Proteome Differences between Hepatitis B Virus Genotype-B- and

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The proteome differences between hepatitis B virus genotype B and genotype C induced hepatocellular carcinoma revealed by iTRAQ based quantitative proteomics Dahai Wei, Yongyi Zeng, Xiaohua Xing, Hongzhi Liu, Minjie Lin, Xiaolong Liu, and Jingfeng Liu J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/acs.jproteome.5b00838 • Publication Date (Web): 28 Dec 2015 Downloaded from http://pubs.acs.org on December 29, 2015

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Journal of Proteome Research

Title page The proteome differences between hepatitis B virus genotype B and genotype C induced hepatocellular carcinoma revealed by iTRAQ based quantitative proteomics Dahai Weia,b,#, YongyiZenga,b,c,#, Xiaohua Xinga,b,, Hongzhi Liua,b, Minjie Lin a,b,Xiao Hand, Xiaolong Liua,b,* and Jingfeng Liua,b,c*

a

The United Innovation of Mengchao Hepatobiliary Technology Key Laboratory of

Fujian Province, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou 350025, People’s Republic of China b

The Liver Center of Fujian Province, Fujian Medical University, Fuzhou 350025,

People’s Republic of China c

Liver Disease Center, The First Affiliated Hospital of Fujian Medical University,

Fuzhou 350007, People’s Republic of China d

Biotechnology Research Institute, Chinese Academy of Agricultural Sciences,

Beijing 100081, People’s Republic of China #

These

authors

contributed

equally

to

this

work.

*Corresponding

Author

(Correspondence should be address to XL. Liu and JF. Liu), Postal Address: Xihong Road 312, Fuzhou 350025, Fujian Province, P.R. China. Tel.: +86591-83705927, E-mail addresses: [email protected], [email protected].

Running title: The proteome differences between different HCC subtypes

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Abstract Hepatitis B virus (HBV) is the main cause of hepatocellular carcinoma (HCC) in southeast Asia where HBV genotype B and genotype C are the most prevalent. Viral genotypes have been reported to significantly affect the clinical outcomes of HCC. However, the underlying molecular differences among different genotypes of HBV virus infected HCC is still not revealed. Here, we applied isobaric tags for relative and absolute

quantitation

(iTRAQ)

technology

integrated

with

liquid

chromatography-tandem mass spectrometry (LC-MS/MS) analysis to identify the proteome differences between the HBV genotype B and genotype C induced HCC. In brief, a total of 83 proteins in the surrounding noncancerous tissues and 136 proteins in the cancerous tissues between HBV genotype B and genotype C induced HCC were identified respectively. Therefore, these information revealed that there might be different molecular mechanisms of the tumorigenesis and development of HBV genotypes B and C-induced HCC. Furthermore, our results indicate that the two proteins ARFIP2 and ANXA1 might be potential biomarkers for distinguishing the HBV genotype B and genotype C induced HCC. Thus, the quantitative proteomic analysis revealed molecular differences between the HBV genotype B and genotype C induced HCC, and might provide fundamental information for further deep study.

Key words Quantitative proteomics; Hepatocellular carcinoma; HBV genotype B and genotype C; ARFIP2; ANXA1;

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Introduction Chronic infection with hepatitis B virus (HBV) is a major health problem leading to significant death rate worldwide, especially in developing countries including China.1-3 More than 400 million people are chronically infected with HBV worldwide, and up to one-third will progress to advanced fibrosis and cirrhosis, and even hepatocellular cancer (HCC).4-6 Epidemiological observations clearly indicated that several host factors are associated with increased risk of developing HCC in HBV infected population.7-11 These factors include age at infection, sex, alcohol abuse, human immunodeficiency virus (HIV) coinfection, nonalcoholic fatty liver disease (NAFLD), diabetes/obesity and various host genetic factors.12-14 In addition, several viral factors, including HBV genotype, viral DNA load and specific HBV viral mutations, have been closely associated with the pathogenesis of HCC as well.12-15 Among these, HBV viral genotype has profound impacts on the clinical outcome of HBV associated HCC.11 According to the homogeneity of HBV sequences, at least 8 genotypes have been classified with distinct geographical and ethnic distributions.8,11 Genotypes B and C are common in Asia and the Pacific region including China.1,3 As previously stated, HBV genotype C is associated with an increased risk of progressive liver disease and a significantly higher incidence of developing HCC than genotype B.4-9,12 It has been reported that individuals infected with HBV genotype C may have higher levels of HBV DNA and experience delayed hepatitis B core antigen (HbeAg) seroconversion than those infected with genotype B. This may reflect a relatively higher level of viraemia among patients infected with HBV genotype C.13-15 Furthermore, some mutations in HBV genome such as BCP double mutations, which independently may be associated with higher risk of HCC, appear to occur significantly more frequently in HBV genotype C than genotype B.11 Therefore, these studies suggest that HBV genotype C infection might be more aggressive liver disease compared with genotype B infection. Interestingly, several studies have shown that HBV genotype B was associated with 3 / 31



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the early onset of HCC, whereas genotype C was associated with HCC development the early onset of HCC, whereas genotype C was associated with HCC development at older ages.13,14 From a study completed in Taiwan, HBV genotype B was associated with HCC in patients younger than 35 years, and most cases were non-cirrhotic chronic hepatitis B while genotype C was more prevalent in patients with cirrhosis and people over 50 years of age with HCC.15-18 In addition, HBV genotype C induced HCC patients might have a poorer prognosis than those with genotype B infection. In Taiwan, among 193 resectable HBV-related HCC patients, HBV genotype B infected patients had a higher rate of solitary tumour (94% vs. 86%, p = 0.048) and more satellite nodules (22% vs. 12%, p = 0.05) than genotype C patients.6,11,19 Taken together, these data suggested that HCC patients with HBV genotype C infection could be prone to the development of multiple tumours in cirrhotic liver and have a poorer prognosis after curative resection of HCC compared with those with genotype B infection. On the basis of virus-host interactions, HBV are known to regulate host macromolecular synthesis by modifying host transcription and translation machineries and making the hosts serve the requirements of viruses during infection.20,21 Therefore, host proteins that are involved in evolution to HCC play essential roles in chronic hepatitis B progresses across a spectrum that includes asymptomatic carriers, chronic active hepatitis, liver cirrhosis and, finally, HCC.22,23 With the accumulated data, it was found that HBV can induce HCC directly by activating cellular oncogenes or indirectly through the process of the inflammation, regeneration and fibrosis associated with cirrhosis due to the HBV infection.24-26 These evidences infer that remarkable differences in molecular pathogenesis of HBV-related HCC among patients infected with different genotypes indeed exist. Comparative proteomic approaches coupling isobaric tags for relative and absolute quantitation (iTRAQ) are widely used to analyze host responses in animals, humans, and plants during virus infection. Meanwhile, the iTRAQ based quantitative study for 4 / 31


