EEF1D (eukaryotic translation elongation factor 1δ) is a subunit of the elongation factor 1 complex of proteins that mediates the elongation process during protein synthesis via enzymatic delivery of aminoacyl-tRNAs to the ribosome. Although the functions of EEF1D in the translation process are recognized, EEF1D expression was found to be unbalanced in tumours. In the present study, we demonstrate the overexpression of EEF1D in OSCC (oral squamous cell carcinoma), and revealed that EEF1D and protein interaction partners promote the activation of cyclin D1 and vimentin proteins. EEF1D knockdown in OSCC reduced cell proliferation and induced EMT (epithelial–mesenchymal transition) phenotypes, including cell invasion. Taken together, these results define EEF1D as a critical inducer of OSCC proliferation and EMT.

CLINICAL PERSPECTIVES

  • Oral squamous cell carcinoma is the most common type of cancer in the oral cavity. The discovery of altered proteins is essential to understand mechanisms associated with tumor development and progression.

  • This study suggests that the EEF1D signalling pathway modulates proliferation and invasion, which strongly supports a role for this protein in oral carcinogenesis.

  • Understanding the molecular mechanisms by which tumour cells evoke this process is relevant for cancer therapy.

INTRODUCTION

The EEF1 (eukaryotic translation elongation factor 1) complex is composed of non-ribosomal protein enzymatic factors that, in their GTP-bound form, mediate protein synthesis by recruiting aminoacylated tRNA molecules to the ribosome [1]. Besides the canonical functions in translation elongation, EEF1 proteins are related to other cellular functions, including nuclear export of tRNAs, recognition of damaged proteins and activation of the proteasome degradation system, apoptosis and senescence regulation, and viral propagation [24]. Owing to their canonical and non-canonical functions, a growing body of evidence suggests that up-regulation of EEF1 proteins, particularly the prototypical member EEF1α, may be required for the control of cellular behaviour during tumorigenesis [5]. Furthermore, a few studies have suggested that EEF1D, the δ member of the EEF1 family, may play a role in cancer [69].

The first evidence of EEF1D deregulation in cancer came from the study of Shuda et al. in 2000 [6], who demonstrated higher amounts of EEF1D mRNA in hepatocarcinomas. Later, by using different approaches including chromosomal comparative genomic hybridization, two-dimensional electrophoresis coupled with MALDI–TOF-MS and quantitative PCR, the up-regulation of EEF1D was described in oesophageal carcinomas [7], non-small-cell lung cancers [8] and medulloblastomas [9]. The study by Ogawa et al. [7] showed further that higher EEF1D mRNA levels were correlated with lymph node metastasis, advanced disease stage, and shortened disease-specific survival for patients with oesophageal carcinoma. However, with the exception of this study, little has been uncovered regarding the biological mechanisms related to EEF1D overexpression in cancer, and the involvement of EEF1D in oral tumorigenesis has not yet been investigated.

In the present study, we identified the overexpression of EEF1D in OSCC (oral squamous cell carcinoma) in comparison with oral healthy mucosa by using laser-capture microdissection and MS-based proteomics approaches, which was confirmed by immunohistochemistry in a series of clinical samples. Moreover, co-immunoprecipitation of EEF1D followed by MS in association with protein–protein predictive interaction analysis revealed that EEF1D is necessary for the regulation of cyclin D1 and vimentin. To gain insights into the molecular mechanisms by which EEF1D affects the cell cycle and EMT (epithelial–mesenchymal transition), EEF1D was knocked down in OSCC cells expressing high levels of the transcript. The decrease in EEF1D levels resulted in a decrease in the cell cycling and proliferation, which was associated with a reduction in cyclin D1 production and RB (retinoblastoma protein) phosphorylation. EEF1D knockdown also promoted EMT phenotypes such as modulation of EMT markers [E-cadherin (epithelial cadherin) and vimentin], specific transcription factors (SNAIL1, ZEB1 and ZEB2), production of MMP-2 (matrix metalloproteinase 2) and cell invasion. These results suggest that EEF1D might have a critical role in modulation of proliferation and EMT in OSCCs.

EXPERIMENTAL

Samples

This study was approved by the local ethics committee of the Faculdade de Odontologia de Piracicaba-UNICAMP (protocol number 105/2013), written informed consent was obtained from all participants. The proteomics analysis was conducted using ten pairs of OSCC and adjacent normal tissue samples, collected from ten patients. Fresh samples were divided: one part was fixed in formalin for haematoxylin and eosin staining and immunohistochemistry, whereas the other was immediately embedded in OCT (Optimal Cutting Temperature) compound (Tissue Tek® O.C.T., Sakura Finetek) and stored at −80°C until use.

