.30 ±0.06	3.42E-06	0	1.66	1
P08571	Monocyte differentiation antigen CD14	40.1	0.30 ±0.20	0.42 ±0.06	0.35 ±0.05	0.36 ±0.05	2.23E-06	28.09	2.13	1
Q9P0M2	A-kinase anchor protein 7 isoform gamma	39.5	0.39 ±0.26	0.44 ±0.10	0.37 ±0.05	0.40 ±0.03	7.03E-06	0	2.3	1
Q9BYB0	SH3 and multiple ankyrin repeat domains protein 3	184.6	0.24 ±0.09	0.59 ±0.12	0.44 ±0.22	0.42 ±0.14	3.54E-05	0	0.35	1
P58499	Protein FAM3B	26	0.38 ±0.19	0.43 ±0.12	0.39 ±0.04	0.40 ±0.02	8.14E-07	35.12	21.7	4
Q9UHY1	Nuclear receptor-binding protein	59.8	0.37 ±0.19	0.47 ±0.12	0.62 ±0.16	0.49 ±0.10	3.31E-06	0	2.06	1
Q9H4M9	EH domain-containing protein 1	60.6	0.44 ±0.24	0.45 ±0.09	0.37 ±0.02	0.42 ±0.04	1.89E-06	0	1.31	1
Q7Z591	AT-hook-containing transcription factor	155	0.66 ±0.44	0.12 ±0.05	0.30 ±0.08	0.36 ±0.22	4.54E-03	0	0.56	1
O95777	U6 snRNA-associated Sm-like protein LSm8	10.4	0.58 ±0.34	0.50 ±0.05	0.44 ±0.05	0.51 ±0.06	5.58E-06	29.4	7.29	1
Q14289	Protein-tyrosine kinase 2-beta	115.8	0.35 ±0.09	0.44 ±0.04	0.53 ±0.06	0.44 ±0.07	1.66E-08	0	0.79	1
Q9GZZ8	Extracellular glycoprotein lacritin	14.2	0.28 ±0.02	0.56 ±0.04	0.30 ±0.01	0.38 ±0.13	1.11E-06	0	20.29	3
O75015	Low affinity immunoglobulin gamma Fc region receptor III-B	26.2	0.50 ±0.25	0.30 ±0.01	0.54 ±0.04	0.45 ±0.10	4.70E-06	57.65	20.17	2
P45877	Peptidyl-prolyl cis-trans isomerase C	22.7	0.53 ±0.28	0.57 ±0.09	0.52 ±0.10	0.54 ±0.02	7.52E-07	0	13.68	2
P01712	Ig lambda chain V-II region WIN	11.7	0.47 ±0.21	0.46 ±0.08	0.50 ±0.03	0.48 ±0.02	9.98E-08	20.2	14.41	1
Q16378	Proline-rich protein 4	15.1	0.26 ±0.01	0.25 ±0.06	0.28 ±0.05	0.26 ±0.01	8.17E-10	0	15.67	2
P22626	Heterogeneous nuclear ribonucleoproteins A2/B1	37.4	0.31 ±0.02	0.39 ±0.04	0.34 ±0.03	0.35 ±0.03	5.35E-11	0	10.48	3
P61981	14-3-3 protein gamma	28.3	0.56 ±0.25	0.62 ±0.07	0.59 ±0.08	0.59 ±0.02	8.36E-08	309.72	24.29	3
O43852	Calumenin	37.1	0.36 ±0.05	0.38 ±0.02	0.35 ±0.04	0.36 ±0.01	1.12E-11	63.32	42.22	11
P15531	Nucleoside diphosphate kinase A	17.1	0.63 ±0.28	0.61 ±0.13	0.52 ±0.07	0.59 ±0.05	5.69E-07	187.22	57.24	4
P16870	Carboxypeptidase E	53.1	0.40 ±0.10	0.49 ±0.02	0.39 ±0.02	0.43 ±0.04	9.52E-10	126.97	23.11	10
Q13185	Chromobox protein homolog 3	20.8	0.42 ±0.07	0.61 ±0.10	0.37 ±0.04	0.47 ±0.10	9.65E-08	74.45	12.02	2
P39687	Acidic leucine-rich nuclear phosphoprotein 32 family member A	28.6	0.41 ±0.06	0.66 ±0.07	0.49 ±0.16	0.52 ±0.10	1.37E-07	119.57	20.08	2
Q86VR7	V-set and immunoglobulin domain-containing protein 10-like	91.6	0.44 ±0.15	0.51 ±0.10	0.63 ±0.17	0.53 ±0.08	3.10E-07	0	1.04	1
Q8NC60	Nitric oxide-associated protein 1	78.4	0.52 ±0.17	0.58 ±0.03	0.60 ±0.04	0.57 ±0.03	2.39E-09	0	1	1
P62993	Growth factor receptor-bound protein 2	25.2	0.62 ±0.23	0.62 ±0.03	0.62 ±0.12	0.62 ±0.00	3.15E-08	39.98	30.88	7