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the screening of diagnostic and prognostic protein biomarkers of HCC have also been reported.27-29 Therefore, the proteomics strategies provide an overall understanding of the host factors involved in virus infection and give insight into the alteration of signaling pathways, allowing us to further understand the viral pathogenesis.27-29 However, the application of iTRAQ labeling in studying the molecular differences between HBV genotype B and genotype C induced HCC has never been reported. Here, we quantitatively compared the proteomes between the tumor tissues and the adjacent noncancerous tissues using iTRAQ based quantitative proteomic approach (2D LC-MS/MS), and identified differentially expressed protein profiles between HBV genotype B and genotype C induced HCC patients. Our finding provided the first insight into the molecular differences between HBV genotypes B and C induced HCC, and might provide fundamental information for further deep study. Materials and methods 2.1 Sample collection HBV genotyping of all samples was performed by real-time fluorescence PCR using a commercial HBV genotyping kit (Shanghai Fosun Pharmaceutical Co., Ltd. Shanghai, China) as recommended by the manufacturer. Real-Time PCR was performed on an ABI 7500 Real-Time PCR System (Applied Biosystems, USA). Fluorescence emitted from reporter dyes FAM and Hex™ was monitored at regular intervals during the annealing extension phase respectively. Ct values obtained from the real-time PCR were adopted to identify HBV genotypes as described previously.8 Tissue samples, including the cancerous and paired surrounding noncancerous tissues, were collected at the time of surgery from 24 HBV-induced HCC patients at Mengchao Hepatobiliary Hospital of Fujian Medical University from August 2008 to January 2013. 12 patients were infected with HBV genotype B, and 12 patients were infected HBV genotype C. Patients with the following criteria were excluded: co-infection with other viruses, such as hepatitis C virus or HIV; decompensated liver

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disease; autoimmune or immunologically mediated disease and organ transplantation. Relevant clinical information of the patients is shown in Table 1. Fresh tissues were collected at the time of surgery from patients with HBV associated primary HCC; part of the collected tissues was immediately frozen in liquid nitrogen after washing with phosphate-buffered saline (PBS) within 20 minutes, and eventually preserved at -80 °C for long term storage; part of the tissues was formalin embedded and stored for immunohistochemistry. The histological diagnosis of the tissue samples

was

confirmed

by

experienced

pathologists.

Paired

surrounding

noncancerous tissues were isolated from at least 2 cm away from the tumor border and were confirmed to lack of tumor cells by histological examination under microscope. The project was approved for the using of human biopsy by the Institution Review Board of Mengchao Hepatobiliary Hospital of Fujian Medical University. The written consent was received from all participants in this study. 2.2 Protein preparation and iTRAQ labeling The tissues from patients were divided into 4 groups: cancerous tissues from HBV genotype B induced HCC patients (BC group, n=12); surrounding noncancerous tissues from HBV genotype B induced HCC patients (BN group, n=12); cancerous tissues from HBV genotype C induced HCC patients (CC group, n=12); surrounding noncancerous tissues from HBV genotype C induced HCC patients (CN group, n=12). For each group, every 6 individual samples with equal tissue weight were mixed, and then the proteins were extracted from the mixed samples. Therefore, we have 4 repeated protein extracts for each group to minimize the individual differences of the patients. Tissues were prepared as described previously.27, 29 In brief, all tissues were frozen at -80 °C until use; part of the stored tissues was carefully collected and resuspended in lysis buffer supplemented with 1×Protease Inhibitor Cocktail (Roche Ltd. Basel, Switzerland), then followed by tissue homogenization and sonication on ice. After centrifugation at 17,000 g for 10 min at 4 °C, the supernatant was collected and 6 / 31


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transferred to a fresh tube. The protein concentration of the supernatant was determined by BCA assay (TransGen Biotech, Beijing, China) following the manufacture’s protocol. Afterwards, 100 µg proteins per condition were transferred into a new tube, and the final volume was adjusted to 100 µL with 100 mM TEAB (triethylammonium bicarbonate). Then 5 µL DTT (200 mM) was added into the protein samples, and the samples were further incubated at 55 °C for another 1 hour; afterwards, 10 µL iodoacetamide (500 mM) was added to each sample to alkylate the proteins, then all of the samples were incubated for 30 min in dark at room temperature. For each sample, proteins were precipitated by ice-cold acetone, and then were re-dissolved in 100 µL TEAB (100 mM). Afterwards, the proteins were typically digested by sequence-grade modified trypsin (Promega, Madison, WI), and then the resultant peptides mixture was further labeled using chemicals from the iTRAQ reagent kit (AB SCIEX, USA). Peptides were labeled with the iTRAQ 8-plex reagent as follows: four groups (BC group, BN group, CC group and CN group) were labeled with 113, 114, 115 and 116 isobaric tag, respectively; and the peptides from the biological repetitions of the above 4 groups were labeled with 117, 118, 119 and 121, respectively. The iTRAQ 8-plex labeling was independently repeated 2 times, defining as A and B. Equal amount of labeled samples were desalted with the Sep-Pak VacC18 cartridges and then dried in a vacuum centrifuge for further usage. 2.3 High pH reverse phase separation and Low pH nano-LC-MS/MS analysis The mixed peptides were re-dissolved with buffer A (20 mM ammonium formate in water, pH 10.0), and then fractionated by high pH separation using a Acquity UPLC system (Waters Corporation, Milford, MA) connected to a reverse phase column (BEH C18, 1.7 µm, 2.1×50 mm, Waters Corporation, Milford, MA). High pH separation was eluted with linear gradient of 5-35% buffer B (20 mM ammonium formate in 90% ACN, pH 10.0) in 20 min at a constant flow rate of 600 µL/min. After the separation, the column was re-equilibrated at initial conditions for 15 min. Finally a total of 40 fractions 7 / 31



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were collected, and then two fractions with the same time interval were pooled together into 20 fractions.29 The fractions at the end were dried in a vacuum concentrator for further usage. Each fraction was analyzed using a Nano-Aquity UPLC system (Waters Corporation, Milford, MA) connected to a quadrupole-Orbitrap mass spectrometer (Q-Exactive) (Thermo

Fisher

Scientific,

Bremen,

Germany)

coupled

with

an

online

nano-electrospray ion source. The fractions were re-suspended with 32 µL solution C (0.1% formic acid in water), and separated by nano-LC and analyzed by on-line electrospray tandem mass spectrometry as follows. 8 µL peptide sample was loaded onto the trap column (Thermo Scientific Acclaim PepMap C18, 100 µm×2 cm) with a flow rate of 10 µL/min, and subsequently separated on the analytical column (Acclaim PepMap C18, 75 µm×50 cm) with a linear gradient, from 2% D to 40% D in 135min (solution D: 0.1% formic acid in ACN) and with a constant flow rate of 300 nL/min at 40 °C. The electrospray voltage of 2.2 kV at the inlet of the mass spectrometer was used. After the nano-LC separation, the column was re-equilibrated at initial conditions for 15 min. The Q-Exactive mass spectrometer was operated in the data-dependent mode to switch automatically between MS and MS/MS acquisition. Survey full-scan MS spectra (m/z 350-1200) was acquired with a mass resolution of 70 K, followed by 15 sequential high energy collisional dissociation (HCD) MS/MS scans with a resolution of 17.5 K. In all cases, one microscan was recorded using dynamic exclusion of 30 seconds. 2.4 Data Analysis Following the separation of peptides, we proceeded in the identification and quantification of detected proteins. MS/MS spectra were searched using Mascot (version 2.3.0, Matrix Science, London, UK) embedded into Trans-Proteomic Pipeline (TPP 4.6.2) against a human database provided by the Universal Protein Resource (http://www.uniprot.org/uniprot, released at 2014-04-10, with 20264 entries) and the 8 / 31