The immunohistochemical validation was conducted in the ten pairs of OSCCs and adjacent histologically normal tissues. To expand the number of samples and characterize further the expression pattern of EEF1D, immunohistochemical analysis was also performed in a high-density tissue microarray (TMA #OR601a) containing 47 primary OSCCs and ten adjacent oral normal tissues and in a series of samples characterizing OSCC development (20 cases of oral fibrous hyperplasias with normal epithelium, 48 oral leukoplasias of which 19 were histologically classified as mild epithelial dysplasia, 17 moderate dysplasia and 12 severe dysplasia, and 27 OSCCs).

To confirm the overexpression of EEF1D in OSCCs, fresh samples of OSCC (n=12) and normal oral mucosa (n=12) were used to investigate the expression of EEF1D using qPCR (quantitative PCR). The samples were snap-frozen in liquid nitrogen and kept at −80°C until use. The initial diagnosis was based on clinical findings and confirmed later by histopathological analysis of the specimens.

LCM (laser-capture microdissection) and protein extraction

Cryosections of 8 μm were placed on to membrane slides (Arcturus PEN membrane glass slide), fixed with 75% ethanol for 2 min and stained with haematoxylin. LCM microdissection was performed using the ArcturusXT™ Laser Capture Microdissection System (Life Technologies). Normal surface epithelium from control samples and invasive tumour islands from OSCC samples were microdissected with a mean area of ∼9000 μm2/sample.

Microdissected tissues were treated with 1.6 M urea, following reduction (5 mM DTT for 25 min at 56°C), alkylation (14 mM iodoacetamide for 30 min at room temperature in the dark) and digestion with trypsin (1:50, w/w). The reaction was stopped with 0.4% formic acid and the samples were dried in a vacuum concentrator and reconstituted in 0.1% formic acid.

LC–MS/MS analysis

An aliquot was analysed on an ETD-enabled LTQ Velos Orbitrap mass spectrometer (Thermo Fisher Scientific) connected to a nanoflow LC–MS/MS system by an EASY-nLC system (Proxeon Biosystem) through a Proxeon nanoelectrospray ion source. Peptides were separated by a 2–90% acetonitrile gradient in 0.1% formic acid using an analytical PicoFrit column (20 cm×75 μm internal diameter, 5 μm particle size; New Objective), at a flow of 300 nl/min for 212 min, as described elsewhere for CID by Granato et al. [10].

Bioinformatic analysis

The raw files were processed using the MaxQuant software platform version 1.2.7.4 [11], and the MS/MS spectra were searched using the Andromeda search engine [12] against the UniProt Human Protein Database (released 11 July 2012; 69711 entries). The initial maximal allowed mass tolerance was set to 20 p.p.m. for precursor and then set to 6 p.p.m. in the main search and to 0.5 Da for fragment ions. Enzyme specificity was set to trypsin with a maximum of two missed cleavages. Carbamidomethylation of cysteine (57.021464 Da) was set as a fixed modification, and oxidation of methionine (15.994915 Da) and protein N-terminal acetylation (42.010565 Da) were selected as variable modifications. Both peptide and protein identifications were filtered at a maximum of 1% false discovery rate. For protein quantification, a minimum of two ratio counts for normalized spectral protein intensity [LFQ (label-free quantification)] was set and the ‘requantify’ and ‘match between runs’ with 2 min window functions were enabled.

Bioinformatic analysis was performed using Perseus version 1.2.7.4, which is available in the MaxQuant environment. Reverse and ‘only identified by site’ entries were excluded, and proteins with at least one valid value in the total row were considered for further analysis. The intensity values were log2-transformed. Student's t test was applied for testing of the differences in protein intensities (LFQ) between control and OSCC groups. The protein ratios were calculated from the median of all normalized spectral protein intensities.

The raw files and protein identifications associated with the present study are available for downloading via ftp from the PeptideAtlas data repository at http://www.peptideatlas.org/PASS/PASS00562.

To determine significantly overrepresented GO (gene ontology) terms in the dataset, GO terms were mapped to the list of differentially expressed proteins and enrichment analysis was performed applying the right-sided hypergeometric test corrected by Benjamini–Hochberg within ClueGO version 2.0.5 through the Cytoscape (version 3.0.2) software platform [13].

To evaluate whether the candidate described previously related to cancer or to some biomarker application, a biomarker analysis was performed using the Ingenuity Systems Pathway software (IPA, Ingenuity Systems, http://www.ingenuity.com). The Ingenuity biomarker filter module analysis was performed based on the following criteria: biofluids, ‘all’; disease, ‘cancer’; species, ‘human’; biomarker application, ‘all’.

Immunohistochemistry

Immunohistochemistry was performed using the rabbit anti-EEF1D polyclonal antibody (Sigma–Aldrich) diluted 1:10000 followed by the avidin–biotin–horseradish peroxidase complex method (LSAB System-HRP, Dako). Anti-EEF1D antibody was validated by the Human Protein Atlas project (http://www.proteinatlas.org) in normal and tumour samples from different organs. Negative controls were achieved by omission of the primary antibody. EEF1D expression was assessed with the aid of the Aperio ScanScope CS (Aperio Technologies). The percentage of cytoplasm positivity was calculated and classified into three categories according to their intensity range as weak (from 175 to 205), moderate (from 101 to 174) and strong (from 0 to 100) staining. Each category received an intensity score: 1 for weak, 2 for moderate, and 3 for strong staining.

