Comparative Proteomics Approach to Screening of Potential Diagnostic and Therapeutic Targets for Oral Squamous Cell Carcinoma^,

Abstract

This work demonstrates that a comprehensive strategy of proteomics identification combined with further validation and detailed functional analysis should be adopted in the field of cancer biomarker discovery. A comparative proteomics approach was utilized to identify differentially expressed proteins in 10 oral squamous carcinoma samples paired with their corresponding normal tissues. A total of 52 significantly and consistently altered proteins were identified with eight of these being reported for the first time in oral squamous carcinoma. Of the eight newly implicated proteins, RACK1 was chosen for detailed analysis. RACK1 was demonstrated to be up-regulated in cancer at both the mRNA and protein levels. Immunohistochemical examination showed that the enhanced expression of RACK1 was correlated with the severity of the epithelial dysplasia as well as clinical stage, lymph node involvement, and recurrence, which are known indicators of a relatively poor prognosis in oral squamous carcinoma patients. RNA interference specifically targeted to silence RACK1 could initiate apoptosis of oral squamous carcinoma cells. Taken together, the results indicate that RACK1 is up-regulated in oral squamous carcinoma, not only being closely related to cell proliferation and apoptosis but also linked to clinical invasiveness and metastasis in carcinogenesis. The observations suggest that RACK1 may be a potential biomarker for early diagnosis, prognosis, and monitoring in the therapy of oral squamous carcinoma. Further this comprehensive strategy could be used for identifying other differentially expressed proteins that have potential to be candidate biomarkers of oral squamous carcinoma.

Oral squamous carcinoma (OSCC)1 accounts for about 90% of malignant oral lesions and is widely recognized as the most frequently occurring malignant tumor of oral structures. Each year ∼500,000 new cases are diagnosed worldwide with only a 50% survival rate over 5 years (1). In the early stages, the tumor responds well to treatment based on a combined therapy as evidenced by the effect that 80% of the patients have an expected 5-year survival. However, the response to treatment is much weaker for patients in late stage OSCC (2). As is the case with tumors in general, OSCC is believed to develop following a multistep process, initially with premalignant lesions, among which oral leukoplakia (OLK) is the most common (up to 85% of all premalignant lesions), through hyperplasia to dysplasia, then carcinoma in situ, and finally invasive carcinoma (3). Consequently there is a need to devise critical tools for the early detection of OSCC and the monitoring of disease progression. In addition, the identification of therapeutic targets is an attractive strategy to relieve further the burden of OSCC. Among these tools, validated biomarkers are viewed as the most important; therefore there is a critical need to discover new specific biomarkers in OSCC.

Proteomics, a study of the complete protein complements of the cell, is a promising approach in the identification of proteins that may be used as new targets for therapeutic intervention and as markers for early detection of cancers (4). Proteomics has been successfully used in studies of various tumors, and there is a large volume of data on biomarkers in different tumor cells, tissues, and body fluids (5). Previous studies have also involved the preliminary application of proteomics in the identification of the biomarkers for OSCC (6–8). Comparison of protein expression profiles between OSCC and normal cell lines or tissues has revealed replicable and significant changes in the expression levels of a number of proteins, including some metabolic enzymes, signal transduction proteins, and oncoproteins (6–8). However, few of the above proteins were found to vary in concert, thus reflecting their regional variability or tissue heterogeneity. Furthermore few of them have been functionally analyzed for their roles in oral carcinogenesis, and therefore there is a lack of the fundamental understanding required for clinical applications and a need for a better comprehension of the underlying biological processes (9, 10).

It has been widely accepted that a major challenge to cancer proteomics is the integration of biochemical, genetics, and proteomics data in the detection of biomarkers to provide the impetus for the next level of clinical application (11, 12). In this study, differentially expressed proteins, between paired cancer tissues and corresponding normal tissues, were profiled from 10 OSCC patients. One of these proteins, RACK1, a scaffold protein for many kinases and a receptor in a wide range of critical biological responses, was chosen for validation and functional analysis. The data resulting from the study were expected to lead to an improved understanding of the involvement of different biomarkers in the initiation and development of oral cancer and thereby ultimately promote the translation of experimental findings into clinical applications.

EXPERIMENTAL PROCEDURES

Tissue Samples—

All tissue specimens were obtained from West China Stomatological Hospital, Sichuan University. The specimens were examined histologically after staining with hematoxylin and eosin staining, and the clinicopathologic stage was determined according to the TNM classification system of the International Union against Cancer (13). Tumors were classified by two experienced pathologists. For two-dimensional electrophoresis (2-DE), Western blotting, and RT-PCR, OSCC tissues paired with corresponding normal tissue were collected from 10 OSCC patients who underwent oral surgery (see Table II). The tumor tissues were incised from the center of the tumor mass, and the paired corresponding normal tissues were incised along a surgical safety border that was verified with frozen section staining. All the tissue specimens were snap frozen in liquid nitrogen for proteomics analysis. For immunohistochemistry (IHC) analyses, 20 normal tissues were collected from healthy persons undergoing plastic surgery, and 48 OLK as well as 76 OSCC specimens were recruited from the archives of the pathology department for which a 3-year follow-up was done. All clinical information was obtained from archives of case history. Informed consent was obtained from all of patients or their relatives for the use of their tissues in the experimental procedures. The project was approved by the Scientific and Ethics Committee of Sichuan University.

Table II.

