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  • Research article
  • Open Access
  • Open Peer Review

Quantitative proteomic analysis for novel biomarkers of buccal squamous cell carcinoma arising in background of oral submucous fibrosis

  • 1,
  • 1,
  • 1Email author,
  • 1,
  • 1,
  • 1,
  • 1,
  • 1,
  • 2,
  • 2 and
  • 2
BMC Cancer201616:584

https://doi.org/10.1186/s12885-016-2650-1

  • Received: 31 August 2015
  • Accepted: 28 July 2016
  • Published:
Open Peer Review reports

Abstract

Background

In South and Southeast Asian, the majority of buccal squamous cell carcinoma (BSCC) can arise from oral submucous fibrosis (OSF). BSCCs develop in OSF that are often not completely resected, causing local relapse. The aim of our study was to find candidate protein biomarkers to detect OSF and predict prognosis in BSCCs by quantitative proteomics approaches.

Methods

We compared normal oral mucosa (NBM) and paired biopsies of BSCC and OSF by quantitative proteomics using isobaric tags for relative and absolute quantification (iTRAQ) to discover proteins with differential expression. Gene Ontology and KEGG networks were analyzed. The prognostic value of biomarkers was evaluated in 94 BSCCs accompanied with OSF. Significant associations were assessed by Kaplan-Meier survival and Cox-proportional hazards analysis.

Results

In total 30 proteins were identified with significantly different expression (false discovery rate < 0.05) among three tissues. Two consistently upregulated proteins, ANXA4 and FLNA, were validated. The disease-free survival was negatively associated with the expression of ANXA4 (hazard ratio, 3.4; P = 0.000), FLNA (hazard ratio, 2.1; P = 0.000) and their combination (hazard ratio, 8.8; P = 0.002) in BSCCs.

Conclusion

The present study indicates that iTRAQ quantitative proteomics analysis for tissues of BSCC and OSF is a reliable strategy. A significantly up-regulated ANXA4 and FLNA could be not only candidate biomarkers for BSCC prognosis but also potential targets for its therapy.

Keywords

  • Oral submucous fibrosis
  • Buccal squamous cell carcinoma
  • Quantitative proteomic analysis
  • Annexin A4
  • Filamin-A

Background

Oral submucous fibrosis (OSF) is a chronic and insidious lesion of oral mucosa which demonstrates particularly prevalent in some South and Southeast Asian countries [1, 2]. Its histopathologic feature is characterized by the inflammatory reaction of juxta-epithelial region followed by excessive collagen deposition of the lamina propria and the underlying submucosal layer, with associated epithelial atrophy [3]. A major clinical symptom of OSF patient is trismus, a limited ability to open the mouth, which eventually impairs eating, speaking and dental care [4, 5]. Various epidemiological studies have found that the chewing of areca-nut is the main etiological factor for OSF. [6].

OSF is associated with raised risk for the oral squamous cell carcinoma (OSCC), especially buccal SCC (BSCC), because buccal mucosa is the most common region that is stimulated by chewing areca nut [79]. The frequency of OSF canceration has been reported to range from 3 % to 6 % [10]. The oral precancerous condition defined by WHO is that a generalized pathological state of the oral mucosa associated with a significantly increased risk of cancer, which accords well with OSF characteristics [8]. Meanwhile, OSF is currently a public health problem in many countries, especially in some countries of southeastern Asian [11].

The molecular mechanisms of OSF progression and oncogenesis remain unclear and may be considered complex events in the deregulated expression of multiple molecules [12]. High-throughput proteomics can perform analysis to know expression profiles for thousands of proteins and characterize the biologic behaviors of cell simultaneously, which can contribute to better understand the changes of multiple proteins related to the disease progression and identify diagnostic and prognostic biomarkers. Different proteomics studies have been successfully engaged in the biomarker discovery of oral cancer. However, it is still hard to discover unique biomarkers to predict which oral mucosal disease will progress to OSCC [13, 14].

In the present study, we analyzed normal buccal mucosa (NBM), OSF and BSCC by isobaric tags for relative and absolute quantification (iTRAQ) system with two-dimensional liquid chromatography-tandem mass spectrometry (2DLC-MS/MS) to find the biomarkers contributed to the diagnosis and prognosis of OSF and BSCC. iTRAQ can label total peptide, preserve the information of post-translational modification and make quantitative analysis of 4 tissue samples simultaneously with same experimental conditions [14, 15]. Two novel protein biomarkers identified in our study may be clinically useful for BSCC detection arising from OSF, and evaluate their prognosis values.

Methods

Experimental design and analytical strategy

Briefly, there were three consecutive phases in this study: first a discovery screen of quantitative proteomics based on iTRAQ was carried out to identify candidate biomarkers with the consistently deregulated expressing levels from NBM to OSF to BSCC, second a protein-level evaluation of promising biomarkers by western blotting and immunohistochemistry, and third a validation of the candidate biomarkers in clinical samples by a retrospective study. We received ethical approval from the Xiangya Hospital Human Research Ethics Committee. All patients included for both the biomarker discovery screen and the retrospective clinical validation study were diagnosed with a primary BSCC arising from OSF. Enrolled cases were scheduled for surgical treatment with informed consent. Meanwhile, all cases had the habit of areca-quid chewing, and no previous local treatments for oral mucosa. All histological evaluations and grading were done according to the WHO standard criteria.