Co-immunoprecipitation using anti-EEF1D followed by LC–MS/MS and bioinformatic analysis of EEF1D-interacting partners

To identify EEF1D-binding partners, SCC-9 (squamous cell carcinoma) cell lysates were subjected to immunoprecipitation reactions with purified antibody against EEF1D (Sigma–Aldrich) or rabbit IgG (Sigma–Aldrich) for 16 h at 4°C. Two independent experiments were performed. The entire gel lanes were excised and the proteins were digested with trypsin [14].

LC–MS/MS analysis was performed as described above at a flow rate of 300 nl/min over 27 min for each band. Data were submitted to MaxQuant software platform version 1.2.7.4 as described above, including phosphorylation of serine, threonine and tyrosine residues as variable modifications. Using the application software Perseus version 1.2.7.4, the list of phosphosites identified was filtered by minimum localization probability of 0.75. Functional annotation of EEF1D-interacting partners was analysed through the Ingenuity knowledge base (IPA, Ingenuity Systems) and network analysis was performed using pathways and subnetworks from IPA.

Functional experiments

Cell culture

The human OSCC cell line SCC-9 (A.T.C.C., Manassas, VA, U.S.A.) was cultured as recommended in a 1:1 mixture of DMEM (Dulbeccos modified Eagles medium) and Ham's F12 (Invitrogen) supplemented with 10% (v/v) FBS and 400 ng/ml hydrocortisone (Sigma–Aldrich) at 37°C in a humidified atmosphere of 5% CO2.

Stable cells mediating EEF1D silence

SCC-9 cells grown in a 12-well plate at a confluence of 50% were incubated with control or EEF1D shRNA lentiviral particles (MISSION® shRNA Lentiviral Transduction Particles, Sigma–Aldrich). The efficacy of EEF1D knockdown was determined by qPCR and Western blotting.

qPCR

Total RNA was isolated with TRIzol® reagent (Invitrogen) according to the manufacturers protocol. The resulting cDNAs were subjected to qPCR using SYBR® Green PCR master mix (Applied Biosystems) in the StepOnePlus Real Time PCR (Applied Biosystems). Gene expression was determined by the ΔΔCT method and the housekeeping gene PPIA (cyclophilin A) was used as a reference gene for data normalization. Primer pairs for EEF1D were 5′-AGCTCGTCGTCCGGATTG-3′ (forward) and 5′-GTACCACGCCACGCAGACT-3′ (reverse) and for PPIA were 5′-GCTTTGGGTCCAGGAATGG-3′ (forward) and 5′-GTTGTCCACAGTCAGCAATGGT-3′ (reverse).

Western blot analysis

Western blot analysis was performed according to Aragão et al. [14] and Granato et al. [10]. The antibodies used were anti-EEF1D (1:1000 dilution; Sigma–Aldrich), anti-β-actin (1:30000 dilution; Sigma–Aldrich), anti-cyclin D1 (1:200 dilution; Santa Cruz Biotechnology), anti-pRB (1:200 dilution; Santa Cruz Biotechnology), anti-EGFR (epidermal growth factor receptor) (1:5000 dilution; Santa Cruz Biotechnology), anti-caveolin-1 (1:1000 dilution; Santa Cruz Biotechnology), anti-ERK (extracellular-signal-regulated kinase) (1:1000 dilution; Santa Cruz Biotechnology) and anti-pERK (1:1000 dilution; Santa Cruz Biotechnology).

Apoptosis analysis

The protocol used for analysing apoptosis was that of Seguin et al. [15]. The apoptosis index was determined by annexin V–FITC labelling. Apoptosis was analysed on a FACSCalibur flow cytometer (BD Biosciences) equipped with an argon laser and quantified as the number of annexin V–FITC-positive and 7-AAD (7-aminoactinomycin D)-negative cells divided by the total number of cells. A minimum of 10000 events was analysed in each sample. Three independent experiments were performed with triplicates.

BrdU (bromodeoxyuridine)-labelling index

This BrdU labelling assay was performed according to Simabuco et al. [16]. Three independent experiments were performed with five replicates.

Cell cycle analysis

SCC-9-shControl and SCC-9-shEEF1D cells were synchronized for 48 h by serum starvation and released with medium containing 10% (v/v) FBS. The distribution of cells in the cell cycle phases was analysed with the aid of the FACSCalibur flow cytometer equipped with an argon laser and the ModFit LT software (Verity Software House). Three independent experiments were performed with triplicates.

Invasion assay

The invasion assay was performed according to Granato et al. [10]. Three independent experiments were performed with triplicates.