The clinical and pathologic data of OSCC patients for 2-DE

Sample no.	Gender	Age	Location	Histological typea	TNM classification	Clinical stage	-Fold change (cancer versus normal)
		yr
1	Male	56	Tongue	Well diff. SCC	T4N1M0	IV	5.8
02	Male	61	Buccal mucosa	Well diff. SCC	T0M0N0	I	3.4
03	Male	57	Tongue	Well diff. SCC	T1N0M0	I	2.4
04	Female	58	Tongue	Well diff. SCC	T1N0M0	I	2.9
05	Female	59	maxima	Mod. diff. SCC	T1N1M0	I	5.1
06	Female	56	Gingival	Well diff. SCC	T2N0M0	II	4.7
07	Male	47	Tongue	Well diff. SCC	T2N0M0	III	3.9
08	Male	62	Buccal mucosa	Poor. diff. SCC	T3N1M0	III	4.8
09	Male	42	Buccal mucosa	Mod. diff. SCC	T2N0M0	II	2.3
10	Male	46	Tongue	Mod. diff. SCC	T2N0M0	II	4
Total							3.9 ± 1.2 (p < 0.01)

^adiff, differentiated; Poor., poorly; Mod., moderately; SCC, squamous cell carcinoma.

Two-dimensional Electrophoresis—

0.1 g of tissue samples was cut into ∼2-mm³ pieces and then crushed to a fine powder in liquid nitrogen. The powder was dissolved in 1 ml of lysis buffer (7 m urea, 2 m thiourea, 4% chaps, 50 mm DTT, 2% Ampholyte, pH 3–10; Bio-Rad). Samples were lysed by ultrasonification in an ice bath for eight cycles, each consisting of 5-s sonication followed by a 10-s break, and then held for 30 min on ice with periodic vortexing. The lysates were centrifuged at 20,500 × g for 60 min at 4 °C. The supernatant was precipitated with acetone at −20 °C for 2 h and resolved with 600 μl of rehydration buffer. The protein concentration of the supernatants was determined using a Bio-Rad protein quantitation kit. The protein samples were aliquoted and stored at −80 °C. IPG strips were passively rehydrated using 400 μl (equal to 1 mg of protein) of each paired preparation (17 cm, pH 3–10 non-linear; Bio-Rad). After 14 h of rehydration, the strips were transferred to an IEF cell (Bio-Rad). IEF was performed as follows: constant power (50 μA/IPG strip) at 250 V for 30 min, linear; 1000 V for 1 h, rapid; linear ramping to 10,000 V for 5 h; and finally 10,000 V for 6 h. Once IEF was completed, the strips were equilibrated with 50 mm Tris/HCl, pH 8.8, 6 m urea, 20% glycerol, 2% SDS, 10 mm DTT for 15 min; washed with 50 mm Tris/HCl, pH 8.8, 6 m urea, 20% glycerol, 2% SDS, 200 mm iodoacetamide for another 15 min; transferred to the top of SDS gradient gels (12%); and finally embedded in low melting agarose subjected to SDS-PAGE at constant current (10 mA for and initial 30 min and then 30 mA/IPG strip to the end). The protein spots were visualized with Coomassie Brilliant Blue R-250 (Merck). Each experiment was performed twice to ensure the accuracy of analyses.

Image Analysis—

The images were scanned using a Bio-Rad high quality white light GS-800 scanner (400–750 nm). The differentially expressed proteins were identified using PDQuest 2-DE analysis software (Bio-Rad). The quantity of each spot in a gel was normalized as a percentage of the total quantity of all spots in that gel and evaluated in terms of OD. Only those spots that changed consistently and significantly (more than 2-fold) were selected for MS/MS analysis.

Tryptic In-gel Digestion—

In-gel digestion of proteins was carried out using mass spectrometry grade Trypsin Gold (Promega, Madison, WI) according to the manufacturer's instructions. Briefly spots were cut out of the gel (1–2-mm diameter) and destained twice with 100 mm NH₄HCO₃, 50% ACN at 37 °C for 45 min in each treatment. After dehydration with 100% ACN and drying, the gels were preincubated in 10–20 μl of trypsin solution (10 ng/μl) for 1 h. Sufficient digestion buffer (40 mm NH₄HCO₃, 10% ACN) was then added to cover the gels, which were incubated overnight at 37 °C (12–14 h). Tryptic digests were extracted using Milli-Q water followed by double extraction with 50% ACN, 5% TFA for 1 h each time. The combined extracts were dried in a SpeedVac concentrator (Thermo Scientific) at 4 °C. The samples were then subjected to mass spectrometry.

MS Peptide Sequencing—

Peptides from the solubilized digests were analyzed using a Q-TOF Primer mass spectrometer (Micromass, Manchester, UK) coupled to the ESI ion source. The sample was introduced, and the voltage was set to 3.0 kV. The automatic scan rate was 1.0 s with an interscan delay of 0.02 s. Spectra were accumulated until a satisfactory signal/noise ratio had been obtained. Only double or more than double charge peaks in the mass range from 400 to 1600 m/z were considered for MS/MS. Ions exhibiting a detection intensity exceeding 10 counts/s were selected for production of ion spectra by CID. A switch to the MS survey was made when either a duration of 10 s had elapsed or the ion intensity had fallen below 2 counts/s. Trypsin autolysis products and keratin-derived precursor ions were automatically excluded. The CID cell was filled with argon at a flow rate of 0.45 ml/min, and the collision energy varied between 18 and 57 eV depending on the mass of the precursor. To enable accurate mass determination, a standard calibration peptide (Glu-fibrinopeptide) was used in the external calibration of the instrument prior to data-directed analysis acquisition. Three MS/MS ions were selected for each survey scan; thus all data used to extract peak information, which was used to create the MS/MS peak list, were generated from one combined spectrum.