Patients and Tissue Samples

Paired biopsies of BSCC and OSF tissue were collected from BSCC patient accompanied with OSF lesion simultaneously. For every patient, BSCC sample was taken from the surgical cancer tissue, and matched OSF sample was from the contralateral buccal mucosa. In addition, unmatched NBM tissue was procured from healthy volunteer without the habit of betel-quid chewing. Each specimen was divided into three parts: one was for pathologic review to confirm the diagnosis, while the remaining two parts were immediately snap-frozen for quantitative proteomic and western blotting analysis respectively. If a paraffin specimen was confirmed by pathologists, it would be stored for immunohistochemical analysis. Eventually, 6 NBMs, 6 OSFs and 6 BSCCs were enrolled for proteomic and western blotting analysis. Clinical and histopathologic details of enrolled cases are listed in Table 1. Ninety-four formalin-fixed paraffin-embedded BSCC specimens, which were all removed from primary BSCC patients accompanied with OSF between November 2008 to August 2013, were drawn and reconfirmed for the retrospective clinical validation study. Age, TNM grade, UICC classification, OSF and BSCC histological grade, and survival time were recorded as the clinicopathological data (Additional file 1: Table S1). All enrolled cases had the habit of areca-quid chewing. All histological evaluations were done according to the WHO standard criteria.
Table 1

BSCC patients enrolled for iTRAQ quantitative proteomic analysis

Case

Age Range

BSCC Site

BSCC

BSCC

OSF

(y)

T-stage

Differentiation

Differentiation

1

30–35

Left

T3N1M0

Well

Moderate

2

35–40

Left

T1N0M0

Well

Moderate

3

45–50

Right

T3N0M0

Well

Early

4

25–30

Left

T2N0M0

Well

Moderate

5

55–60

Right

T2N1M0

Well

Advanced

6

45–50

Left

T3N2M0

Well

Advanced

Reagents and apparatus

iTRAQ™ Reagents Kit was bought from Applied Biosystems (San Jose, CA, USA). The acetonitrile, formic acid, acetone, trypsilin, and sodium citrate buffer were from Sigma-Aldrich (California, CA, USA). The Zorbax 300SB-C18 reversed-phase column (Microm, Auburn, CA, USA), the Polysulfoethyl column (The Nest Group, Southborough, MA, USA) and QSTAR XL System (Applied Biosystem, California, CA, USA) were for 2D LC-MS/MS. Sep-Pak Vac C18 cartridges was obtained from Millipore Corporation (Minneapolis, Minnesota, USA). The rabbit polyclonal antibodies were purchased from Abcam (London, UK).

Candidate biomarkers discovery by quantitative proteomic analysis

Protein preparation and iTRAQ labeling

The protein samples were quantitated by the Bradford method. The iTRAQ labeling was carried on according to the protocol. Briefly, 200 μg proteins were precipitated at-20 °C for 60 min. Then they were resuspended in 20 μl dissolution buffer. After reduction and alkylation, the peptides were labeled with iTRAQ regents for 60 min. Three iTRAQ regents (115, 116 and 117) were used to label the peptides of OSF, BSCC and NBM respectively. Sequentially, the samples were mixed together, and desalted by Sep-Pak Vac C18 cartridges.

2D LC-MS/MS analysis

The mixed labeling peptides were fractionated by strong cation exchange chromatography (SCX). The mixture was reconstituted with Buffer A (10 mM KH2PO4 in 25 % acetonitrile, PH 2.6). The mixed peptides were separated at a 500 μl/min with a gradient of 0-80 % Buffer B (Buffer B was Buffer A containing 350 mM KCl) in Buffer A for 1 h. The 215 nm and 280 nm absorbance was monitored and a total of 12 SCX fractions were got together. The fractions were dried and resuspended in 50 μl HPLC Buffer A (5 % acetonitrile, 0.1 % formic acid). Then they were loaded across the Zorbax 300 SB-C18 reversed-phase column and assessed on a QSTAR XL System with a 20 AD HPLC system. The elution flow rate was 0.3 μl/min with gradient 5 %–35 % HPLC Buffer B (98 % acetonitrile, 0.1 % formic acid) for 90 min. The scans were obtained with m/z ranges of 400–1800 for MS with up to three precursors selected from m/z 100–2000 for MS/MS.

Protein identification

The MS/MS data were searched from the International Swissprot using the Protein Pilot software 3.0 (Applied Biosystem, USA). The parameters were as follows: trypsilin as enzyme, methylmethanethiosulphonate of cysteines residues as modification. Then the Paragon Algorithm followed by the ProGroup Algorithm (Applied Biosystem, USA) were used to cancel redundant hits. Parent ion accuracy, fragment ion mass accuracy, tryptic cleavage specificity, and allowance for missed cleavages were provided by Protein Pilot. The benchmark for protein identification was unused Prot-Score >1.3 (95 %) as the threshold. The relative protein expression was based on the ratio of peptides ions (115:117 or 116:115). We used the fold change ratio ≤ 0.5 or ≥2 to designate differentially expressed proteins (P < 0.05).

Bioinformatic analysis

Pathway analysis was performed by the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Gene Ontology (GO) database was used to facilitate the biological interpretation of the identified protein in these studies. The differentially expressed proteins of GO were divided into 3 categories as follows: biological process (BP), molecular function (MF) and cellular component (CC).

Validation Studies

Western blotting

30 μg protein was firstly separated with 12 % SDS-PAGE, then transferred on the polyvinylidene fluoride (PVDF) membrane. After blocked, filter was incubated by the primary antibody. The secondary antibody (Santa Cruz Biotechnology, California, CA) was applied onto the filter at 1:2,000 dilutions. Samples were probed with antiβ-actin antibody (BD Biosciences, San Jose, CA) as an internal control. We used ECL system (Amersham, Buckinghamshire, UK) to visualize bands, and the Bandscan software (Glyko, Novato, CA) for the analysis of signal intensity.

Immunohistochemical evaluation

Briefly, serial 3 μm thick sections of tissue sample were mounted on silanized slides. After blocked by 3 % hydrogen peroxide, sections were incubated by primary antibodies, then by the biotinylated IgG (Santa Cruz Biotechnology, CA) for 30 min. Antigen–antibody complexes were dealed with diaminobentzidine (DAB). Then slides were counterstained by Mayer’s Hematoxylin. The immunoreactivity of candidate biomarkers were assessed by counting the number of positive cells. We considered that ≥10 % positive cells were graded as immunopositive. For every sample, the result of immunoreactive staining was evaluated by two observers blinded for the data.