Analysis of EMT markers

The expression of EMT markers E-cadherin and vimentin in SCC-9-shControl and SCC-9-shEEF1D cells was evaluated by qPCR and Western blotting as described above. The quantification of the classical transcription factors related to EMT, SNAIL1, SNAIL2 (SLUG), TWIST, ZEB1 and ZEB2 was also performed by qPCR. The anti-E-cadherin and anti-vimentin antibodies were purchased from BD Biosciences and used at concentrations of 1:2500 and 1:1000 respectively. qPCR primer pairs were: E-cadherin, 5′-ACAGCCCCGCCTTATGATT-3′ (forward) and 5′-TCGGAACCGCTTCCTTCA-3′ (reverse); vimentin, 5′-GGCTCGTCACCTTCGTGAAT-3′ (forward) and 5′-TCAATGTCAAGGGCCATCTTAA-3′ (reverse); SNAIL1, 5′ GCGTGTGCTCGGACCTTCT3 (forward) and 5′-ATCCT-GAGCAGCCGGACTCT-3′ (reverse); SNAIL2, 5′-GGAGCA-TACAGCCCCATCA-3′ (forward) and 5′-TGGTAGCTGGG-CGTGGAA-3′ (reverse); TWIST, 5′-GCGCTGCGGAAGAT-CATC-3′ (forward) and 5′-GCTTGAGGGTCTGAATCTT-GCT-3′ (reverse); ZEB1, 5′-GCTTTCCCATTCTGGCTCC-TA-3′ (forward) and 5′-TCTTGGTCGCCCATTCACA-3′ (reverse); ZEB2, 5′-AAGATAGGTGGCGCGTGTTT-3′ (forward) and 5′-ACTGACGTGTTACGCCTCTTCTAA-3′ (reverse). Three independent experiments were performed.

Adhesion assay

The adhesion assay was performed according to Aragão et al. [14] and Granato et al. [10]. Three independent experiments were performed with four replicates.

Zymography

Zymographic analysis was performed as described in [13]. Three independent experiments were performed.

Statistical analysis

To assess the immunohistochemical expression of EEF1D, a Mann–Whitney test and a Kruskal–Wallis test were used, whereas an unpaired Student's t test was performed to determine the differences between SCC-9-shControl and SCC-9-shEEF1D cells. All analyses were realized in Prism 6 statistics software (GraphPad), and P ≤ 0.05 was considered statistically significant.

RESULTS

Proteomic analysis of OSCC tissues

Proteomic analysis identified 11840 unique peptides at a maximum false discovery rate of less than 1%. In total, 1786 proteins were identified (Supplementary Table S1), with 1499 proteins found to have at least two unique peptides. Among them, 1143 proteins were common between the two groups, and 247 and 388 were exclusive to OSCC and normal oral mucosa (control) respectively (Figure 1A). Normalized spectral protein intensity (LFQ intensity) was used to calculate protein abundance and to quantitatively compare the individual samples. A paired Student's t test, applied to log2 LFQ intensity values, was used to determine the significance of protein differences between normal and tumour samples. A total of 132 proteins showed statistically significant expression (P<0.05, Table 1). Among them, 63 proteins were down-regulated and 69 were up-regulated in tumour tissues. To assess the similarities of the proteomes, unsupervised clustering (Euclidean distance and average linkage), in which samples were grouped on the basis of their expression patterns, was performed. For that, log2 values of differentially expressed proteins were normalized by Z-score and represented as a heat map (Figure 1B). From the ten tumour samples, eight clustered together, indicating high similarity among those samples, despite individual differences.

Differentially expressed proteins in control and OSCC tissues, and network of the functional categories of the differently expressed proteins

Figure 1
Differentially expressed proteins in control and OSCC tissues, and network of the functional categories of the differently expressed proteins

(A) Venn diagram showing the number of proteins identified by MS exclusive or in common between control and OSCC microdissected tissues. (B) Hierarchical clustering of significantly up- and down-regulated proteins in control and OSCC tissues was performed using the Z-score calculation on log2 LFQ intensity values is represented as a heat map, applying the Euclidian distance method and average linkage. Vertical trees indicate the biological samples and corresponding experimental replicates included in the clustering analysis. Missing values were imputed by normal distribution. (C) Genes were grouped according to GO terms of ‘biological process’ and ‘cellular component’ using the ClueGO plugin within Cytoscape. (D) Genes grouped according to GO terms of ‘molecular function’ using the ClueGO plugin within Cytoscape. Colourless nodes indicate that proteins from both categories (up- and down-regulated) were identified within the same GO term. Functional GO terms for up-regulated proteins (red nodes) and down-regulated proteins (green nodes) are shown.