Protein Identification and Database Searching—

The MS/MS data, “pkl list” (pkl) files acquired by the software ProteinLynx 2.2.5 (Waters), included the mass values and the intensity and the charge of the precursor ions (parent ions with +2 or +3 charge in this study). The pkl files were analyzed using the MASCOT search engine (Matrix Science) against the Swiss-Prot protein database. Searching parameters were set as follows: enzyme, trypsin; allowance of up to one missed cleavage peptide; mass tolerance, 1.0 Da; MS/MS mass tolerance, 0.3 Da; fixed modification parameter, carbamoylmethylation (Cys); variable modification parameters, oxidation (at Met) and phosphorylation (ST); auto hits allowed (only significant hits were reported); result format, peptide summary report. Proteins were identified on the basis of two or more peptides whose ions scores both exceeded the threshold, p < 0.05, which indicated the 95% confidence level for these matched peptides.

Immunohistochemistry—

Sections were stained with rabbit polyclonal antibodies against RACK1 (1:200; Santa Cruz Biotechnology) using the DakoCytomation EnVision system (DakoCytomation Corp., Carpinteria, CA) according to the manufacturer's instructions.

Saturation and intensity of immunostained cells were evaluated over eight visual fields at a power of ×400 under a light microscope (Olympus Optical, Tokyo, Japan). In statistical analysis, with reference to the study by Kreisberg et al. (14), total staining of RACK1 was scored as the product of the staining intensity (on a scale of 0–3: negative = 0, weak = 1, moderate = 2, and strong = 3) × the percentage of cells stained (positively recorded on an ordered categorical scale: 0 = zero, 1 = 1–25%, 2 = 26–50%, and 3 = 51–100%), resulting in a scale of 0–9. The evaluation was performed by two independent investigators.

Cell Culture—

Tca8113 cells, a poorly differentiated lingual squamous cell carcinoma cell line, was maintained in Dulbecco's modified Eagle's medium (Invitrogen) containing 10% fetal calf serum (Invitrogen), 100 units/liter penicillin, and 10 mg/liter streptomycin. HOK16E6E7 cells, a human immortalized oral keratinocyte cell line, was cultured in keratinocyte growth medium containing 0.15 mm calcium and supplemented with epidermal growth factor (Invitrogen). Both cell lines were maintained at 37 °C in an atmosphere containing 5% CO₂.

RNA Interference—

Four pairs of RACK1-specific small interfering RNA (siRNA) and the siRNA for negative control were synthesized by Ambion Inc. The cells were grown on a 6-well plate in growth medium until they reached 70% confluence; transfections of siRNA were carried out with Lipofectamine 2000 (Invitrogen) according to the manufacturer's instructions. A pilot study was carried out to screen the most efficient RACK1-specific siRNA. siRACK1-231 siRNA had proven effective in the inhibition of RACK1 expression by both RT-PCR and Western blotting analysis and thus was utilized for the following experiments.

Semiquantitative RT-PCR—

Total RNA was isolated using TRIzol reagent (Invitrogen). cDNA was synthesized using the QuantiTect Reverse Transcription kit (Qiagen) according to the manufacturer's instructions. PCR was performed using human RACK1 primers (sense, 5′-GCTCTGCCATAAACTTCTAGCGTGTGC-3′; antisense, 5′-CTGTGCTTCTGGAGGCAAGGATGGCCA-3′). The amplification parameters consisted of 27 cycles at 94 °C for 30 s, 56 °C for 30 s, and 72 °C for 30 s. The PCR products (5 μl) were analyzed by electrophoresis through 1% agarose gels and visualized by SYBR Gold (Molecular Probes, Eugene, OR) staining.

Western Blotting—

Protein extracts were prepared using a lysis buffer containing protease inhibitor mixture 8340. For Western blotting analysis, ∼20 μg of protein were separated by 12% SDS-PAGE, then transferred to a PVDF membrane (Millipore), and probed with polyclonal rabbit anti-RACK1 antibody (1:1000; Santa Cruz Biotechnology). The blots were labeled with horseradish peroxidase-conjugated secondary antibodies (1:10,000) and visualized with an ECL detection system (Pierce).

MTT Assay—

Cell growth and viability were assessed using an MTT cell proliferation kit (Roche Applied Science). The cells were seeded on 96-well microplates at 1.0 × 10⁴/well. At 24, 48, and 72 h post-transfection with RACK1-231 siRNA, the cells were subsequently incubated with 10 μl of MTT labeling reagent for 4 h followed by addition of 100 μl of solubilization solution into each well. The plates were left in a dark room overnight, and OD was measured at a 590-nm test wavelength and a 620-nm reference wavelength with an ELISA multiwell spectrophotometer (Molecular Devices Corp., Sunnyvale, CA). The cell vitality index was calculated according to the following formula: VI % (vitality index) = OD treated wells/OD control wells × 100.

Colony Formation Assay—

The cells were seeded at 3 × 10²/dish (60-mm diameter; Costar) and incubated overnight. At 6 h post-transfection, cells were supplemented with fetal bovine serum-containing medium and allowed to grow for an additional 10 days. The colonies were then fixed with methanol and stained with crystal violet (Sigma).