Clinical and prognostic validation in a retrospective case study

Cohort for the retrospective study

Ninety–four primary BSCCs accompanied with OSFs were immunohistochemically stained for biomarker candidates.

Follow-up study

All patients undergoing surgery were followed up. The time to death or recurrence was recorded in detail periodically. Disease free survival time was recorded from the time of histological diagnosis to the time of the last follow-up. If a patient died or was found recurrent, survival time was censored at that time. Overall survival can not be regarded as a separate parameter, because among the patients lost to follow up, the death number could not be ascertained. Only disease free survival of the patients was recorded.

Statistical Analysis

Statistical analysis of western blotting data was dealed with Student’s t test. The relationship between the expression of proteins and clinicopathological parameters was evaluated by Chi-Square or Fisher’s exact test. Follow-up studies were evaluated by Kaplan–Meier and Cox’s Proportional Hazards test. P < 0.05 was regarded as significant. All statistical analysis was performed by SPSS 19.0.1 software.

Ethics statement

This study has been approved by the Ethics Board of Xiangya Hospital, which was also in accordance with the 1975 Helsinki Declaration. All patients had written the informed consent. Human samples were performed anonymously.

Results

Biomarker discovery screen

A total of 1998 proteins were identified from 14237 peptides among three tissues, based on the Unused ProtScore >1.3 (95 %) with at least one peptide above the 95 % confidence. 71.7 % proteins were with at least two peptides. And 56.2 % proteins were identified with three or more (Additional file 2: Table S2). Compared NBM 117 labeled, 90 proteins were up-regulated and 46 were down-regulated significantly in OSF 115 labeled. Meanwhile, between BSCC 116 labeled and OSF 115 labeled, 91 differential proteins were obtained, which contained 51 up-regulated and 40 down-regulated proteins in BSCC. Most importantly, in total of 30 proteins were identified with significantly different expression among three tissue types (Table 2). Among them, 2 candidate proteins (Annexin A4, ANXA4; Filamin-A, FLNA) were consistently upregulated, and one protein (Fibrinogen alpha chain precursor, FGA) was consistently down-regulated from NBM to OSF to BSCC.
Table 2

Total 30 differentially expressed proteins among three tissue types

Protein Symbol

Accession

Fold Change

BSCC/OSF (116:115)

OSF/NBM (115:117)

ANXA4 *

IPI00872780.1

4.8305881

2.051162243

MFAP4

IPI00793751.1

0.033419501

5.7543993

GATM

IPI00792191.1

3.250873089

0.343557954

CES1

IPI00607801.2

11.16862965

0.108642563

PSME1

IPI00479722.2

3.837071896

0.296483129

KRT19

IPI00479145.2

0.197696999

18.03017807

HIST1H4I

IPI00453473.6

0.04786301

2.582260132

VIM

IPI00418471.6

0.296483099

2.558585882

FLNA *

IPI00333541.6

3.83707315

2.128139019

KRT7

IPI00306959.10

0.246603906

4.285485268

COL1A2

IPI00304962.3

0.343558013

2.167704105

COL1A1

IPI00297646.4

0.27289781

2.884031534

GPD1

IPI00295777.6

0.2511885881

4.055085182

LTB4DH

IPI00292657.3

3.630779982

0.405508548

COL6A1

IPI00291136.4

0.366437614

2.83139205

MPO

IPI00236556.1

0.205116197

2.466039419

GOT1

IPI00219029.3

0.465586096

8.709635735

ADH4

IPI00218899.5

0.2679167986

5.649369717

GSTM1

IPI00218831.4

0.2051162004

9.549925804

CALM1

IPI00075248.11

0.35318321

13.55189419

CTSG

IPI00028064.1

0.251188606

5.105050087

HSP90B1

IPI00027230.3

2.83139205

0.310455948

PDIA3

IPI00025252.1

3.40408206

0.237684026

TF

IPI00022463.1

2.754229069

0.322106868

APCS

IPI00022391.1

0.110662401

4.830587864

FGA *

IPI00021885.1

0.387257606

0.432513833

P4HB

IPI00010796.1

3.28095293

0.280543357

PDIA4

IPI00009904.1

3.908409119

0.187068209

EPHX1

IPI00009896.1

3.80189395

0.157036275

HSPA5

IPI00003362.2

2.77971292

0.23120648

*The proteins written with bold words were the same differentially expressed proteins among three tissue types (from BSCC to OSF to NBM)

KEGG pathway analysis

Thirty–two signaling pathways among three tissue types were identified using KEGG database (Fig. 1a). The differentially expressed protein clusters could be assigned into numerous subcategories including the systemic lupus erythematosus, antigen processing and presentation, arginine and proline metabolism, focal adhesion, tyrosine metabolism, and so on. There were cross-talks among these pathways, as one protein might participate in several signaling pathways. Alcohol dehydrogenase 4 (ADH4) was involved in the most pathways (9 pathways) and Systemic lupus erythematosus pathway accounted for the most differentially expressed proteins (15 proteins) (Additional file 3: Table S3).
Fig. 1
Fig. 1

Bioinformatic analysis of differentially expressed proteins. a KEGG pathway analysis of the network relationships between proteins and related pathways. Red boxes indicate differentially expressed proteins, and yellow circles indicate the related pathways. The depth of red color shows the p-value which indicates the enrichment of proteins in the pathway. b pie graph of GO mapping for differential expression proteins. Cellular process GO term accounted for the top GO term, then the physiological process and cell part

GO analysis

These differentially expressed proteins were grouped into 72 (45.28 %) GO terms based on BP GO terms. The most enriched BP GO terms included cell redox homeostasis, interspecies interaction between organisms and oxidation reduction. There were 52 (32.7 %) GO terms identified by MF classification, and 35 (22.01 %) GO terms identified by CC classification. The top component for MF were protein binding, which consisted of 7 proteins. While the top component for CC were cytoplasm, which also consisted of 7 proteins (Additional file 4: Table S4).