Figure 1
Differentially expressed proteins in control and OSCC tissues, and network of the functional categories of the differently expressed proteins

(A) Venn diagram showing the number of proteins identified by MS exclusive or in common between control and OSCC microdissected tissues. (B) Hierarchical clustering of significantly up- and down-regulated proteins in control and OSCC tissues was performed using the Z-score calculation on log2 LFQ intensity values is represented as a heat map, applying the Euclidian distance method and average linkage. Vertical trees indicate the biological samples and corresponding experimental replicates included in the clustering analysis. Missing values were imputed by normal distribution. (C) Genes were grouped according to GO terms of ‘biological process’ and ‘cellular component’ using the ClueGO plugin within Cytoscape. (D) Genes grouped according to GO terms of ‘molecular function’ using the ClueGO plugin within Cytoscape. Colourless nodes indicate that proteins from both categories (up- and down-regulated) were identified within the same GO term. Functional GO terms for up-regulated proteins (red nodes) and down-regulated proteins (green nodes) are shown.

Table 1
Fold change values for differentially expressed genes between OSCC and healthy tissues in oral cancer patients

EEF1D is highlighted in bold.

Gene nameDescriptionLFQ intensity fold changeP–value
Up-regulated proteins 
HSPH1 Heat-shock protein 105 kDa 1.745 0.0001 
NSF Vesicle-fusing ATPase 0.660 0.0002 
PLEC Plectin 0.812 0.0002 
PKM2 Pyruvate kinase isoenzymes M1/M2 0.606 0.0005 
MYH9 Myosin-9 0.831 0.0005 
EEF1D Eukaryotic translation elongation factor 1δ 0.959 0.0006 
CAP1 Adenylate cyclase-associated protein 1 0.683 0.0008 
FLNA Filamin-A 1.181 0.0008 
MYL12B Myosin regulatory light chain 12B 0.675 0.0010 
SERPINH1 Serpin H1 2.609 0.0011 
S100A2 Protein S100-A2 2.014 0.0012 
PNP Purine nucleoside phosphorylase 1.257 0.0013 
SEPT9 Septin-9 0.699 0.0018 
FSCN1 Fascin 1.551 0.0018 
HSPA8 Heat-shock cognate 71 kDa protein 0.541 0.0021 
ACLY ATP-citrate synthase 0.755 0.0024 
GLRX3 Glutaredoxin-3 0.612 0.0028 
TCP1 T-complex protein 1 subunit α 0.418 0.0040 
TXNDC17 Thioredoxin domain-containing protein 17 1.045 0.0040 
SLC2A1 Solute carrier family 2. facilitated glucose transporter member 1 1.166 0.0042 
TPM3 Tropomyosin α3 chain 0.673 0.0049 
TAGLN2 Transgelin-2 0.593 0.0050 
SLC3A2 4F2 cell-surface antigen heavy chain 1.340 0.0060 
TUBB3 Tubulin β3 chain 1.580 0.0063 
KIF5B Kinesin-1 heavy chain 0.532 0.0069 
EIF4A1 Eukaryotic initiation factor 4A-I 0.355 0.0098 
ITGB4 Integrin β4 1.263 0.0101 
EEF1G Eukaryotic translation elongation factor 1γ 0.495 0.0102 
COPE Coatomer subunit ε 1.023 0.0106 
ATL3 Atlastin-3 0.399 0.0111 
BSG Basigin 1.739 0.0136 
KARS Lysine–tRNA ligase 0.789 0.0138 
CLTC Clathrin heavy chain 1 0.273 0.0140 
MSN Moesin 0.632 0.0146 
BZW1 Basic leucine zipper and W2 domain-containing protein 1 0.810 0.0155 
ACTG1 Actin cytoplasmic 2 0.397 0.0157 
EIF3EIP Eukaryotic translation initiation factor 3 subunit L 0.324 0.0165 
MAP4 Microtubule-associated protein 0.730 0.0165 
PABPC1 Polyadenylate-binding protein 1 0.449 0.0176 
ACTN1 α-Actinin-1 2.214 0.0178 
SEC22B Vesicle-trafficking protein SEC22b 0.822 0.0180 
CCT8 T-complex protein 1 subunit θ 0.367 0.0188 
MARCKS Myristoylated alanine-rich C-kinase substrate 0.512 0.0194 