Flow Cytometry—

The cells were seeded on 6-well plates (Costar) at 2.5 × 10⁵ cells/well. Cells were harvested at 48 h post-transfection. After washing once more with PBS, the cells were then resuspended and incubated in propidium iodide/Annexin V solution (R&D Systems). Flow cytometry assay was performed on a FACSAria flow cytometry system (BD Biosciences). Data were analyzed using BD FACSDiva software.

Statistical Analysis—

A paired t test and one-way analysis of variance were used to analyze differences between groups. p < 0.05 was considered significant in all analyses. Computations were performed using the SPSS version 11.5 software package.

RESULTS

2-DE Profiling of OSCC and the Corresponding Normal Tissues—

2-DE with immobilized pH gradients was used to study proteins extracted from OSCC tumor tissue and control normal tissue. A total of 10 pairs of tissue lysates were analyzed. To ensure reproducibility, every sample was studied at least twice, and a check was made to verify that the same protein patterns were obtained. When the protein pattern of primary tumor and corresponding normal tissue were compared, multiple proteins were found to be differentially expressed. Fig. 1 shows 2-DE patterns obtained from tumor and corresponding normal tissues. Computer-assisted PDQuest gel analysis software (Bio-Rad) was used to perform qualitative and quantitative analysis of the differentially expressed proteins. Those proteins on two-dimensional gels with repeatability in at least three paired samples and with an expression difference beyond 2-fold were selected for MS analysis.

Fig. 1.

Proteomics analysis of OSCC tissues and corresponding normal tissues using 2-DE gels. Whole cell lysates (1 mg) from OSCC (A) and normal (B) tissues were separated by 2-DE and visualized by Coomassie Blue staining. Arrows indicate identified protein spots significantly and consistently altered between carcinoma tissue and control normal tissues.

Identification of Putative OSCC Biomarkers—

Fifty-two different proteins from 2-DE gels were successfully identified from the above altered proteins spots; details of the proteins identified are listed in Table I. The observed molecular mass and pI for most identified proteins matched very well with their theoretical values. For some proteins (marked with Footnote g in Table I), differences between the experimental molecular weight/pI and the theoretical value occurred; these may be due to post-translational modifications such as truncation and/or protein phosphorylation (15). The proteins were assigned into 10 functional categories based mainly on gene ontology and associated biological processes as shown in Table I. It should be noted that eight of the identified proteins had not been reported previously as differentially expressed in the OSCC proteome: namely, RACK1, calcium-binding protein P22, protein DJ-1, Rho GDP dissociation inhibitor 1, proteasome activator complex subunit 2, S100 family proteins (S100-A7, S100-A9, and S100-A14), translationally controlled tumor protein, and peroxiredoxin-4.

Table I.

Proteins identified by mass spectrometry as significantly changed in expression between OSCC tissues and control normal tissues