On the other hand, as shown in Fig. 1b, cellular process (13.80 %) GO term which belongs to BP classification accounted for the top GO term, then the physiological process (13.24 %) and cell part (8.169 %).

Initial evaluation of candidate biomarkers

ANXA4 and FLNA were selected as the candidate biomarkers for BSCC arising OSF lesion because the two showed consistently upregulated from NBM to OSF to BSCC.

Western blotting

Staining intensities of ANXA4 and FLNA in BSCC were all significantly higher than OSF and NBM with a consecutively upregulated trend from NBM to OSF to BSCC (P = 0.002 and 0.001, respectively). Representative results were presented in Fig. 2a.
Fig. 2
Fig. 2

Initial validation of two candidate biomarkers. a Western blot of ANXA4 and FLNA in the samples of NBM and paired BSCC and OSF, as well as their corresponding quantifications. b representative immunohistochemical staining for ANXA4. Negative expression of ANXA4 in NBM, brown cytoplasm staining limited to the spinous epithelial layer of OSF, and intensively staining of the cytoplasm in BSCC cell nest. c representative immunohistochemical staining for FLNA. Weak expression in NBM samples, brown cytoplasm staining limited to the lower spinous epithelial layer and basal cell layer of OSF, and intensively staining of the cytoplasm in BSCC cell

Immunohistochemistry evaluation

In Fig. 2b, no detectable expression of ANXA4 was found in NBM, while OSF exhibited brown cytoplasm staining mainly limited to the spinous epithelial layer, and sometimes keratinocyte layer together. While in the BSCC, ANXA4 protein showed intensively staining of the cytoplasm in cancer cell. The positive expression of ANXA4 in BSCC was significantly higher than OSF (P = 0.008), while positive ANXA4 of OSF was significantly higher than NBM (P < 0.0001).

In Fig. 2c, very weak expression of FLNA was shown in NBM. However, OSF exhibited brown cytoplasm staining mainly limited to the lower spinous epithelial layer and basal cell layer. While in the BSCC, FLNA protein showed intensively staining of the cytoplasm in cancer cell. The positive expression of FLNA in BSCC was significantly higher than OSF (P = 0.004), while positive FLNA expression in OSF tissues was significantly higher than NBM tissues (P = 0.01).

Correlation of candidate biomarkers with clinicopathological parameters

As shown in Table 3, positive ANXA4 and FLNA were significantly related to T stage (P = 0.017 and P = 0.042, respectively). Positive ANXA4 showed a forward relationship with N stage (P = 0.001), while positive FLNA showed an inverse trend with N stage (P = 0.017). Meanwhile, there was a statistically significant relationship between positive ANXA4 and tumor stage (P = 0.004), while no association was found in other parameters.
Table 3

Correlation with clinicopathologic characteristics of the patients and immunostaining of ANXA4 and FLNA (n = 94)

 

Cases

ANXA4 (+)(%)

p value

FLNA (+)(%)

p value

Gender

     

  Male

83

59(71.1)

0.455

57(68.7)

0.425

  Female

11

9(81.8)

 

5(45.5)

 

Age

     

  ≤40 yr

42

32(76.2)

0.453

28(66.7)

0.896

  >40 yr

52

36(69.2)

 

34(65.4)

 

T stage

     

  T1

8

3(37.5)

0.017*

2(25)

0.042*

  T2

44

29(65.9)

 

28(63.6)

 

  T3

36

30(83.3)

 

27(75)

 

  T4

6

6(100)

 

5(83.3)

 

N Stage

     

  N0

48

27(56.3)

0.001*

38(79.2)

0.017*

  N1

43

38(88.4)

 

23(53.5)

 

  N2

3

3(100)

 

1(33.3)

 

UICC Stage

     

  I

7

2(28.6)

0.004*

4(57.1)

0.696

  II

31

19(61.3)

 

19(61.3)

 

  III

50

41(82)

 

34(68)

 

  IV

6

6(100)

 

5(83.3)

 

Diff.

     

  Well

80

59(73.8)

0.652

55(68.8)

0.180

  Moderate

10

6(60)

 

6(60)

 

  Poor

4

3(75)

 

1(25)

 

OSF Stage

     

  Early

27

22(81.5)

0.294

16(59.3)

0.278

  Moderated

46

30(65.2)

 

34(73.9)

 

  Advanced

21

16(76.2)

 

12(57.1)

 

* P < 0.05

Association of candidate biomarkers with patient prognosis

Seventy–three of 94 BSCC patients could be followed up. Patients were monitored for a period of median 22 months and a maximum of 58 months. Kaplan-Meier curves revealed that the disease-free survival was associated significantly with the negative expression of ANXA4 and FLNA (P = 0.000 and P = 0.000, respectively) in BSCCs in Fig. 3. Hazard ratios calculated by univariate Cox regression analysis, were 3.4 (95 % confidence interval, 2.2–7.5; P = 0.004) for ANXA4 and 2.1 (95 % confidence interval, 1.7–5.5; P = 0.0036) for FLNA. ANXA4 and FLNA immunostaining data were combined to form one BSCC group with positive ANXA4 and FLNA expression, and another group with negative ANXA4 and FLNA. This classification showed an association between patients with negative ANXA4 and FLNA and disease-free survival (P = 0.002) and has a superior prognostic power with a hazard ratio of 8.8 (95 % confidence interval, 3.0–32.6; P = 0.005).
Fig. 3
Fig. 3