KTN1 Kinectin 1.225 0.0207 
PFKP 6-Phosphofructokinase type C 1.099 0.0219 
HSP90AA1 Heat-shock protein 90-α 0.795 0.0220 
NSFL1C NSFL1 cofactor p47 0.312 0.0227 
CALR Calreticulin 0.782 0.0245 
LDHA L-Lactate dehydrogenase A chain 0.771 0.0255 
RPL15 60S ribosomal protein L15 0.290 0.0283 
HSPA5 78 kDa glucose-regulated protein 0.812 0.0286 
PFDN2 Prefoldin subunit 2 0.597 0.0295 
AP2A1 AP-2 complex subunit α1 0.586 0.0318 
ITGA6 Integrin α6 1.101 0.0336 
HSP90AB1 Heat-shock protein 90β 0.542 0.0342 
RARS Arginine–tRNA ligase. cytoplasmic 0.418 0.0370 
PSMD12 26S proteasome non-ATPase regulatory subunit 12 0.375 0.0390 
TPD52L2 Tumour protein D54 0.477 0.0401 
TPM4 Tropomyosin α4 chain 0.831 0.0403 
NASP Nuclear autoantigenic sperm protein 1.311 0.0419 
ESYT1 Extended synaptotagmin-1 0.637 0.0420 
IPO5 Importin-5 0.835 0.0422 
FLNB Filamin-B 1.146 0.0429 
TUBA1C Tubulin α1C chain 0.375 0.0432 
CCT6A T-complex protein 1 subunit ζ 0.343 0.0438 
ERO1L ERO1-like protein α 0.828 0.0447 
TUBB4B Tubulin β4B chain 0.342 0.0482 
STOM Erythrocyte band 7 integral membrane protein 0.922 0.0488 
SRSF2 Serine/arginine-rich splicing factor 2 0.646 0.0493 
Down-regulated proteins    
VAT1 Synaptic vesicle membrane protein VAT-1 homologue −0.657 0.0005 
MYH14 Myosin-14 −1.751 0.0007 
GBP6 Guanylate-binding protein 6 −1.670 0.0007 
ALDH3A2 Fatty aldehyde dehydrogenase −1.522 0.0008 
GLUD1 Glutamate dehydrogenase 1, mitochondrial −0.505 0.0010 
MPST 3-Mercaptopyruvate sulfurtransferase −1.199 0.0013 
CDH1 Cadherin-1 −0.963 0.0026 
CTNND1 Catenin δ1 −0.566 0.0031 
CTNNA1 Catenin α1 −0.638 0.0036 
LUM Lumican −1.383 0.0041 
HSPB1 Heat-shock protein β1 −0.774 0.0041 
AHNAK Neuroblast differentiation-associated protein AHNAK −0.593 0.0054 
ECM1 Extracellular matrix protein 1 −1.506 0.0067 
RAB25 Ras-related protein Rab-25 −0.682 0.0069 
PEBP1 Phosphatidylethanolamine-binding protein 1 −1.066 0.0071 
PPL Periplakin −1.183 0.0072 
ALDH9A1 4-Trimethylaminobutyraldehyde dehydrogenase −1.052 0.0076 
S100A14 Protein S100-A14 −1.177 0.0082 
ANXA4 Annexin A4 −0.617 0.0085 
TTLL12 Tubulin–tyrosine ligase-like protein 12 −0.542 0.0087 
PGD 6-Phosphogluconate dehydrogenase, decarboxylating −0.829 0.0095 
A2ML1 α2-Macroglobulin-like protein 1 −1.644 0.0121 
KRT4 Keratin type II cytoskeletal 4 −2.777 0.0125 
ALDH3A1 Aldehyde dehydrogenase, dimeric NADP-preferring −0.744 0.0135 
EPHX1 Epoxide hydrolase 1 −1.625 0.0149 
ALDH2 Aldehyde dehydrogenase, mitochondrial −0.783 0.0149 
CBR1 Carbonyl reductase (NADPH) 1 −0.870 0.0159 
PRDX2 Peroxiredoxin-2 −0.757 0.0160 
ADH7 Alcohol dehydrogenase class 4 μ/σ chain −1.266 0.0162 
ALDH4A1 δ1-Pyrroline-5-carboxylate dehydrogenase, mitochondrial −0.867 0.0163 
LGALS3 Galectin-3 −1.261 0.0182 
CAPN1 Calpain-1 catalytic subunit −0.639 0.0186 
CPNE3 Copine-3 −0.706 0.0191 
ACADVL Very-long-chain specific acyl-CoA dehydrogenase, mitochondrial −0.437 0.0192 
ALDH7A1 α-Aminoadipic semialdehyde dehydrogenase −0.526 0.0196 
EVPL Envoplakin −1.012 0.0209 
DLAT Dihydrolipolylysine-residue acetyltransferase component of pyruvate dehydrogenase complex, mitochondrial −0.490 0.0211 
HSPA1A Heat-shock 70 kDa protein 1A/1B −0.664 0.0216 
DLD Dihydrolipoyl dehydrogenase, mitochondrial −0.955 0.0245 
POF1B Protein POF1B −1.425 0.0260 
H2AFY Core histone macro-H2A.1 −0.356 0.0262 