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decoy database. For protein identification, the searches were run using the following parameters: peptide mass tolerance = 10 ppm, MS/MS tolerance = 0.05 Da, tryptic cleavage specificity, fixed modification of carbamidomethyl (C), variable modifications of iTRAQ 8-plex (T) and oxidation (M).29 A decoy database search strategy was adopted to estimate the FDR (false discovery rate) for peptide identification. In our study, Scaffold (version Scaffold_4.3.4, Proteome Software Inc., Portland, OR, USA) was used to validate MS/MS based peptide and protein identifications, and quantitate the iTRAQ labeling peptides and proteins. All data were reported based on 99% confidence for protein and peptide identification as determined by FDR less than 1.0%. Protein probabilities were assigned by the Protein Prophet algorithm.30 Proteins that contain similar peptides and could not be distinguished on the basis of MS/MS analysis alone were grouped to satisfy the principles of parsimony.31 Proteins sharing significant peptide evidences were grouped into clusters. Channels were normalized in all samples on the basis of the algorithm described in i-Tracker.31 Acquired intensities in the experiment were globally normalized across all acquisition runs. Multiple isobaric-tagged samples were normalized by comparing the median protein ratios to the reference channel, and intensities for each peptide identification were normalized within the assigned protein. We used the variance function (var) in Microsoft Excel to calculate variability in reporter ion ratios between peptides. The proteins were quantified only in case that 8 reporter ion ratios were all identified, and then the variability was assessed. We do not quantify any proteins when there is less than 8 identified reporter ion ratios. The reference channels were normalized to produce a 1 : 1 fold change. All normalization calculations were performed using medians to multiplicatively normalize data.32-34 To identify the differentially expressed proteins, the relative protein expression values were compared between groups (BN vs. CN, BC vs. CC). The proteins were considered to be differentially expressed if the iTRAQ ratios were > 1.5 or < 0.67 in the surrounding noncancerous tissue and cancerous tissue groups of HCC patients 9 / 31



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induced with HBV genotype C as opposed those induced with genotype B respectively, with the p value < 0.05, which were statistically analyzed by the paired t-test. Here, we use volcano plot to display the differentially expressed proteins, where the x-axis is log2 based fold change and y-axis represent the negative log10 of the p-value calculated from two tailed t-test. The red points showed in the up-left panel are significant up-regulated proteins, while the green points are significant down-regulated proteins. 2.5 Functional Analysis of the differentially expressed proteins The Gene Ontology (GO) annotation and pathway enrichment analysis of all the identified proteins and differentially expressed proteins was implemented using the online tool DAVID (http://david.abcc.ncifcrf.gov/). GO annotation contains biological processes, involved cell components and molecular functions. The ingenuity Pathways Analysis (IPA) software (version 7.5) was used to analyze the biological functions and signaling pathways annotations of the differentially expressed proteins. The GO annotations, involved signaling pathways and networks were ranked in term of the enrichment of the differentially expressed proteins. 2.6 Quantitative real-time PCR (qRT-PCR) Total RNA was extracted from 48 fresh-frozen HCC tissues using Trizol reagent (Invitrogen, CA) and quantified using Nanodrop spectrophotometry (ThermoScientific, Wilmington, DE, USA). Reverse transcription was performed using a Goscript Reverse Transcription System Kit (Promega, Madison, WI, USA) according to the manufacturer’s instruction. qRT-PCR was run in technical duplicates for each reaction using 50 ng cDNA from at least triplicate of BN/CN and BC/CC. Relevant information on gene-specific primers used to detect the expression of these genes was designed using

Primer-Blast

Database

TGATGCCTACCGAACAGACTT

(primer (5’

sequence: to

3’);

ARFIP2-forward

primer,

ARFIP2-reverse

primer,

AGCTTCTCATACTTGTCCCGAT

(5’

to

3’);

ANXA1-forward

primer,

CTAAGCGAAACAATGCACAGC

(5’

to

3’);

ANXA1-reverse

primer,

10 / 31


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CCTCCTCAAGGTGACCTGTAA

(5’

to

ATAGCACAGCCTGGATAGCAACGTAC

(5’

3’); to

β-actin-forward 3’);

β-actinreverse

primer, primer,

CACCTTCTACAATGAGCTGCGTGTG (5’ to 3’). For qPCR analysis, aliquots of double-stranded cDNA were amplified using a Go-Taq qPCR Master mix (Promega, A6002). The cycling parameters were 45 cycles of 95°C for 15 seconds, 60°C for 15 seconds, and 72°C for 20 seconds. The relative gene expression was normalized to the geometric mean of the housekeeping gene β-actin and calculated according to the Livak method. 2.7 Immunoblotting analysis Expression of selected proteins in tissue samples was verified by Western blot (WB), as previously reported.27,29 Briefly, 30 µg proteins of each sample were separated by SDS-PAGE and transferred onto the NC membranes (Millipore, Bradford, MA). Afterwards, the membranes were blocked for 2 h in the PBST buffer containing 5% BSA, and probed with the ARFIP2 and ANXA1 primary antibody (1 : 2000 dilution, Santa Cruz Biotechnology) and β-actin antibody (1 : 5000 dilution, TransGen Biotech) at 4˚C overnight. After washed 3 times with PBST buffer for 10 min of each, the membranes were incubated with appropriate HRP-conjugated secondary antibodies (1 : 5000 dilution, TransGen Biotech) for 1 h at room temperature. Following washing again in the TBST buffer, the protein expression levels were detected by enhanced chemiluminescence and visualized by autoradiography. 2.8 Statistical analysis The data were expressed as mean ± SD, and analyzed with the Student’s t-test between two groups. p < 0.05 was considered as statistically significant. Statistical analysis was performed using the Statistical Program for Social Sciences (SPSS) software 17.0 (SPSS Inc., Chicago, IL, USA). Results 3.1 Clinical characteristics of the study population

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A total of 24 surrounding noncancerous and cancerous tissues from HCC patients induced with HBV genotype B and genotype C were used in this study (Table 1). All of the patients were selected with similar age distributions, sex distributions, tumor size distributions, differentiation degrees, degree of tumor boundaries definition, degree of tumor encapsulation integrity, and similar vascular tumor thrombosis. However, compared with genotype B, genotype C induced HCC patients had a significant higher HBV viral load level (4.89±0.36 vs. 6.01±0.34, p=0.034) and higher percentage of multiple tumor numbers (58% vs. 25%). 3.2 The quantitative proteomics of the HBV genotype B and genotype C induced HCC Experiments were carried out in two iTRAQ 8-plex labeling replicates with at least 1 peptide of >95% confidence and the MS results were validated by Scaffold_4.3.4 to identify a total of 4538 proteins. And 4223 overlapped proteins were shared by the two replicates, accounting for 93.06% of the total quantified proteins (Figure 1A). The numbers of overlapped proteins identified by MS were subsequently classified by bioinformatics analysis. Gene Ontology annotation was applied to classify identified proteins in terms of their subcellular localizations and each protein was assigned at least one term. More than 30% proteins were annotated as belonging to cytoplasmic-associated proteins, and the other two main categories of these proteins were the membrane (26%) and nucleus (13%) compartments (Figure 2B). Although the cytoplasmic and membrane-associated proteins were the most highly represented categories in our extracts, the nuclear and extracellular proteins were also readily identified; it indicates that our protein extraction procedure was not strongly biased to a few cell compartments. The distribution of MW, PI and hydrophobicity also clearly showed that the overall proteome datasets of the HBV genotype B and genotype C induced HCC had no strong bias. As summarized in Figure 1C, the top three molecular functions were translation, oxidation reduction and intracellular transport process. The distribution of MW, PI and hydrophobicity also clearly showed that the overall proteome datasets of the HBV genotype B and genotype C induced HCC had 12 / 31