Kaplan-Meier curves of local disease free survival of BSCC patients accompanied with OSF in relation to ANXA4 staining, FLNA staining, and the combination of both

Discussion

Some previous studies have identified a large number of differentially expressed biomarkers at the mRNA level between normal oral mucosa and OSCC or OSF tissues respectively [1619]. Meanwhile lots of protein biomarkers between normal oral mucosa and OSCC have also been found for long time. However, few studies focused the differentially expression of protein biomarkers between NBM and OSF. The present study is the first comprehensive research on proteins with differential expression among NBM, OSF and BSCC arising from OSF by using the iTRAQ shot-gun proteomic approach [20]. In this present study, we used whole tissue rather than microdissected tissue cells for our proteomics analysis. We think that whole tissue could have the ability of reflecting the tumor microenvironment accurately, which is believed to determine whether cancer can spread through epithelial-mesenchymal interactions (EMT) [21]. However, the main limitation for whole tissue in proteomics analysis is the cell heterogeneity of different tissues.

By iTRAQ proteomic approach, we identified in total 30 unique proteins from NBM to OSF to BSCC. Among the deregulated proteins, some were previously reported to be correlated with the pathogenesis of OSF, such as KRT19 [16], COL1A2 [22], GSTM1 [23], VIM; [24] some were not yet observed in OSF but within OSCC, for instance PSME1 [25], FLNA [26], GOT1 [27], GSTM1; [28] and some were not reported in any study on both OSF and OSCC. In addition, a large number of proteins identified in the previous reports were not found in our present study. The discordance between them may be explained partially by the limited dynamic range of iTRAQ [15]. Moreover, the difference of races and region distributions, the different processed methods of areca nut, as well as the different procedure of tissue collection and management may contribute to the distinction among various laboratories.

The location, function and regulation of the differentially expressed proteins can be better and easier to understand by bioinformatics analysis. The results of bioinformatic analysis showed that most consistently expressed proteins were randomly regulated proteins during OSF pathogenesis and carcinogenesis, because most of them were found in the discrete interaction networks. The top 5 GO components showed that the differentially expressed proteins in the present study were located mainly in cytoplasm with the protein binding function, which contained cell redox homeostasis, interaction between organisms, oxidation reduction and tissue regeneration. The top regulation network in the study, systemic lupus erythematosus pathway, indicated that immunological reaction might be the most important factor during the pathogenesis and carcinogenesis of OSF lesion, which is in agreement with the conclusions of our previous study and other research groups [16, 2931].

Notable proteins in our present study were three consistently deregulated proteins from NBM to OSF to OSCC, which were related to the mechanisms of the progression of OSCC arising from OSF. Two consistently upregulated proteins, ANXA4 and FLNA, were selected as the candidate biomarkers because we considered that the progress of OSF pathogenesis and carcinogenesis could be blocked effectively through interfering their upregulated expression. They would be promising targets for molecular therapy of OSF and OSCC.

The annexins, a multigene family of calcium-dependent phospholipid-binding proteins, have some special functions include the aggregation of vesicles and regulation of ion channels as well as roles in the regulation of cell cycle, cell signal and cell differentiation [32]. Meanwhile, annexins have been found in the processes of several disease, involving in inflammation and several neoplasia [33]. Of all annexins, ANXA4 was related to the loss of cell adhesion, and play important roles in apoptosis, carcinogenesis, chemoresistance, migration and invasion of cancer cells [34]. It binds phospholipids through the Ca-dependent manner and is located in the nucleus, cytoplasm, or membrane of cell [35]. ANXA4 was overexpressed in various primary clinical epithelial tumors, such as renal cancer [34], ovary cancer [35], gastric cancer [36], colorectal cancer [37], breast cancer [38], laryngeal carcinoma [38], pancreatic cancer [38, 39]. Its overexpression could enhanced significantly with the tumor stage and poorer prognosis [39], and be related to promote cell migration in a model tumor system [37]. These results are correlates with our observation in the present study that increased ANXA4 expression is associated with BSCC stage and poor prognosis. ANXA4 can form protein kinase C complexes. Moreover, it is found that at least 10 isoforms of protein kinase C have roles in the progression of cancers, including OSCC [40]. It could be found association with protein kinase C that ANXA4 has a vital effect on the BSCC pathogenesis. All these findings indicate that ANXA4 might have a vital role in the BSCC progression and migration. Meanwhile, ANXA4 expression was first identified in OSF tissues, which further proved the potential carcinogenic capacity of OSF.

FLNA is a type of actin filament cross-linking protein that participates in cytoskeletal rearrangement [41]. By its scaffolding function, FLNA can interact with more than 90 functionally diverse binding partners to regulate cellular functions and processes [42, 43]. The FLNA-deficient cells can not polarize and move because of their unstable surfaces which can continuously expand and contract circumferential blebs [44]. The orthogonal networks of FLNA have the active and reversible organizational properties, which can protect cell from various shear stresses [45]. In the present study, we firstly found that FLNA was positively expressed in OSF. Obviously, for oral mucosa cells in OSF patients, persistently mechanical shear stress caused by areca-nut chewing could be the key reason for the upregulated FLNA as a protective reaction of oral mucosa. Mis-regulation of FLNA plays a critical role in DNA double strand breaks response for the initiation of tumorigenesis [46]. Meanwhile, because of its ability to control cell mobility, cell-ECM interactions, cell signaling, and DNA damage response, FLNA could be regarded as a novel biomarker for the diagnosis and outcome prediction of cancer. Meanwhile, it has been reported that there was the correlation of increased FLNA expression in different stages of various cancer types and patient outcomes, such as colorectal cancer [47], pancreatic cancer [48], gliomas [49], prostate cancer [50] and salivary gland adenoid cystic carcinoma [51]. In the present study, we employed quantitative proteomic analysis to assess the FLNA expression and localization. Our data also illustrated that the expression of FLNA was increased in BSCC, and a poor survival index for patients with BSCC have high FLNA levels. So it is conceivable that the FLNA level in BSCC can be developed as a promising biomarker for the outcome prediction of BSCC.