TECR Trans-2,3-enoyl-CoA reductase −1.291 0.0272 
VPS35 Vacuolar protein sorting-associated protein 35 −0.382 0.0275 
PTGR1 Prostaglandin reductase 1 −1.477 0.0296 
ECHS1 Enoyl-CoA hydratase, mitochondrial −0.612 0.0302 
HK1 Hexokinase-1 −0.476 0.0313 
DSP Desmoplakin −0.788 0.0327 
STK24 Serine/threonine protein kinase 24 −1.379 0.0328 
PRDX6 Peroxiredoxin-6 −0.526 0.0331 
NDUFA9 NADH dehydrogenase (ubiquinone) 1α subcomplex subunit 9, mitochondrial −0.469 0.0332 
CTNNB1 Catenin β1 −0.792 0.0349 
NDUFS3 NADH dehydrogenase (ubiquinone) iron–sulfur protein 3, mitochondrial −1.196 0.0362 
HADH Hydroxyacyl-CoA dehydrogenase, mitochondrial −0.765 0.0374 
GLTP Glycolipid-transfer protein −0.701 0.0386 
VPS26A Vacuolar protein sorting-associated protein 26A −0.576 0.0393 
TGM3 Protein–glutamine γ-glutamyltransferase E −0.730 0.0410 
ATP5A1 ATP synthase subunit α, mitochondrial −0.319 0.0412 
IL1RN Interleukin-1 receptor antagonist protein −0.919 0.0420 
IL18 Interleukin-18 −0.948 0.0443 
NCKAP1 Nck-associated protein 1 −0.453 0.0454 
CD81 CD81 antigen −0.586 0.0467 
CYFIP1 Cytoplasmic FMR1-interacting protein 1 −0.295 0.0493 
HIBCH 3-Hydroxyisobutyryl-CoA hydrolase, mitochondrial −0.694 0.0498 
Gene nameDescriptionLFQ intensity fold changeP–value
Up-regulated proteins 
HSPH1 Heat-shock protein 105 kDa 1.745 0.0001 
NSF Vesicle-fusing ATPase 0.660 0.0002 
PLEC Plectin 0.812 0.0002 
PKM2 Pyruvate kinase isoenzymes M1/M2 0.606 0.0005 
MYH9 Myosin-9 0.831 0.0005 
EEF1D Eukaryotic translation elongation factor 1δ 0.959 0.0006 
CAP1 Adenylate cyclase-associated protein 1 0.683 0.0008 
FLNA Filamin-A 1.181 0.0008 
MYL12B Myosin regulatory light chain 12B 0.675 0.0010 
SERPINH1 Serpin H1 2.609 0.0011 
S100A2 Protein S100-A2 2.014 0.0012 
PNP Purine nucleoside phosphorylase 1.257 0.0013 
SEPT9 Septin-9 0.699 0.0018 
FSCN1 Fascin 1.551 0.0018 
HSPA8 Heat-shock cognate 71 kDa protein 0.541 0.0021 
ACLY ATP-citrate synthase 0.755 0.0024 
GLRX3 Glutaredoxin-3 0.612 0.0028 
TCP1 T-complex protein 1 subunit α 0.418 0.0040 
TXNDC17 Thioredoxin domain-containing protein 17 1.045 0.0040 
SLC2A1 Solute carrier family 2. facilitated glucose transporter member 1 1.166 0.0042 
TPM3 Tropomyosin α3 chain 0.673 0.0049 
TAGLN2 Transgelin-2 0.593 0.0050 
SLC3A2 4F2 cell-surface antigen heavy chain 1.340 0.0060 
TUBB3 Tubulin β3 chain 1.580 0.0063 
KIF5B Kinesin-1 heavy chain 0.532 0.0069 
EIF4A1 Eukaryotic initiation factor 4A-I 0.355 0.0098 
ITGB4 Integrin β4 1.263 0.0101 
EEF1G Eukaryotic translation elongation factor 1γ 0.495 0.0102 
COPE Coatomer subunit ε 1.023 0.0106 
ATL3 Atlastin-3 0.399 0.0111 
BSG Basigin 1.739 0.0136 
KARS Lysine–tRNA ligase 0.789 0.0138 
CLTC Clathrin heavy chain 1 0.273 0.0140 
MSN Moesin 0.632 0.0146 
BZW1 Basic leucine zipper and W2 domain-containing protein 1 0.810 0.0155 
ACTG1 Actin cytoplasmic 2 0.397 0.0157 
EIF3EIP Eukaryotic translation initiation factor 3 subunit L 0.324 0.0165 
MAP4 Microtubule-associated protein 0.730 0.0165 
PABPC1 Polyadenylate-binding protein 1 0.449 0.0176 
ACTN1 α-Actinin-1 2.214 0.0178 
SEC22B Vesicle-trafficking protein SEC22b 0.822 0.0180 
CCT8 T-complex protein 1 subunit θ 0.367 0.0188 
MARCKS Myristoylated alanine-rich C-kinase substrate 0.512 0.0194 
KTN1 Kinectin 1.225 0.0207 
PFKP 6-Phosphofructokinase type C 1.099 0.0219 
HSP90AA1 Heat-shock protein 90-α 0.795 0.0220 
NSFL1C NSFL1 cofactor p47 0.312 0.0227 