P28838	Cytosol aminopeptidase	56.1	0.51 ±0.16	0.25 ±0.02	0.29 ±0.07	0.35 ±0.11	1.65E-05	0	1.35	1
O75695	Protein XRP2	39.6	0.55 ±0.18	0.67 ±0.02	0.49 ±0.09	0.57 ±0.07	3.64E-08	0	2.86	1
P50748	Kinetochore-associated protein 1	250.6	0.61 ±0.24	0.24 ±0.05	0.37 ±0.01	0.41 ±0.15	4.32E-05	0	0.41	1
Q8NI35	InaD-like protein	196.2	0.40 ±0.04	0.34 ±0.13	0.41 ±0.16	0.38 ±0.03	6.11E-07	0	0.33	1
P09382	Galectin-1	14.7	0.55 ±0.18	0.58 ±0.07	0.60 ±0.11	0.58 ±0.02	1.13E-08	0	5.93	1
P80303	Nucleobindin-2	50.2	0.34 ±0.04	0.30 ±0.02	0.26 ±0.04	0.30 ±0.03	3.58E-10	338.95	35	12
O00584	Ribonuclease T2	29.5	0.59 ±0.16	0.51 ±0.10	0.50 ±0.04	0.53 ±0.04	1.04E-08	161.14	40.63	9
Q9Y490	Talin-1	269.6	0.52 ±0.09	0.57 ±0.05	0.66 ±0.06	0.58 ±0.06	2.59E-10	87.26	3.66	7
Q04917	14-3-3 protein eta	28.2	0.53 ±0.05	0.43 ±0.04	0.48 ±0.08	0.48 ±0.04	1.46E-10	328.41	26.83	3
P05089	Arginase-1	34.7	0.63 ±0.16	0.47 ±0.10	0.43 ±0.06	0.51 ±0.09	1.51E-07	98.25	23.6	7
P52565	Rho GDP-dissociation inhibitor 1	23.2	0.63 ±0.14	0.58 ±0.14	0.58 ±0.07	0.60 ±0.02	5.16E-09	156.91	28.92	7
Q9BRZ2	E3 ubiquitin-protein ligase TRIM56	81.4	0.50 ±0.10	0.27 ±0.01	0.34 ±0.02	0.37 ±0.10	2.74E-07	50.08	0.93	1
P02808	Statherin	7.3	0.32 ±0.17	0.24 ±0.04	0.10 ±0.02	0.22 ±0.09	2.45E-04	86.57	48.39	1
P19021	Peptidyl-glycine alpha-amidating monooxygenase	108.3	0.63 ±0.10	0.64 ±0.02	0.62 ±0.06	0.63 ±0.01	4.72E-12	116.92	13.05	10
P01036	Cystatin-S	16.2	0.53 ±0.01	0.62 ±0.03	0.44 ±0.04	0.53 ±0.07	2.24E-10	15493.84	72.34	5
P23528	Cofilin-1	18.5	0.65 ±0.09	0.55 ±0.11	0.51 ±0.07	0.57 ±0.06	3.31E-09	167.41	72.29	11
P05154	Plasma serine protease inhibitor	45.6	0.59 ±0.02	0.54 ±0.10	0.66 ±0.02	0.60 ±0.05	4.98E-11	0	7.14	3
Q96FV2	Secernin-2	46.6	0.58 ±0.04	0.64 ±0.08	0.49 ±0.02	0.57 ±0.06	1.11E-10	43.94	3.76	1
Q05315	Galectin-10	16.4	0.65 ±0.08	0.57 ±0.13	0.40 ±0.05	0.54 ±0.10	6.40E-08	76.84	52.11	7
P23280	Carbonic anhydrase 6	35.3	0.42 ±0.15	0.61 ±0.04	0.32 ±0.01	0.45 ±0.12	6.93E-07	1812.13	52.27	12
P14550	Alcohol dehydrogenase [NADP(+)]	36.5	0.66 ±0.10	0.62 ±0.03	0.57 ±0.02	0.62 ±0.04	9.05E-12	91.82	26.15	8
Q9Y4Y9	U6 snRNA-associated Sm-like protein LSm5	9.9	0.58 ±0.05	0.46 ±0.06	0.54 ±0.20	0.53 ±0.05	5.03E-08	0	20.88	1
Q96DA0	Zymogen granule protein 16 homolog B	22.7	0.66 ±0.03	0.45 ±0.03	0.45 ±0.02	0.52 ±0.10	4.41E-09	1080	37.5	8
O75976	Carboxypeptidase D	152.8	0.64 ±0.09	0.59 ±0.07	0.57 ±0.10	0.60 ±0.03	2.33E-10	0	0.58	1
Q05707	Collagen alpha-1(XIV) chain	193.4	0.61 ±0.14	0.56 ±0.12	0.65 ±0.03	0.61 ±0.04	1.62E-09	0	0.5	1
P55957	BH3-interacting domain death agonist	22	0.65 ±0.06	0.36 ±0.07	0.40 ±0.04	0.47 ±0.13	1.31E-06	46.34	7.69	1
P30740	Leukocyte elastase inhibitor	42.7	0.65 ±0.08	0.47 ±0.03	0.62 ±0.09	0.58 ±0.08	1.64E-09	1489.02	55.67	23
Q8TAX7	Mucin-7	39.1	0.62 ±0.31	0.35 ±0.05	0.48 ±0.08	0.48 ±0.11	1.38E-05	204.21	11.41	5