Conclusion

Taken together, our proteome analysis has revealed a number of potential biomarkers among NBM, OSF and BSCC. Meanwhile, of these, ANXA4 and FLNA seem to have large prognostic value for patient survival, which may represent OSF and BSCC biomarkers and potential targets for therapeutical intervention. To our knowledge, although ANXA4 and FLNA has been reported on the carcinogenic roles of some tumors, no studies has been published on their expression in BSCC arising from OSF. However, more large-scale, prospective multicenter trials should be carried out to further elucidate their value in the clinic, and the roles of two biomarkers in BSCC development and invasion are in need of further study.

Abbreviations

2-DE, two-dimensional gel electrophoresis; 2DLC-MS/MS, two-dimensional liquid chromatography-tandem mass spectrometry; ADH4, Alcohol dehydrogenase 4; ANXA4, Annexin A4; BP, biological process; BSCC, buccal squamous cell carcinoma; CC, cellular component; DAB, diaminobentzidine; EMT, epithelial-mesenchymal interactions; FGA, Fibrinogen alpha chain precursor; FLNA, Filamin-A; GO, Gene Ontology; ICAT, isotop-encoded affinity tags; iTRAQ, isobaric tags for relative and absolute quantification; KEGG, Kyoto Encyclopedia of Genes and Genomes; MF, molecular function; NBM, normal oral mucosa; OSCC, oral squamous cell carcinoma; OSF, oral submucous fibrosis; PVDF, polyvinylidene fluoride; SCX, strong cation exchange chromatography; SILAC, stable isotope labeling by amino acids in cell culture

Declarations

Acknowledgements

This research was supported by the Department of Oral and Maxillofacial Surgery at Xiangya Hospital, Central South University, China.

Funding

This work was supported by the National Natural Sciences Foundation of China (grant no. 81000445).

Availability of data and materials

The datasets supporting the conclusions of this article are available in the FigShare repository [unique persistent identifier and hyperlink to datasets in https://figshare.com/s/cc1f524ba26e87e2441b], and the DOI number is 10.6084/m9.figshare.3405691.

Authors’ contributions

NL conceived of the study, and participated in its design and coordination and helped to draft the manuscript. WL carried out the proteomics studies and bioinformatic analysis. LZ and FW carried out the immunoassays and immunohistochemical evaluation. CJ, FG, XQ, SZ and CF participated in revising MS critically for important intellectual content. TS and CX participated in the design of the study and performed the statistical analysis. All authors read and approved the final manuscript.

Competing interests

The authors declare that they have no competing interests.

Consent for publication

Not applicable.

Ethics approval and consent to participate

This study has the approval of the Ethics Board of Xiangya Hospital and is also in accordance with the Helsinki Declaration of 1975. Written informed consent was obtained from every patient. All human tissues were processed anonymously.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Department of Oral and Maxillofacial Surgery, Xiangya Hospital, Central South University, No. 88, Xiangya Road, Changsha, China
(2)
Department of Oral Medicine, Xiangya Hospital, Central South University, No. 88, Xiangya Road, Changsha, China