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no strong bias (Figure S1). Full details of the dataset and other related information can be accessed through http://www.iprox.org/index with iProx ID of IPX00037601. 3.3 Identification of the differentially expressed proteins between HBV genotype B and genotype C induced HCC To identify the differentially expressed proteins, the relative protein expression values were compared between groups (BN vs. CN, BC vs. CC). In this study, 83 proteins that presented a mean expression fold change of ≥ ± 1.5 (log2 0.58) were classified as differentially expressed in the surrounding noncancerous tissues when HBV genotype B induced HCC was compared with genotype C induced HCC (BN vs. CN) (Figure 2A, Table S1). When iTRAQ ratios for these 83 proteins were plotted on a heatmap, 42 proteins were up-regulated while 41 proteins were down-regulated in the surrounding noncancerous samples of HBV genotype B induced HCC compared with genotype C induced HCC, and these two types of proteins formed clearly distinct clusters (Figure 2B). GO annotation analysis showed that these 83 proteins were the major participants in the immuno-regulation processes, which is suggesting that there are some differences in the immunopathogenesis among various HBV genotypes induced HCC (Figure 2C and 2D). These results indicate that different HBV genotypes may have different molecular mechanisms in carcinogenesis and development of HCC. Similarly, a comparison of the cancerous tissues between HBV genotype B and genotype C induced HCC (BC vs. CC) identified 136 proteins that presented a mean expression fold change of ≥ ± 1.5 (log2 0.58) (Figure 2E, Table S2). Figure 2F show the 136 differentially expressed proteins, and 82 were up regulated while 54 were down regulated. We further analyzed these protein involved biological processes by GO analysis; the results showed that these identified proteins were mainly involved in nucleosome assembly, protein-DNA complex assembly and carboxylic acid metabolic processes (Figure 2G and 2H). Comparison of results from Table S1 and Table S2, it clearly reveals that only 8 differentially expressed proteins (about 4% of identified proteins) overlapped between surrounding noncancerous 13 / 31



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tissues group (BN vs. CN) and cancerous tissues group (BC vs. CC), which is suggesting that they are possibly associated with different molecular mechanisms. Furthermore, the protein expression alternation between the cancerous tissues and noncancerous tissues in both HBV genotype B and C induced HCC was also analyzed with the above mentioned criteria (± log2 0.58). In genotype B induced HCC cancerous tissues, 164 dysregulated proteins (Table S3) including 86 up-regulated peotiens and 78 down-regulated proteins were identified by comparing with its corresponding noncancerous tissues (BC vs. BN, Figure S2A); in genotype C induced HCC cancerous tissues, 112 dysregulated proteins (Table S4) including 39 up-regulated proteins and 73 down regulated proteins were identified (CC vs. CN, Figure S2B). These up and down regulated proteins in both genotype B and genotype C induced HCC were formed distinct clusters (Figure S2, A and B), and most of these dysregulated proteins have been reported to be tightly associated with different aspects of cancer, ranging from tumorigenesis, tumor development to tumor cell migration and metastasis. The dysregulated proteins in HBV genotype B induced HCC were mainly involved in biological processes, such as response to toxin, RNA splicing, cellular macromolecular complex assembly, mRNA processing, and oxidation reduction (Figure S2C); while the dysregulated proteins in genotype C induced HCC were mainly involved in biological processes, such as organic acid catabolic process, carboxylic acid catabolic process, alcohol biosynthetic process, glucose metabolic process, and gluconeogenesis (Figure S2D). These results also clearly proved that there might be different molecular backgrounds and molecular machanisms between genotype B induced HCC and genotype C induced HCC. 3.4 IPA network analysis of the differentially expressed proteins To further analysis the roles of the protein expression alternations between the HBV genotype B and genotype C induced HCC, we performed Interactive Network analysis on the differentially expressed proteins using the Ingenuity Pathway Analysis (IPA) tool. As shown in Figure S2E, the dysregulated proteins in HBV genotype B induced 14 / 31


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HCC comparing with its corresponding noncancerous tissues (BC vs. BN), were mainly participated in the regulation of ERK 1/2 signaling (17 upregulated proteins and 13 down regulated proteins), NF-κB signaling (8 upregulated proteins and 16 down regulated proteins), and APP signaling (8 upregulated proteins and 10 down regulated proteins), which were the top 3 modulated signaling pathways in terms of the enrichment of dysregulated proteins. Interestingly, the dysregulated proteins in HBV genotype C induced HCC (CC vs. CN) were also mainly modulating the ERK 1/2 (8 up and 15 down regulated proteins), NF-κB (10 up and 14 down regulated proteins) and APP (6 up and 10 down regulated proteins) signaling pathways (Figure S2F). These results may reflect that there are common mechanisms of the tumorigenesis or tumor development of HBV genotype B and C induced HCC. However, specific signaling pathways of the dysregulated proteins are also can be identified; such as PI3K and AKT signaling pathways were only altered in genotype B induced HCC tumor tissues, while TGF and UBC signaling pathways were only altered in the genotype C induced HCC tumor tissues. Therefore, different HBV genotype induced HCC should have its own specific molecular characteristics and molecular mechanisms as well. Furthermore, the analyzed results also suggest that specific signaling pathways are indeed involved in surrounding noncancerous tissues group and cancerous tissues group of HBV genotype B and genotype C induced HCC patients, although there are common signaling pathways involved in as well. The IPA analysis demonstrated that the altered protein expressions in surrounding noncancerous tissues between HBV genotype B and genotype C induced HCC patients are mostly involved in NF-κB, PI3K/Akt, JAK-STAT and p38/MAPK signaling pathway, while the altered protein expressions in cancerous tissues between HBV genotype B and genotype C induced HCC are mostly involved in Wnt/β-catenin and NF-κB signaling pathway. Figure 3A is the proteins, which were differentially expressed in surrounding noncancerous tissue between HBV genotype B and genotype C induced HCC 15 / 31