For a candidate protein, the fold changes of >1.5 and <0.67 were considered as up-regulated and down-regulated protein, respectively.

Note: G1, Group 1 HCC patients; G2, Group 2 HCC patients; G3, Group 3 HCC patients; C, Healthy controls. G1/C, average fold change ratio between two technical replicates of Group 1 and healthy controls; G2/C, average fold change ratio between two technical replicates of Group 2 and healthy controls; G3/C, average fold change ratio between two technical replicates of Group 3 and healthy controls; G/C, average fold change ratio between three groups of HCC patients and healthy controls.

HCC: hepatocellular carcinoma.

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 1.

Top 10 enriched GO terms of differentially expressed proteins. P <0.05 was considered to be statistically significant.

Figure 2.

Statistically significantly enriched KEGG pathways of differentially expressed proteins. P <0.05 was considered statistically significant.

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 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.

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.

Supplemental Material

Supplementary_table_1 – Supplemental material for iTRAQ-based proteomics reveals SOD2 as a potential salivary biomarker in liver cancer

Supplemental material, Supplementary_table_1 for iTRAQ-based proteomics reveals SOD2 as a potential salivary biomarker in liver cancer by Feng Ding, Kehuan Sun, Ningning Sun, Qianqian Jiang, Meiqun Cao and Zhengzhi Wu in The International Journal of Biological Markers

Supplemental Material

supplementary_table_2 – Supplemental material for iTRAQ-based proteomics reveals SOD2 as a potential salivary biomarker in liver cancer

Supplemental material, supplementary_table_2 for iTRAQ-based proteomics reveals SOD2 as a potential salivary biomarker in liver cancer by Feng Ding, Kehuan Sun, Ningning Sun, Qianqian Jiang, Meiqun Cao and Zhengzhi Wu in The International Journal of Biological Markers

Footnotes

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

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