No.a	Acc. no.b	Protein identity	Gene name	Molecular mass/pI (theo.)c	No. of pep.d	Scoree	-Fold changef
Cell apoptosis/differentiation/development
19	Q06100	Galectin-1	Cgl1	15,078/5.65	13	116	+3.0
21	P58546	Myotrophin	MTPN	13,058/5.27	8	191	+2.9
14	P47929	Galectin-7	LGALS7	15,123/7.03	29	1326	+2.8
Oncoprotein and tumor suppressor
17g	Q99497	Protein DJ-1	PARK7	20,050/6.33	16	566	+4.1
13	Q01995	Transgelin	TAGLN	22,653/8.87	8	188	+2.1
20	P16949	Stathmin	STMN1	17,292/5.76	5	183	+2.2
32	P13693	Translationally controlled tumor protein	TPT1	19,699/4.84	7	105	+4.2
48	P15531	Nucleoside-diphosphate kinase A	NME1	17,309/5.83	11	125	−2.3
Signal transduction
1	P63244	RACK1	GNB2L1	35,055/7.60	5	205	+3.9
26	P52565	Rho GDP dissociation inhibitor 1	GDIA1	23,250/5.02	6	254	+2.6
27	P52566	Rho GDP dissociation inhibitor 2	ARHGDIB	22,988/5.1	6	158	+3.5
43	Q9NUA4	Tyrosine-protein kinase HCK	HCK	60,075/6.27	1	37	−5.5
Calcium-binding protein
31	Q96HK3	Calmodulin	CALM1	16,827/4.09	11	403	+3.6
33	Q99653	Calcium-binding protein P22	CHP	22,442/4.98	2	37	+2.6
51	P05109	Protein S100-A8	S100A8	10,885/6.51	1	42	−3.1
49	P31151	Protein S100-A7	S100A7	11,564/6.27	6	110	−10.7
50	P06702	Protein S100-A9	S100A9	13,291/5.71	10	312	−11.7
52	Q9HCY8	Protein S100-A14	S100A14	11,826/5.16	5	190	−3.1
38	P04083	Annexin A1	ANXA1	38,918/6.57	25	985	−3.4
35	P07355	Annexin A2	ANXA2	38,808/7.57	15	579	−10.4
47	P08758	Annexin A5	ANXA5	35,971/4.94	19	630	−18.4
Cell motility
29	P06753	Tropomyosin α-3 chain	TPM3	32,856/4.68	11	189	+7.2
Proteolysis
34	P49721	Proteasome subunit β type 2	PSMB2	22,993/6.51	1	51	−3.2
46	Q06323	Proteasome activator complex subunit 2	PSME2	27,515/5.44	17	327	−4.1
42	P04080	Cystatin-B	CSTB	13,291/5.71	21	533	−3.5
28	P07858	Cathepsin B precursor	CTSB	38,766/5.88	3	109	+3.1
7	P29508	Serpin B3 (SCC1)	SERPINB3	44,564/6.35	7	230	+7.3
Stress resistance
40	P02511	α-Crystallin B chain	CRYAB	20,146/6.76	24	448	−4.5
16	P04792	Heat-shock protein β-1	HSPB1	22,826/5.98	6	148	+2.4
Redox regulation
39	P04179	Superoxide dismutase	SOD2	24,881/8.35	15	144	+7.4
45	Q13162	Peroxiredoxin-4	PRDX4	30,749/5.86	9	162	−3.7
41	Q06830	Peroxiredoxin-1	PRDX1	22,324/8.27	8	82	−2.5
Structural component
23	P68363	Tubulin α ubiquitous chain	None	50,120/4.94	14	298	+4.1
6	O00151	PDZ and LIM domain protein 1	PDLIM1	36,513/6.56	14	525	+5.2
4	P31942	Heterogeneous nuclear ribonucleoprotein H3	HNRPH3	36,960/6.37	3	109	+2.8
Metabolism
30	O75347	Tubulin-specific chaperone A	TBCA	12,904/5.25	5	118	+3.5
18	P63241	Eukaryotic translation initiation factor 5A-1	EIF5A	17,049/5.08	6	50	+3.2
12g	P14618	Pyruvate kinase isozymes M2	PKM2	58,470/7.96	10	465	+3.0
9g	P60174	Triose-phosphate isomerase	TPI1	26,938/6.45	10	168	+3.8
25	P00738	Haptoglobin precursor	HP	45,861/6.13	6	85	+2.7
24	P02787	Serotransferrin precursor	TF	79,280/6.81	22	232	+3.9
8	Q14376	UDP-glucose 4-epimerase	GALE	38,656/6.26	8	162	+3.2
36	P40925	Malate dehydrogenase cytoplasmic	MDH1	36,403/6.91	5	84	−3.5
22	P07108	Acyl-CoA-binding protein	DBI	10,038/6.12	7	96	+4.8
10	Q01469	Fatty acid-binding protein, epidermal	FABP5	15,497/6.60	6	196	+4.6
37	P49643	Carbonic anhydrase 3	PRIM2A	59,233/7.97	24	571	+4.2
15	P61088	Ubiquitin-conjugating enzyme E2 N	UBE2N	17,184/6.13	2	68	−2.6
11	P04406	Glyceraldehyde-3-phosphate dehydrogenase	GAPDH	36,204/8.57	7	163	+2.4
2	P06732	Creatine kinase M-type	CKM	43,302/6.77	21	708	+7.8
Transcription
44	P26641	Elongation factor 1-γ	EFIG	50,429/6.25	2	40	−2.5
Blood coagulation
5	P02675	Fibrinogen β chain precursor	FGB	56,577/8.54	14	55	+3.0
Ion channel
3g	P45880	Voltage-dependent anion-selective channel protein 2	VDAC2	38,068/6.33	3	47	+4.7

^aThe spot numbers correspond to those on the 2-DE images shown in Fig. 1.

^bSwiss-Prot accession number.

^cTheoretical molecular mass (kDa) and pI from the ExPASy database.

^dThe number of unique peptides identified by MS/MS sequencing.

^eProbability-based MOWSE (molecular weight search) scores.

^fExpression change level in OSCC tumor tissue compared with control (+, increase in tumor; −, decrease in tumor).

^gDifferences between the experimental molecular mass/pI and the theoretical value were noted.

As shown in Table II, among the identified proteins, RACK1 exhibited a high expression in cancer tissues (3.9-fold elevation) when compared with the corresponding normal tissues. The mass spectra of RACK1 are shown in Fig. 2. The MS/MS data supporting this assertion appeared to be reliable (MASCOT score, 205; sequence coverage, 11%), and RACK1 displayed significant alteration in 10 cancer tissues. RACK1 has attracted a lot of interest for its pivotal role in serving as a scaffold protein for numerous kinases and receptors. Although RACK1 is recognized as essential for cell proliferation and survival under normal physiological conditions, the role of RACK1 in tumor formation and development has proven to be complex and remains controversial (16). Therefore, in view of the fact that RACK1 is an important factor in the maintenance of normal cellular homeostasis and that its elevated expression contributes to oncogenic transformation, the involvement of this protein in OSCC became the subsequent focus of the study.

Fig. 2.

Results of RACK1 as the representative of protein identification using ESI-Q-TOF-MS/MS. A, B, and C, output of the database searching by the MASCOT program using MS/MS data used in the identification of RACK1. The matched peptides were shown in bold red. D, MS/MS spectrum of parent ions with m/z values of 655.2098.

Overexpression of RACK1 in OSCC Cancer Cells and Cancer Tissues—

Evaluation of RACK1 expression in OSCC cells (Tca8113) relative to the human immortalized oral keratinocytes (HOK16E6E7) and of RACK1 expression in OSCC tumor tissues versus corresponding normal tissues was performed by semiquantitative RT-PCR and Western blotting. Consistent with observations from 2-DE, expression of RACK1 was markedly increased at both the mRNA and protein levels in Tca8113 cells and tumor tissues compared with normal keratinocytes and tissues (Fig. 3).