References

  1. Ko YC, Huang YL, Lee CH, Chen MJ, Lin LM, Tsai CC. Betel quid chewing, cigarette smoking, and alcohol consumption related to oral cancer in Taiwan. J Oral Pathol Med. 1995;24:450–3.View ArticlePubMedGoogle Scholar
  2. Tilakaratne WM, Klinikowski MF, Saku T, Peters TJ, Warnakulasuriya S. Oral submucous fibrosis: review on etiology and pathogenesis. Oral Oncol. 2006;42:561–8.View ArticlePubMedGoogle Scholar
  3. Isaac U, Issac JS, Ahmed KN. Histopathologic features of oral submucous fibrosis: a study of 35 biopsy specimens. Oral Surg Oral Med Oral Pathol Oral Radiol Endod. 2008;106:556–60.View ArticlePubMedGoogle Scholar
  4. Rajalalitha P, Vali S. Molecular pathogenesis of oral submucous fibrosis–a collagen metabolic disorder. J Oral Pathol Med. 2005;34:321–8.View ArticlePubMedGoogle Scholar
  5. Pindborg JJ, Sirsat SM. Oral submucous fibrosis. Oral Surg Oral Med Oral Pathol. 1966;22:764–79.View ArticlePubMedGoogle Scholar
  6. Murti PR, Bhonsie RB, Pindborg JJ, Daftary DK, Gupta PC, Mehta FS. Malignant transformation rate in oral submucous fibrosis over a 17-year period. Community Dent Oral Epidemiol. 1985;13:340–1.View ArticlePubMedGoogle Scholar
  7. Auluck A, Rosin MP, Zhang L, Sumanth KN. Oral submucous fibrosis, a clinically benign but potentially malignant disease: report of 3 cases and review of the literature. J Can Dent Assoc. 2008;74:735–40.PubMedGoogle Scholar
  8. World Health Organization. Guide to epidemiology and diagnosis of oral mucosal diseases and conditions. Community Dent Oral Epidemiol. 1980;8:1–26.View ArticleGoogle Scholar
  9. Scully C, Bagan J. Oral squamous cell carcinoma: an overview. Oral Oncol. 2009;45:301–8.View ArticlePubMedGoogle Scholar
  10. Trivedy CR, Craig G, Warnakulasuriya S. The oral health consequences of chewing areca nut. Addict Biol. 2002;l7:115–25.View ArticleGoogle Scholar
  11. Jacob BJ, Straif K, Thomas G, Ramdas K, Mathew B, Zhang ZF. Betel quid without tobacco as a risk factor for oral precancers. Oral Oncol. 2004;40:697–04.View ArticlePubMedGoogle Scholar
  12. Lin SC, Chen YJ, Kao SY, Hsu MT, Lin CH, Yang SC, et al. Chromosomal alterations in betel-associated oral squamous cell carcinoma and their relation to clinical parameters. Oral Oncol. 2002;38:266–73.View ArticlePubMedGoogle Scholar
  13. Melle C, Ernst G, Winkler R, Schimmel B, Klussmann JP, Wittekindt C. Proteomic analysis of human papillomavirus-related oral squamous cell carcinoma: Identification of thioredoxin and epidermal-fatty acid binding protein as upregulated protein markers in microdissected tumor tissue. Proteomics. 2009;9:2193–201.View ArticlePubMedGoogle Scholar
  14. Ralhan R, DeSouza LV, Matta A, Tripathi SC, Ghanny S, DattaGupta S. iTRAQ Multidimensional Liquid Chromatography and Tandem Mass Spectrometry-Based Identification of Potential Biomarkers of Oral Epithelial Dysplasia and Novel Networks between Inflammation and Premalignancy. J Proteome Res. 2009;8:300–9.View ArticlePubMedGoogle Scholar
  15. Wulfkuhle JD, Liotta LA, Petricoin EF. Proteomic applications for the early detection of cancer. Nat Rev Cancer. 2003;3:267–75.View ArticlePubMedGoogle Scholar
  16. Ning L, Xinchun J, Yanjia H, Chunjiao X, Yaozhi G, Xiaohuan Z. Discovery of novel biomarkers in oral submucous fibrosis by microarray analysis. Cancer Epidemiol Biomarkers Prev. 2008;17:2249–59.View ArticleGoogle Scholar
  17. Kao WB, Shieh YD, Hsia YJ, Shieh TY. The micro-array analysis of genetic change and proteins identification of oral submucous fibrosis. Int J Oral Maxillofac Surg. 2005;34:139.View ArticleGoogle Scholar
  18. Onken MD, Winkler AE, Kanchi KL, Chalivendra V, Law JH, Rickert CG, et al. A surprising cross-species conservation in the genomic landscape of mouse and human oral cancer identifies a transcriptional signature predicting metastatic disease. Clin Cancer Res. 2014;20:2873–84.View ArticlePubMedPubMed CentralGoogle Scholar
  19. Vincent VK, Anwar A, Karen LP, Cheong SC, Yang YH, Pradeep PJ, et al. Genome wide analysis of chromosomal alterations in oral squamous cell carcinomas revealed over expression of MGAM and ADAM9. PLoS ONE. 2013;8, e54705.View ArticleGoogle Scholar
  20. Wu WW, Wang G, Baek SJ, Shen RF. Comparative Study of Three Proteomic Quantitative Methods, DIGE, cICAT, and iTRAQ, Using 2D Gelor LC-MALDI TOF/TOF. J Proteome Res. 2006;5:651–8.View ArticlePubMedGoogle Scholar
  21. Sausville EA. The challenge of pathway and environment-mediated drug resistance. Cancer Metastasis Rev. 2001;20:117–22.View ArticlePubMedGoogle Scholar
  22. Chiu CJ, Chang ML, Chiang CP, Hahn LJ, Hsieh LL, Chen CJ. Interaction of collagen-related genes and susceptibility to betel quid-induced oral submucous fibrosis. Cancer Epidemiol Biomarkers Prev. 2002;11:646–53.PubMedGoogle Scholar
  23. Deepa A, Shalini G, Deepti A, Prakash G, Mohit A. Role of GSTM1 and GSTT1 Polymorphism: Susceptibility to Oral Submucous Fibrosis in the North Indian Population. Oncology. 2010;79:181–6.View ArticleGoogle Scholar
  24. Meghanand TN, Anjali S, Rajiv SD. Immunohistochemical Analysis of Vimentin in Oral Submucous Fibrosis. J Cancer Epidemiol. 2013;2013:549041.Google Scholar
  25. Saiful AK, Afiza S, Rosnah BZ, Ching C, Mariati AR. Comparative proteomics analysis of oral cancer cell lines: identification of cancer associated proteins. Proteome Sci. 2014;12:3.View ArticleGoogle Scholar
  26. Daniela CG, Mariana RZ, Rebeca K, Sami Y, Romênia RD, Annelize ZA, et al. Integrated Proteomics Identified Up-Regulated Focal Adhesion-Mediated Proteins in Human Squamous Cell Carcinoma in an Orthotopic Murine Model. PLoS ONE. 2014;9, e98208.View ArticleGoogle Scholar