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patients, involved key signaling pathway network. As shown in Figure 3A, the network is enriched with proteins significantly linked to immune cell trafficking, connectivity tumour establishment, progression and regression. This network also exhibited focus hubs containing NF-κB (9 proteins including 1 up-regulated proteins and 8 down-regulated protein), PI3K/Akt (3 proteins including 1 up-regulated proteins and 1 down-regulated protein), JAK-STAT (2 proteins including 1 up-regulated protein and 1 down-regulated protein) and p38/MAPK (2 proteins including 1 up-regulated protein and 1 down-regulated protein), all which regulate inflammation, and survival and proliferation of tumor cells. In cancerous tissues of HBV genotype B and genotype C induced HCC patients, 27 dysregulated proteins (including 13 up-regulated proteins and 14 down-regulated proteins) were related to lipid metabolism small molecule biochemistry and molecular transfort, including proteins involved in Wnt/β-catenin (10 proteins) and NF-κB (5 proteins) signaling pathway (Figure 3B) . Although, all of above mentioned signaling pathways are key players in hepatocarcinogenesis, only the NF-κB signaling pathway is extensively involved in both HCC surrounding noncancerous and cancerous tissues groups of HBV genotype B and genotype C induced HCC patients. It has been reported that the transcription factor NF-κB is a key orchestrator of innate immunity and inflammation, and recent evidence suggests that it represents a molecular link between inflammation and cancer.35 Here, our data supports the hypothesis that the effects of NF-κB on hepatocarcinogenesis strongly depend on the degree of NF-κB activation or inhibition. Therefore, it is not surprising that NF-κB signaling pathway is involved in both groups. Interestingly, the Wnt/β-catenin signaling pathway is only enriched in the HCC cancerous tissues group, but not enriched in the HCC surrounding noncancerous tissue group. The Wnt/β-catenin signaling pathway is known a key development pathway also involved in the formation of many types of cancer including HCC.36 Therefore, our results demonstrated that in the HBV genotype B and genotype C induced HCC, the Wnt/β-catenin signaling pathway may contribute to the regulation of 16 / 31


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HCC angiogenesis, infiltration and metastasis through regulating the differentially expressed proteins. 3.5 The verification of the differential expression ofARFIP2 and ANXA1 According to both hierarchal clustering analysis (Figure 2) and IPA network analysis (Figure 3), the protein Arfaptin-2 (ARFIP2) was only down regulated (3.2 fold) in the surrounding noncancerous tissues of HBV genotype B induced HCC, while Annexin A1 (ANXA1) was only up regulated (1.5 fold) in cancerous tissues of HBV genotype B induced HCC, when comparing with HBV genotype C induced HCC. ARFIP2, which was originally identified as a binding partner for the Arf1, Arf6, and Rac1 GTPases, was demonstrated to regulate NF-κB signaling by interacting with the functional IKK complex.37,38 ANXA1, first described as an anti-inflammatory protein acting mainly through phospholipase A2 inhibition, is an intracellular protein which is aberrantly expressed in many types of cancers.39 Therefore, they might be potential interesting biomarkers to distinguish the HBV genotype B and genotype C induced HCC. Hence, the expression changes of ARFIP2 and ANXA1 were further investigated by qRT-PCR and WB analysis using independent sets of 48 tissue samples (12 BN, 12 CN, 12 BC and 12 CC). Figure 4A shows the relative mRNA expression levels of ARFIP2 and ANXA1 as normalized to β-actin in HCC surrounding noncancerous (BN vs. CN) and cancerous tissue (BC vs. CC) samples. The mRNA expression levels of ARFIP2 were down-regulated 5 folds (n=12 patients, p < 0.01) while no significant changes of ANXA1 mRNA expression were observed, in surrounding noncancerous tissue of HBV genotype B induced HCC compared with genotype C induced HCC. In contrast, the mRNA expression levels of ANXA1 were significantly up-regulated 2.2 folds (n=12 patients, p < 0.05) while no significant changes of ARFIP2 mRMA expression were observed, in the cancerous tissue of HBV genotype B induced HCC compared with genotype C induced HCC. Afterwards, we further performed the Western Blot to validate the expression of 17 / 31



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ARFIP2 and ANXA1 at protein level. As shown in Figure 3B, the protein levels of ARFIP2 were only significantly down-regulated (n=7 patients, 2.6 fold, p < 0.01) in surrounding noncancerous tissue samples of HBV genotype B induced HCC patients compared with genotype C induced HCC patients, while the protein levels of ANXA1 were only remarkably up-regulated (n=7 patients, 2.1 fold, p < 0.05) in cancerous tissues of HBV genotype B induced HCC compared with genotype C induced HCC. These results are in well agreement with the Q-PCR analysis results and the LC-MS/MS results. Since the results from all analysis including proteomics data, mRNA data and WB data are quite comparable and well consistent with each other, the two proteins ARFIP2 and ANXA1 might be potential biomarkers for distinguishing the HBV genotype B and genotype C induced HCC, but the underlying molecular mechanisms needs to be further dissected. Discussions Hepatocarcinogenesis is, and will continue to be a major worldwide health problem.6 With chronic HBV genotypes B and C infections being responsible for a significant proportion of HCC cases, the results of previous studies confirm that HBV genotype C is associated with an increased risk of developing liver cirrhosis and HCC, as compared with genotype B.12,15,26 While much of the existing literatures have focused on noting the presence of disparities in HBV genotypes B and C induced HCC, little is known about the potential mechanisms and the difference of involved specific biological pathways within the context of different genotype background. The proteomic data presented in this study is the first report of the molecular differences at proteome level between HBV genotype B and genotype C induced HCC. These data not only provide confirmation of earlier studies, which HCC patients with genotype C infection might have a poorer prognosis than those with genotype B infection,19 but also provide new information regarding of the HBV genotype B and genotype C induced HCC, which will facilitate further in deepth investigation. There are significant and huge differences between the HBV genotype B and 18 / 31


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genotype C induced HCC at the proteome level through a comprehensive analysis of the differentially expressed proteins which are identified by iTRAQ based approach. Among 4538 identified proteins with FDR < 1%, 93.06 % of them were shared in all 2 technical repeats, which proved the stability of the workflow and the reliability of the research conclusion. The expression alterations of identified proteins in different HBV genotype infected patients were involved in different biological processes, including translation, oxidation reduction, intracellular transport, establishment of protein localization, protein transport and translational elongation, which is confirming that the pathogenesis of HBV genotypes B and C induced HCC is associated with different molecular mechanisms. This finding well strengthened our initial speculation that different HBV genotypes may have different hepatocarcinogenetic mechanisms. Individual differences among patients are extremely important for clinical proteomics, and it is the key obstacle for the clinical translation of proteomics. Here, we have pooled the tissue samples together (3 patients’ samples as one biological repeat) to minimize the individual differences; therefore, we have 4 biological repeats for each group (BC, BN, CC, CN). From our analysis, the common changed proteins, which were altered their expression in all 4 repetitions, were accounted for 42% in BC vs. BN, and 46% CC vs. CN of the proteins which were altered their expression in any of the repetitions. These results indicated that the donor variances in cancer vs. surrounding tissues across donors are not a big issue for our analysis after the sample pooling. However, it is also worth to investigate the proteomics alternation in the tumor tissue of each individual patient in future study, which might provide valuable data for the application of protemics in precised medicine. In this study, we successfully identified 83 differentially expressed proteins in the surrounding noncancerous tissues when HBV genotype B induced HCC was compared with genotype C induced HCC (BN vs. CN). Many of these identified differentially expressed proteins (BN vs. CN) were associated with the acute inflammatory response, acute phase response, defense response, response to 19 / 31