Fig. 3.

Validation of RACK1 in OSCC tissues and cells relative to normal tissues and normal keratinocytes. A, a, 2-DE gel map of RACK1 in OSCC tissues and corresponding normal tissues; arrows indicate RACK1 spots. Lower part, three-dimensional image of RACK1 expression by PDQuest software. A, b, chart indicating RACK1 expression in 2-DE. B, representative image of quantitative measurement of RACK1 protein in 10 pairs of OSCC tissues and corresponding normal tissues by Western blotting analysis using RACK1-specific antibody. C, representative image of mRNA levels of RACK1 between OSCCs and their corresponding normal tissues measured by semiquantitative reverse transcription PCR. D, protein levels of RACK1 in Tca8113 and HOK16E6E7 cells revealed by Western blotting analysis using RACK1-specific antibody. E, mRNA levels of RACK1 in Tca8113 and HOK16E6E7 cells measured by semiquantitative RT-PCR. Error bars represent ±S.D. of the mean.

Overexpression of RACK1 in Clinical OSCC Specimens Examined by Immunohistochemical Assay—

To further validate the elevation of RACK 1 in clinical samples and with an aim to determine its role in OSCC carcinogenesis and prognosis, 144 specimens in different clinicopathological stages were examined. Their clinical information is shown in Table III.

Table III.

RACK1 immunostaining results among normal, precancerous OLK, and infiltrated cancer tissues

Tissue type	Number	Gender		Locationa					Positive rate	Intensity	Staining scoreb	p value
		Male	Female	B	T	F	G	O
Normal tissue	20	15	5	10	4	0	3	3	0.65 ± 0.58	1.20 ± 1.10	1.30 ± 1.26	0.00c
OLK without dysplasia	21	11	10	7	14	0	0	0	0.90 ± 0.62	1.38 ± 0.97	1.52 ± 0.98
OLK with dysplasia	27	19	8	10	16	0	1	0	1.30 ± 0.54	1.81 ± 0.74	2.81 ± 2.21
OSCC	76	45	31	16	32	6	12	10	2.49 ± 0.53	2.25 ± 0.82	5.55 ± 2.42

^aB, buccal mucosa; T, tongue; F, oral floor; G, gingival; O, others.

^bTotal staining of RACK1 was scored as the product of the staining intensity (on a scale of 0–3) × the percentage of cells stained (on a scale of 0–3).

^cp value between the four groups: normal tissue, OLK without dysplasia, OLK with dysplasia, and infiltrated OSCC.

The intensity of RACK1 staining (scale, 0–3) and the percentage of the stained cells (scale, 0–3) were determined independently. The positive staining of epithelium cells was expressed as yellow-brown granules with weak to strong intensity (Fig. 4, arrows). Total staining was expressed as a product of the two numbers (resulting in a staining scale of 0–9). RACK1 immunoreactivity was readily detected in the cytoplasm and occasionally in the nucleus. In normal epithelium, positive staining was rarely detected or was limited to the most active parabasal and basal layers. As the severity of epithelial hyperplasia was increased, the positive staining spread into other layers with enhanced intensity in previously affected layers (Fig. 4). The staining among four groups including normal epithelium, OLK without dysplasia, OLK with dysplasia, and infiltrated OSCC revealed a remarkable increasing trend that paralleled the increasing severity of epithelium dysplasia. Statistical tests suggested that RACK1 expression in OSCC tumor tissues was significantly higher than in normal tissues and precancerous tissues (Table III).

Fig. 4.

Expression of RACK1 in human oral squamous carcinoma. Immunohistochemical analysis showed the expression of RACK1 to increase gradually with the progression of carcinogenesis from normal epithelium to premalignant lesion to invasive carcinoma. The staining of immunoreactivity was expressed as a product of the intensity and the proportion of cells staining positive. The positive staining of epithelium cells was expressed as yellow-brown granules with weak (thin arrows) to moderate-strong (thick arrows) intensity. RACK1 immunoreactivity was readily detected in the cytoplasm and occasionally in the nucleus. A, in normal epithelium, the positive staining was rarely detected or was limited to the most active basal or parabasal layer of the epithelium. With increasing severity of epithelial hyperplasia, the positive staining spread into other layers, and staining intensity was enhanced. B, for OLK without dysplasia, the immunostaining profile was similar to that of normal epithelium. C, for OLK with dysplasia, the positive staining spread sporadically into other epithelial layers with weak to moderate staining. D, for infiltrated OSCC, the positive staining spread to almost all the cancer nest with moderate to strong staining. E, RACK1 expression in recurrent OSCC lesion in the neck region exhibited the strongest immunoreactivity.

In addition, the correlation of RACK1 expression in OSCC with clinicopathological indexes was evaluated, including with gender, age, smoking, and drinking habits; differentiation, lymph node involvement; clinical stage; recurrence; etc. The data indicated no apparent relationship between staining patterns and gender, age, differentiation, and habits. However, the patients who had advanced clinical stage disease (p = 0.005), metastasis (p < 0.001), or recurrent lesion (p = 0.01) showed increased RACK1 expression. Because the latter three factors have a great impact on the prognosis of patients, we propose that RACK1 is a good potential predictor of tumor outcome (Table IV).

Table IV.

Immunostaining of RACK1 in OSCC: correlation with clinicopathological parameters

The staining score and p value were evaluated as in Table III.