  27. Fernando MS, Rebeca K, Sami Y, Daniela CG, Lucas M, Michelle A, et al. ADAM17 mediates OSCC development in an orthotopic murine model. Mol Cancer. 2014;13:24.View ArticleGoogle Scholar
  28. Rosy M, Sankar KG, Javed HC, Anil S, Kavita S, Marine H, et al. Mitochondrial DNA Copy Number and Risk of Oral Cancer: A Report from Northeast India. PLoS ONE. 2013;8, e57771.View ArticleGoogle Scholar
  29. Li N, Hu Q, Jiang C, Guo F, Munnee K, Jian X, et al. Cys-X-Cys ligand 9 might be an immunological factor in the pathogenesis of oral submucous fibrosis and its concomitant oral lichenoid lesion. Clin Oral Investig. 2013;17:1251–8.View ArticlePubMedGoogle Scholar
  30. Rajendran R, Deepthi K, Nooh N, Anil S. α4β1 integrin-dependent cell sorting dictates T-cell recruitment in oral submucous fibrosis. J Oral Maxillofac Pathol. 2011;15:272–7.View ArticlePubMedPubMed CentralGoogle Scholar
  31. Jeng JH, Wang YJ, Chiang BL, Lee PH, Chan CP, Ho YS, et al. Roles of keratinocyte inflammation in oral cancer: regulating the prostaglandin E2, interleukin-6 and TNF-alpha production of oral epithelial cells by areca nut extract and arecoline. Carcinogenesis. 2003;24:1301–15.View ArticlePubMedGoogle Scholar
  32. Gerke V, Creutz CE, Moss SE. Annexins: linking Ca2+ signalling to membrane dynamics. Nat Rev Mol Cell Biol. 2005;6:449–61.View ArticlePubMedGoogle Scholar
  33. Hayes MJ, Moss SE. Annexins and disease. Biochem Biophys Res Commun. 2004;322:1166–70.View ArticlePubMedGoogle Scholar
  34. Zimmermann U, Balabanov S, Giebel J, Teller S, Junker H, Schmoll D, et al. Increased expression and altered location of annexin IV in renal clear cell carcinoma: a possible role in tumour dissemination. Cancer Lett. 2004;209:111–8.View ArticlePubMedGoogle Scholar
  35. Kim A, Serada S, Enomoto T, Naka T. Targeting annexin A4 to counteract chemoresistance in clear cell carcinoma of the ovary. Expert Opin Ther Targets. 2010;14:963–71.View ArticlePubMedGoogle Scholar
  36. Lin LL, Chen CN, Lin WC, Lee PH, Chang KJ, Lai YP, et al. Annexin A4: a novel molecular marker for gastric cancer with Helicobacter pylori infection using proteomics approach. Proteomics Clin Appl. 2008;2:619–34.View ArticlePubMedGoogle Scholar
  37. Duncan R, Carpenter B, Main LC, Telfer C, Murray GI. Characterisation and protein expression profiling of annexins in colorectal cancer. Br J Cancer. 2008;98:426–33.View ArticlePubMedGoogle Scholar
  38. Deng S, Wang J, Hou L, Li J, Chen G, Jing B, et al. Annexin A1, A2, A4 and A5 play important roles in breast cancer, pancreatic cancer and laryngeal carcinoma, alone and/or synergistically. Oncol Lett. 2013;5:107–12.PubMedGoogle Scholar
  39. Sitek B, Sipos B, Alkatout I, Poschmann G, Stephan C, Schulenborg T, et al. Analysis of the pancreatic tumor progression by a quantitative proteomic approach and immunhistochemical validation. J Proteome Res. 2009;8:1647–56.View ArticlePubMedGoogle Scholar
  40. Lai KC, Liu CJ, Chang KW, Lee TC. Depleting IFIT2 mediates atypical PKC signaling to enhance the migration and metastatic activity of oral squamous cell carcinoma cells. Oncogene. 2013;8(32):3686–97.View ArticleGoogle Scholar
  41. Savoy RM, Ghosh PM. The dual role of filamin A in cancer: can’t live with (too much of) it, can’t live without it. Endocr Related Cancer. 2013;20:341–56.View ArticleGoogle Scholar
  42. Nakamura F, Stossel TP, Hartwig JH. The filamins: organizers of cell structure and function. Cell Adh Migr. 2011;5:160–9.View ArticlePubMedPubMed CentralGoogle Scholar
  43. Razinia Z, Makela T, Ylanne J, Calderwood DA. Filamins in mechanosensing and signaling. Ann Rev Biophys. 2012;41:227–46.View ArticleGoogle Scholar
  44. Flevaris P, Stojanovic A, Gong H, Chishti A, Welch E, Du X. A molecular switch that controls cell spreading and retraction. J Cell Biol. 2007;179:553–65.View ArticlePubMedPubMed CentralGoogle Scholar
  45. Kasza KE, Nakamura F, Hu S, Kollmannsberger P, Bonakdar N, Fabry B, et al. Filamin A is essential for active cell stiffening but not passive stiffening under external force. Biophys J. 2009;96:4326–35.View ArticlePubMedPubMed CentralGoogle Scholar
  46. Velkova A, Carvalho MA, Johnson JO, Tavtigian SV, Monteiro AN. Identification of Filamin A as a BRCA1-interacting protein required for efficient DNA repair. Cell Cycle. 2010;9:1421–33.View ArticlePubMedPubMed CentralGoogle Scholar
  47. Uhlen M, Bjorling E, Agaton C, Szigyarto CA, Amini B, Andersen E, et al. A human protein atlas for normal and cancer tissues based on antibody proteomics. Mol Cell Proteomics. 2005;4:1920–32.View ArticlePubMedGoogle Scholar
  48. Logsdon CD, Simeone DM, Binkley C, Arumugam T, Greenson JK, Giordano TJ, et al. Molecular profiling of pancreatic adenocarcinoma and chronic pancreatitis identifies multiple genes differentially regulated in pancreatic cancer. Cancer Res. 2003;63:2649–57.PubMedGoogle Scholar
  49. Bedolla RG, Wang Y, Asuncion A, Chamie K, Siddiqui S, Mudryj MM, et al. Nuclear versus cytoplasmic localization of filamin A in prostate cancer: immunohistochemical correlation with metastases. Clin Cancer Res. 2009;15:788–96.View ArticlePubMedPubMed CentralGoogle Scholar
  50. Sun GG, Lu YF, Zhang J, Hu WN. Filamin A regulates MMP-9 expression and suppresses prostate cancer cell migration and invasion. Tumour Biol. 2014; 35:3819–3826.Google Scholar
  51. Frierson HF, El-Naggar AK, Welsh JB, Sapinoso LM, Su AI, Cheng J, et al. Large scale molecular analysis identifies genes with altered expression in salivary adenoid cystic carcinoma. Am J Pathol. 2002;161:1315–23.View ArticlePubMedPubMed CentralGoogle Scholar

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© The Author(s). 2016

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