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wounding and sulfur metabolic process mainly which reflected the inflammatory disorders and abnormal functions of the innate immunity.40,41 For example, patients with genotype C have a higher risk of inflammatory disorders and a poorer clinical outcome compared to those with genotype B infection, and therefore individuals infected with HBV genotype C had the highest incidence of developing HCC than genotype B infection. Furthermore, this conclusion could be further supported by the obviously altered NF-κB signaling pathway in the HCC surrounding noncancerous tissues between HBV genotype B and genotype C infected patients, which is linking to balance

of

inflammatory

network,

release

of

cytokine/chemokine

in

the

microenvironment during hepatocarcinogenesis. As mentioned above, the 136 differentially expressed proteins in the surrounding noncancerous tissues between HBV genotype B and genotype C induced HCC groups mainly focused on energy production, lipid metabolism, which involved in several major oncogenic pathways that are deregulated in HCC such as Wnt/β-catenin signaling pathway. For example, ALDH3A1 is down-regulated 2.18 folds in cancerous tissues of HBV genotype B induced HCC compared with genotype C induced HCC; it has been reported that ALDH3A1 expression is strongly upregulated in a subset of HCC with CTNNB1 mutations then activating the Wnt/ß-catenin pathway.42 Other proteins such as EPCAM that is involved in regulating the functions of cell adhesion, migration, proliferation and differentiation was identified as up-regulated 2.56 folds in cancerous tissues of HBV genotype B induced HCC compared with genotype C induced HCC. Moreover, it has been confirmed that the silencing of EPCAM led to decreased expression of Wnt/β-catenin, and thus reduced proliferation and increased the apoptosis ratio in the cells.43 Up-regulation of Wnt/β-catenin protein has been reported to diminish membranous E-cadherin expression, and increase HCC recurrence after curative resection.16 This indicates that HBV genotype C is a strong risk factor for HCC recurrence after curative

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resection, possibly because genotype C has a higher level of Wnt/β-catenin signaling pathway activation than genotype B in HCC patients. In addition, we carefully analyzed the clinical features of both HCC types from our tissue bank containing 12 HCC patients with genotype B infection and genotype C infection (Table 1). In a comparison of clinical feature of genotype B infection and genotype C infection, our study showed that patients with genotype C infection had significant more multiple tumor numbers (58% vs. 25%) than those with genotype B infection. Similar results had been reported in study of 193 and 29 HCC patients with genotypes B and C infections.19,22 Therefore, our results, in agreement with these data, suggested that HCC patients with genotype C infection could be prone to the development of multiple tumors and had a higher tumor recurrence rate after curative resection compared with those infected with HBV genotype B. The expression of

ARFIP2 is

significantly down-regulated in surrounding

noncancerous tissue of HBV genotype B infected HCC patients compared with genotype C infected patients, while its expression in the HCC cancerous tissues (BC vs. CC) between genotype B and genotype C infected patients has not been significantly changed. Different studies indicate that ARFIP2 protein works in cooperation with cellular transcription factors and induces transformation causing tumor or cancer.38,45 In this study, up regulation of ARFIP2 expression may increase TNF-ɑ-stimulated transcriptional activation of NF-κB in the HCC surrounding noncancerous tissue of genotype C infected samples compared with genotype B infected samples. The activated NF-κB signal pathway may play a tumor-promoting role by protecting tumor cells from death or enhancing their proliferation, because NF-κB activation is required for the up regulation of many proteins such as ARFIP2. Thus, we discovered and validated that expression level of ARFIP2 in HCC surrounding noncancerous tissues was a positive hallmark for distinguishing the HBV genotype B and genotype C induced HCC. In addition, the expression of ANXA1 was significantly up-regulated in the HCC 21 / 31



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cancerous tissue of genotype B infected patients compared with genotype C infected patients, but kept unchanged in the HCC surrounding noncancerous tissue (BN vs. CN) between genotype B and genotype C induced HCC. ANXA1 was first described as an anti-inflammatory protein acting mainly through phospholipase A2 inhibition, but it also plays several roles in cell proliferation and differentiation, apoptosis, protein trafficking and membrane fusion.39,46 Consistent with the results of IPA network analysis suggesting that NF-κB signaling pathway involved inflammatory and immune disorders are one of the most important pathogenic factors of HCC, many proteins associated with those functions including ANXA1 were dysregulated in our data. The NF-κB signaling pathway may contribute to the regulation of HCC angiogenesis, infiltration and metastasis through regulating the expression of many proteins such as ANXA1. Consistently, our observations indicate that ANXA1 up regulation may decrease activation of NF-κB in the HCC cancerous tissue of genotype B samples compared with genotype C. However, the underlying mechanism by which ANXA1 inhibits NF-κB still remains unclear. Therefore, the expression level of ANXA1 in the cancerous tissue of HBV infected patients might be conferred a potential biomarker for distinguishing the HBV genotype B and genotype C induced HCC. Overall, we have applied the iTRAQ based quantitative proteomics approach to compare the protein expression profile alternations of HBV genotype B and genotype C induced HCC, and identified potential biomarkers and possible therapeutic targets for the HBV genotypes B and C-induced HCC. Conclusions Here, we have applied the iTRAQ based quantitative proteomics approach to study the overall protein profile alternations between HBV genotype B and genotype C induced HCC. As expected, our results clearly proved that different protein profile alternations and different signaling pathways were involved in the HBV genotype B and genotype C induced HCC patients. For a methodological verification, ARFIP2 and ANXA1 were investigated by qRT-PCR and Western-Blot using the same sample set. 22 / 31


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In addition, the results of the current study suggest a potential applicability of ARFIP2 and ANXA1 proteins as biomarkers for distinguishing the HBV genotype B and genotype C induced HCC.

Supporting Information Figure S1. The qualities of the proteome dataset. (A) Frequency distribution of the identified proteins with ≥ 1 unique peptides. (B) Molecular weight distribution of identified proteins proved that there is no bias in the protein extraction process. (C) Isoelectric point distribution of the identified proteins to show the unbias of the protein extraction. (D) Protein hydrophobicity distribution of the identified proteins. Figure S2. The Hierarchical clustering and involved biological processes analysis of differentially expressed proteins between cancerous and noncancerous tissues of HBV genotype B and C induced HCC. (A, B) Hierarchical clustering of the 164 dysregulated proteins (BC vs. BN), and 112 dysregulated proteins (CC vs. CN) with fold change ≥ ± 1.5 and p-values < 0.05 in cancerous tissues of genotype B (A) and genotype C (B) induced HCC compared with its surrounding noncancerous tissues. (C, D) Gene ontology (Go) analysis of the dysregulated proteins involved biological processes in cancerous tissue of HBV genotype B (C) and genotype C (D) induced HCC. Only terms with p values less than 0.001 are shown. (E, F) The dysregulated proteins in cancerous tissues involved key signaling pathways of HBV genotype B (E) and genotype C (F) induced HCC. The red labeling indicates the up-regulated proteins and green labeling indicates the down-regulated proteins. Table S1. Differentially expressed proteins of the surrounding noncancerous tissues between hepatitis B virus genotype B induced HCC and genotype C induced HCC. Table S2. Differentially expressed proteins of the cancerous tissues between hepatitis B virus genotype B induced HCC and genotype C induced HCC. Table S3. Differentially expressed proteins of cancerous tissues of HBV genotype B induced HCC compared with its corresponding noncancerous tissues. 23 / 31



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Table S4. Differentially expressed proteins of cancerous tissues of HBV genotype C induced HCC compared with its corresponding noncancerous tissues.