Factors	Number	Staining score	p value
Gender
Male	48	5.46 ± 2.35	0.66
Female	28	5.71 ± 2.58
Age
≤30 yr	18	5.28 ± 2.35	0.28
31–59 yr	44	5.575 ± 2.61
≥60 yr	14	5.86 ± 2.49
Differentiation
Well	33	4.85 ± 2.00	0.07
Moderate	21	5.90 ± 1.89
Poor	22	6.27 ± 2.58
T stage
T1	20	5.10 ± 2.59	0.60
T2	30	6.07 ± 2.41
T3	11	5.55 ± 2.74
T4	15	5.80 ± 2.34
N (lymph node metastasis)
N0	38	4.41 ± 2.09	0.00
N1N2	38	7.03 ± 2.11
Clinical stage
I	18	4.44 ± 2.40	0.005
II	10	4.40 ± 1.50
III	36	6.52 ± 2.38
IV	12	5.25 ± 2.20
Recurrence
No	56	5.22 ± 2.42	0.01
Yes	20	6.80 ± 2.21
Drinking
No	54	5.65 ± 2.32	0.84
Yes	22	5.77 ± 2.86
Smoking
No	41	5.61 ± 2.30	0.89
Yes	35	5.69 ± 2.63

The Effect of Silencing RACK1 Expression on the Proliferation and Apoptosis of OSCC Cancer Cells and Oral Keratinocytes—

To further verify the oncogenic property and evaluate the therapeutic potentiality of RACK1, OSCC cancer cell line Tca8113 was transiently transfected with a pooled mixture of RACK1-specific siRNA. Semiquantitative RT-PCR and Western blotting confirmed that RACK1 expression in both cell lines was markedly blocked upon treatment with siRACK1-231 (Fig. 5). To examine whether silencing the expression of RACK1 had an effect on OSCC cell proliferation, cell viability was first determined by MTT assay. Compared with the survival rates of the controls, Tca8113 transfected with siRACK1-231 showed a significant decrease (from 90 to ∼40%) in cell survival rate 48 h post-transfection (Fig. 6C). siRACK1-231-mediated inhibition of OSCC cell growth was further demonstrated by colony formation assay. The data demonstrated that the number of colonies was markedly decreased in the siRACK1-231-treated group with statistical significance (210 ± 13, 188 ± 9, 173 ± 6, and 103 ± 8; p = 0.00) (Fig. 6B). To evaluate whether siRACK1-231-mediated inhibition of OSCC cell growth was associated with cell death, apoptosis was determined by using Annexin V-FITC and propidium iodide double staining as described under “Materials and Methods.” The results showed that, in Tca8113 cells, siRACK1-231 induced 20% cell apoptosis compared with the controls (2.1% for PBS controls, 2.3% for liposome controls, and 4.7% for the siRNA for negative control (Fig. 6A). Our data suggested that RACK1 appeared to be functionally important in mediating cell proliferation and apoptosis in oral carcinogenesis. To determine the effect of this silencing on non-cancer cell lines, oral keratinocyte cell line HOK16E6E7 was also transfected with siRACK1-231. MTT assay and flow cytometry analysis were carried out. A clear-cut difference between the effects on the epithelial cells of cancerous and non-cancerous origin was observed (supplemental figure). Collectively our results suggest that silencing RACK1 induces massive apoptosis in cancer cells without apparent toxicity to normal oral keratinocytes.

Fig. 5.

The mRNA and protein expression of RACK1 after specific siRNA transfection in Tca8113 cells. The Tca8113 cells were transfected with siRACK1-231 and the negative control siRNAs (siNC) as described under “Materials and Methods.” The levels of mRNA were determined by semiquantitative RT-PCR (A and C), and the protein levels were determined by Western blotting (B and D). Error bars represent ±S.D. of the mean.

Fig. 6.

Effects of gene silencing of RACK1 on the apoptosis of Tca8113 cells. Three apoptosis-related assays were carried out. A, 48 h after the transfection, Tca8113 cells were analyzed by using flow cytometry. A dot plot display of Annexin V-FITC fluorescence versus propidium iodide (PI) fluorescence is shown in logarithmic scale. Annexin V-positive cells were regarded as apoptotic cells. Upper left quadrant, necrosis cells; lower left quadrant, vital cells; lower right quadrant, early apoptosis cells; upper right quadrant, late apoptosis cells. B, colony formation. 6 h after transfection, Tca8113 cells were supplemented with fetal bovine serum-containing medium and allowed to grow for additional 10 days. The colonies were then fixed with methanol and stained with crystal violet. C, cell viability of Tca8113 cells harvested 24, 48, and 72 h post-transfection after treatment with siRACK1-231 and three different controls. siNC, siRNA for negative control. Error bars represent ±S.D. of the mean.

DISCUSSION

The mortality rate of OSCC has remained relatively high for many years. Consequently there is an urgent need to identify novel tumor markers for early stage diagnosis (17, 18). In response to this need this study used comparative proteomics to annotate the differentially expressed proteins between OSCC and control tissues with an aim to find novel OSCC biomarkers. A total of 52 stable, significantly altered proteins were identified. Some of these proteins, such as SCC1, stathmin, calmodulin, fatty-acid binding protein, glutathione S transferase, galectin-7, calgranulin B, tropomyosin, Annexin A1, and nucleoside-diphosphate kinase A, have been reported to be associated with OSCC in previous studies but without clinical validation and in-depth functional research (6–8). Furthermore eight of the altered expressed proteins, such as Rho GDP dissociation inhibitor 1 (19), proteasome activator complex subunit 2 (20), S100 family proteins (21), translationally controlled tumor protein (22), peroxiredoxin-4 (23), RACK1, calcium-binding protein P22, and protein DJ-1 have been observed to be differentially expressed in cancers from other origins but not previously in OSCC. However, most of these altered proteins have been found to be involved in multiple cellular pathways related to carcinogenesis (e.g. apoptosis, differentiation, proliferation, migration, and invasion). Clearly there is a need for further studies to elucidate the precise functional roles of these individual proteins in the cellular signaling pathway as well as in the initiation and development of cancers.

RACK1 was originally identified as an anchoring protein for protein kinase C (PKC), and as such it has attracted increasing attention. RACK1 is a WD repeat family protein and is predicted to have an architecture with seven blades, homologous with the G protein subunit (24). This protein is highly conserved among all eukaryotes and is also linked to translation initiation in organisms ranging in form from budding yeasts to humans. The protein plays a pivotal role in serving as a scaffold for these kinases, including PKC and subunits (25), PDE4D5 (26), Src family kinases (27), Epstein-Barr virus BZLF1 protein (28), human immunodeficiency virus, type 1 Nef protein (29), and IGF-1 receptor (30), and is a receptor in a wide range of critical biological responses.

The full spectrum of interactions that link RACK1 with tumors has yet to be fully determined. However, some pioneering work on the up-regulation of RACK1 in angiogenesis has been undertaken by Berns et al. (31), and recent investigations regarding the interactions of RACK1 with regulatory factors in signaling transduction pathways have given us a glimpse of the roles of RACK1 in tumorigenesis. Four signaling pathways, including PKC, PDE4D5 (a cyclic AMP-specific phosphodiesterase), tyrosine kinases/phosphatases (27), and signal transducers and activators of transcription (STAT) (33), have been shown to interact with RACK1; these pathways are involved in the coordination of cell growth, adhesion, movement, and division. In view of this, a role of RACK1 in promoting cell proliferation seems probable. In further support of this, a study by Fomenkov et al. (34) showed that ΔNp63 isotypes, lacking the transactivation domain, promote cell proliferation and tumorigenesis in vitro and in vivo; the process is potentially induced by inhibition of nuclear export and degradation of ΔNp63α by both stratifin and RACK1. The marked impact of RACK1 on cell proliferation may be related to the wide variety of signaling complexes to which RACK1 binds in a cell-type specific manner. OSCC appears to be a good model for further study on the cell type-specific involvement of RACK1 in human tumors.

To the best of our knowledge, this is the first report regarding the association of RACK1 with OSCC as based on the proteomics analysis. The staining intensities of RACK1 among normal epithelium, precancerous OLK tissue, and OSCC tissue have revealed a remarkable increased tendency that parallels the severity of the epithelial dysplasia (p = 0.00). The results of this study indicated that overexpression of RACK1 is routinely observed in OSCC cancer cell lines as well as in clinical OSCC specimens, thus suggesting a potential oncogenic property for RACK1 in oral carcinogenesis. Such alterations of RACK1 in the progression of oral carcinogenesis, from normal oral epithelium, OLK with or without dysplasia, to OSCC, appeared to underscore its clinical significance as a diagnostic biomarker. Furthermore those patients at the more advanced clinical stages, those with local or distant lymph node involvements, or those who have had recurrence within 3 years displayed much higher RACK1 expression levels (with statistical significance). The association between the increased expression of RACK1 and clinical stage, metastasis, and recurrence suggests its potential as an independent or supplementary biomarker in the prediction of prognosis for OSCC patients.

Recently RNA interference has been proposed as one of the most novel potential gene therapy strategies (35). The observed significant difference in apoptosis between epithelial cells of cancerous and non-cancerous origin after the silencing of RACK1 expression strongly suggested that RACK1 could be considered as a potential therapeutic drug target against OSCC. Consequently more experiments will be conducted to determine whether silencing RACK1 expression has the same inhibitory effects on OSCC tumor growth in vivo through the establishment of a human OSCC xenograft-nude mouse model.

In conclusion, our study has presented a view of protein alterations in tumorigenesis through combined proteomics analysis, pathological validation, and genomics functional analysis. The findings of all aspects of the current study suggested that RACK1 has substantial clinical impact, but the understanding of the precise mechanisms involved still falls short of that required for the development of practical applications. More targeted research efforts and systems that are more patient-centered are required. Such an approach is in concordance with the conclusions of the Worldwide Strategic Consensus Conference on Biomarker Research that biomarker research efficiently translates any findings into reduced mortality and morbidity from disease (32). Also further research is necessary to establish the in vivo clinical applicability of RACK1 siRNA and to elucidate the mechanisms of apoptosis induction by RACK1. Moreover in addition to the identification of candidate biomarkers by a traditional proteomics approach as used in this study, biomarkers may also be identified via their association with relevant proteins using interaction assays and signal transduction pathway assays. The 52 proteins identified in the present study thus provide a handle by which to identify upstream activators and downstream effectors. The present study has provided a comprehensive and integrated platform to apply powerful novel technologies (such as MS/MS and gene silencing) in the study of clinically relevant samples in a well defined molecular and pathological framework. It is anticipated that such an approach will ultimately ensure that biomarkers with clinical value make their way into routine clinical practice.