Author information #

These

authors

contributed

equally

to

this

work.

*Corresponding

Author

(correspondence should be address to XL. Liu and JF. Liu), Tel.: +86-591-83705927. E-mail addresses: [email protected], [email protected]. Present Addresses Xihong Road 312, Fuzhou 350025, Fujian Province, P.R. China. Notes The authors declare no competing financial interest. Acknowledgments This work is supported by the key clinical specialty discipline construction program of Fujian, P. R.C.; the National Natural Science Foundation of China (Grant No. 31201008); the specialized Science and Technology Key Project of Fujian Province (Grant No. 2013YZ0002-3); the Science and Technology Infrastructure Construction Program of Fujian Province (Grant No. 2014Y2005); the Scientific Foundation of Fuzhou City (Grant No. 2015-S-143-7); the Scientific Foundation of Fuzhou Health Department (Grant No. 2014-S-w19, and Grant No. 2013-S-wp1). Abbreviations HBV, hepatitis B virus; HCC, hepatocellular carcinoma; iTRAQ, isobaric tags for relative and absolute quantitation; LC-MS/MS, liquid chromatography-tandem mass spectrometry; HIV, human immunodeficiency virus; NAFLD, nonalcoholic fatty liver disease; HbeAg, hepatitis B core antigen; SCX, strong cation exchange; Q-Exactive, quadrupole-Orbitrap mass spectrometer; HCD, high energy collisional dissociation; BC, cancerous tissues from HBV genotype B induced HCC patients; BN, surrounding noncancerous tissues from HBV genotype B induced HCC patients; CC, cancerous tissues from HBV genotype C induced HCC patients; CN, surrounding noncancerous 24 / 31


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tissues from HBV genotype C induced HCC patients; qRT-PCR, Quantitative real-time PCR; WB, Western blot; NF-κB, nuclear factor kappa-light-chain-enhancer of activated B cells; ANXA1, annexin A1; ARFIP2, arfaptin-2; ALDH3A1, aldehyde dehydrogenase 3 familymember A1; CTNNB1, cadherin-associated protein beta 1; EPCAM, epithelial cell adhesion molecule References (1) Morise, Z.; Kawabe, N.; Tomishige, H.; Nagata, H.; Kawase, J.; Arakawa, S.; Yoshida, R.; Isetani, M. Recent advances in the surgical treatment of hepatocellular carcinoma. World J Hepatol. 2014, 20, 14381-14392. (2) Venook, A. P.; Papandreou, C.; Furuse, J.; de Guevara, L. L. The incidence and epidemiology of hepatocellular carcinoma: a global and regional perspective. Oncologist 2010, 15, 5-13. (3) Wang, D.; Cai, H.; Yu, W.; Yu, L. Identification of hepatitis B virus X gene variants between hepatocellular carcinoma tissues and pericarcinoma liver tissues in Eastern China. Int J Clin Exp Pathol. 2014, 7, 5988-5996. (4) Chu, C.; Lin, C.; Lin, S.; Lin, D.; Liaw, Y. F. Viral load, genotypes, and mutants in hepatitis B virus-related hepatocellular carcinoma: special emphasis on patients with early hepatocellular carcinoma. Dig Dis Sci. 2012, 57, 232-238. (5) Ringelhan, M.; O'Connor, T.; Protzer, U.; Heikenwalder, M. The direct and indirect roles of HBV in liver cancer: prospective markers for HCC screening and potential therapeutic targets. J Pathol. 2015, 235, 355-367 (6) Tanwar, S.; Dusheiko, G. Is there any value to hepatitis B virus genotype analysis? Curr Gastroenterol Rep. 2012,14, 37-46. (7) Chan, H.; Hui, A.; Wong, M. L.; Tse, A. M.; Hung, L.; Wong, V. W.; Sung, J. J. Genotype C hepatitis B virus infection is associated with an increased risk of hepatocellular carcinoma. Gut 2004, 53, 1494-1498.

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(36) Enzo, M. V.; Rastrelli, M.; Rossi, C. R.; Hladnik, U.; Segat, D. The Wnt/β-catenin pathway in human fibrotic-like diseases and its eligibility as a therapeutic target. Mol Cell Ther. 2015, 35, 709-713 (37) You, D.; Park C. R.; Furlong, M.; Koo, O.; Lee, C.; Ahn, C.; Seong, J. Y.; Hwang, J. I. Dimer of arfaptin 2 regulates NF-κB signaling by interacting with IKKβ/NEMO and inhibiting IKKβ kinase activity. Cell Signal. 2015, 27, 2173-2181. (38) Nakamura, K.; Man, Z.; Xie, Y.; Hanai, A.; Makyio, H.; Kawasaki, M.; Kato, R.; Shin, H. W.; Nakayama, K.; Wakatsuki, S. Structural basis for membrane binding specificity of the Bin/Amphiphysin/Rvs (BAR) domain of Arfaptin-2 determined by Arl1 GTPase. J Biol Chem. 2012, 287, 25478-25489. (39) oudhraa, Z.; Rondepierre, F.; Ouchchane, L.; Kintossou, R.; Trzeciakiewicz, A.; Franck, F.; Kanitakis, J.; Labeille, B.; Joubert-Zakeyh, J.; Bouchon, B.; Perrot, J. L.; Mansard, S.; Papon, J.; Dechelotte, P.; Chezal, J. M.; Miot-Noirault, E.; Bonnet, M.; D'Incan, M.; Degoul, F. Annexin A1 in primary tumors promotes melanoma dissemination. Clin Exp Metastasis 2014, 31, 749-760. (40) Chen, L.; Zhang, Q.; Chang, W.; Du, Y.; Zhang, H.; Cao, G. Viral and host inflammation-related factors that can predict the prognosis of hepatocellular carcinoma. Eur J Cancer 2012, 48, 1977-1987. (41) Nakagawa, H.; Maeda, S. Inflammation- and stress-related signaling pathways in hepatocarcinogenesis. World J Gastroenterol.2012,18, 4071-4081. (42) Han, G.; Tian, Y.; Duan, B.; Sheng, H.; Gao, H.; Huang, J. Association of nuclear annexin A1 with prognosis of patients with esophageal squamous cell carcinoma. Int J Clin Exp Pathol. 2014, 7, 751-759. (43) Lin, Y.; Lin, G.; Fang, W.; Zhu, H.; Chu, K. Increased expression of annexin A1 predicts poor prognosis in human hepatocellular carcinoma and enhances cellmalignant phenotype. Med Oncol. 2014, 31, 327-333. (44) Du, Q.; Geller, D. A. Cross-Regulation Between Wnt and NF-κB Signaling Pathways. For Immunopathol Dis Therap. 2010, 1, 155-181. 29 / 31



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(45) Peters, P. J.; Ning, K.; Palacios, F.; Boshans, R. L.; Kazantsev, A.; Thompson, L. M.; Woodman, B.; Bates, G. P.; D'Souza-Schorey, C. Arfaptin 2 regulates the aggregation of mutant huntingtin protein. Nat Cell Biol. 2002, 4, 240-245. (46) Anbalagan, D.; Yap, G.; Yuan, Y.; Pandey, V. K.; Lau, W. H.; Arora, S.; Bist, P.; Wong, J. S.; Sethi, G.; Nissom, P. M.; Lobie, P. E.; Lim, L. H. Annexin-A1 regulates microRNA-26b* and microRNA-562 to directly target NF-κB and angiogenesis in breast cancer cells. PLoS One 2014, 9, e0114507.

Table 1 Baseline characteristics of patients enrolled in this study Characters Gender (M/F) Age (years) AFP (ng/ml) Tumor size (cm) Tumor number Single Multiple HBV viral load (log10)

Genotype B (n=12) 12/0 50.92 ± 3.08 4035 ± 2000

Genotype C (n=12) 12/0 53.67 ± 1.87 1616 ± 923

t/χ2

P value

0.764 -1.098

0.453 0.284

8.42 ± 1.23

8.08 ± 1.29

-0.184

0.856

9 (75%) 3 (25%)

5 (42%) 7 (58%)

4.89 ± 0.36

6.01 ± 0.34

0.028 2.257

0.034

Figure Legends Figure 1. Features of the proteome dataset of HBV genotypes B and genotype C induced HCC tissues from iTRAQ shotgun analysis. (A) Venn diagrams showed the numbers of identified proteins and the overlaps of protein identification in 2 repeated experiments. (B) Subcellular localizations of the identified proteins. (C) GO analysis of the involved biological processes. The analysis was performed using DAVID and Gene Ontology annotations. Figure 2. The Hierarchical clustering and involved biological processes analysis of differentially expressed proteins between the HBV genotype B and genotype C induced HCC. (A) Volcano plot represented the protein abundance changes in surrounding noncancerous tissue between HBV genotype B induced the HCC and 30 / 31


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genotype C induced HCC (BN vs. CN). A total of 83 dysregulated proteins with fold change ≥ ± 1.5 and p-values < 0.05 were identified. (B) Hierarchical clustering of the 83 dysregulated proteins in surrounding noncancerous tissue between HBV genotype B induced HCC and genotype C induced HCC (BN vs. CN). (C, D) Gene ontology (Go) analysis of the upregulated proteins (C) and downregulated proteins (D) involved biological processes in surrounding noncancerous tissue between HBV genotype B induced HCC and genotype C induced HCC (BN vs. CN). Only terms with p values less than 0.001 are shown. (E) Volcano plot represented the protein abundance changes in cancerous tissue between HBV genotype B induced HCC and genotype C induced (BC vs. CC). A total of 136 dysregulated proteins with fold change ≥ ± 1.5 and p-values < 0.05 were identified. (F) Hierarchical clustering of the 136 dysregulated proteins in cancerous tissue between HBV genotype B induced HCC and genotype C induced HCC (BC vs. CC). (G, H) Gene ontology (Go) analysis of the upregulated proteins (G) and downregulated proteins (H) in cancerous tissue between HBV genotype B induced HCC and genotype C induced HCC (BC vs. CC). Only terms with p values less than 0.001 are shown. Figure 3. The key signaling pathways involved in the HBV genotype B and genotype C induced HCC. (A) The key signaling pathway of differentially expressed proteins in surrounding noncancerous tissues between HBV genotype B induced the HCC and genotype C induced HCC (BN vs. CN). (B) The key signaling pathway of differentially expressed proteins in cancerous tissues between HBV genotype B induced HCC and genotype C induced HCC (BC vs. CC). The red labeling indicates the up-regulated proteins and green labeling indicates the down-regulated proteins. Figure 4. Validation of the differentially expressed proteins between HBV genotype B and genotype C induced HCC patients. (A) Real-time PCR detection of the relative mRNA expression levels of ARFIP2 and ANXA1(p < 0.05, paired T-test). (B) The protein expression levels of ARFIP2 and ANXA1 when validated by Western blot.

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Figure 1. Features of the proteome dataset of HBV genotypes B and genotype C induced HCC tissues from iTRAQ shotgun analysis. (A) Venn diagrams showed the numbers of identified proteins and the overlaps of protein identification in 2 repeated experiments. (B) Subcellular localizations of the identified proteins. (C) GO analysis of the involved biological processes. The analysis was performed using DAVID and Gene Ontology annotations.


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Figure 2. The Hierarchical clustering and involved biological processes analysis of differentially expressed proteins between the HBV genotype B and genotype C induced HCC. (A) Volcano plot represented the protein abundance changes in surrounding noncancerous tissue between HBV genotype B induced the HCC and genotype C induced HCC (BN vs. CN). A total of 83 dysregulated proteins with fold change ≥1.5 and pvalues < 0.05 were identified. (B) Hierarchical clustering of the 83 dysregulated proteins in surrounding noncancerous tissue between HBV genotype B induced HCC and genotype C induced HCC (BN vs. CN). (C, D) Gene ontology (Go) analysis of the upregulated proteins (C) and downregulated proteins (D) involved biological processes in surrounding noncancerous tissue between HBV genotype B induced HCC and genotype C induced HCC (BN vs. CN). Only terms with p values less than 0.001 are shown. (E) Volcano plot represented the protein abundance changes in cancerous tissue between HBV genotype B induced HCC and genotype C induced (BC vs. CC). A total of 136 dysregulated proteins with fold change ≥1.5 and p-values < 0.05 were identified. (F) Hierarchical clustering of the 136 dysregulated proteins in cancerous tissue between HBV genotype B induced HCC and genotype C induced HCC (BC vs. CC). (G, H) Gene ontology (Go) analysis of the upregulated proteins (G) and downregulated proteins (H) in cancerous tissue between HBV genotype B induced HCC and genotype C induced HCC (BC vs. CC). Only terms with p values less than 0.001 are shown.



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Figure 3. The key signaling pathways involved in the HBV genotype B and genotype C induced HCC. (A) The key signaling pathway of differentially expressed proteins in surrounding noncancerous tissues between HBV genotype B induced the HCC and genotype C induced HCC (BN vs. CN). (B) The key signaling pathway of differentially expressed proteins in cancerous tissues between HBV genotype B induced HCC and genotype C induced HCC (BC vs. CC). The red labeling indicates the up-regulated proteins and green labeling indicates the down-regulated proteins.


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Figure 4. Validation of the differentially expressed proteins between HBV genotype B and genotype C induced HCC patients. (A) Real-time PCR detection of the relative mRNA expression levels of ARFIP2 and ANXA1(p < 0.05, paired T-test). (B) The protein expression levels of ARFIP2 and ANXA1 when validated by Western blot.



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For TOC only


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Proteome Differences between Hepatitis B Virus Genotype-B- and

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