CALR Calreticulin 0.782 0.0245 
LDHA L-Lactate dehydrogenase A chain 0.771 0.0255 
RPL15 60S ribosomal protein L15 0.290 0.0283 
HSPA5 78 kDa glucose-regulated protein 0.812 0.0286 
PFDN2 Prefoldin subunit 2 0.597 0.0295 
AP2A1 AP-2 complex subunit α1 0.586 0.0318 
ITGA6 Integrin α6 1.101 0.0336 
HSP90AB1 Heat-shock protein 90β 0.542 0.0342 
RARS Arginine–tRNA ligase. cytoplasmic 0.418 0.0370 
PSMD12 26S proteasome non-ATPase regulatory subunit 12 0.375 0.0390 
TPD52L2 Tumour protein D54 0.477 0.0401 
TPM4 Tropomyosin α4 chain 0.831 0.0403 
NASP Nuclear autoantigenic sperm protein 1.311 0.0419 
ESYT1 Extended synaptotagmin-1 0.637 0.0420 
IPO5 Importin-5 0.835 0.0422 
FLNB Filamin-B 1.146 0.0429 
TUBA1C Tubulin α1C chain 0.375 0.0432 
CCT6A T-complex protein 1 subunit ζ 0.343 0.0438 
ERO1L ERO1-like protein α 0.828 0.0447 
TUBB4B Tubulin β4B chain 0.342 0.0482 
STOM Erythrocyte band 7 integral membrane protein 0.922 0.0488 
SRSF2 Serine/arginine-rich splicing factor 2 0.646 0.0493 
Down-regulated proteins    
VAT1 Synaptic vesicle membrane protein VAT-1 homologue −0.657 0.0005 
MYH14 Myosin-14 −1.751 0.0007 
GBP6 Guanylate-binding protein 6 −1.670 0.0007 
ALDH3A2 Fatty aldehyde dehydrogenase −1.522 0.0008 
GLUD1 Glutamate dehydrogenase 1, mitochondrial −0.505 0.0010 
MPST 3-Mercaptopyruvate sulfurtransferase −1.199 0.0013 
CDH1 Cadherin-1 −0.963 0.0026 
CTNND1 Catenin δ1 −0.566 0.0031 
CTNNA1 Catenin α1 −0.638 0.0036 
LUM Lumican −1.383 0.0041 
HSPB1 Heat-shock protein β1 −0.774 0.0041 
AHNAK Neuroblast differentiation-associated protein AHNAK −0.593 0.0054 
ECM1 Extracellular matrix protein 1 −1.506 0.0067 
RAB25 Ras-related protein Rab-25 −0.682 0.0069 
PEBP1 Phosphatidylethanolamine-binding protein 1 −1.066 0.0071 
PPL Periplakin −1.183 0.0072 
ALDH9A1 4-Trimethylaminobutyraldehyde dehydrogenase −1.052 0.0076 
S100A14 Protein S100-A14 −1.177 0.0082 
ANXA4 Annexin A4 −0.617 0.0085 
TTLL12 Tubulin–tyrosine ligase-like protein 12 −0.542 0.0087 
PGD 6-Phosphogluconate dehydrogenase, decarboxylating −0.829 0.0095 
A2ML1 α2-Macroglobulin-like protein 1 −1.644 0.0121 
KRT4 Keratin type II cytoskeletal 4 −2.777 0.0125 
ALDH3A1 Aldehyde dehydrogenase, dimeric NADP-preferring −0.744 0.0135 
EPHX1 Epoxide hydrolase 1 −1.625 0.0149 
ALDH2 Aldehyde dehydrogenase, mitochondrial −0.783 0.0149 
CBR1 Carbonyl reductase (NADPH) 1 −0.870 0.0159 
PRDX2 Peroxiredoxin-2 −0.757 0.0160 
ADH7 Alcohol dehydrogenase class 4 μ/σ chain −1.266 0.0162 
ALDH4A1 δ1-Pyrroline-5-carboxylate dehydrogenase, mitochondrial −0.867 0.0163 
LGALS3 Galectin-3 −1.261 0.0182 
CAPN1 Calpain-1 catalytic subunit −0.639 0.0186 
CPNE3 Copine-3 −0.706 0.0191 
ACADVL Very-long-chain specific acyl-CoA dehydrogenase, mitochondrial −0.437 0.0192 
ALDH7A1 α-Aminoadipic semialdehyde dehydrogenase −0.526 0.0196 
EVPL Envoplakin −1.012 0.0209 
DLAT Dihydrolipolylysine-residue acetyltransferase component of pyruvate dehydrogenase complex, mitochondrial −0.490 0.0211 
HSPA1A Heat-shock 70 kDa protein 1A/1B −0.664 0.0216 
DLD Dihydrolipoyl dehydrogenase, mitochondrial −0.955 0.0245 
POF1B Protein POF1B −1.425 0.0260 
H2AFY Core histone macro-H2A.1 −0.356 0.0262 
TECR Trans-2,3-enoyl-CoA reductase −1.291 0.0272 
VPS35 Vacuolar protein sorting-associated protein 35 −0.382 0.0275 
PTGR1 Prostaglandin reductase 1 −1.477 0.0296 
ECHS1 Enoyl-CoA hydratase, mitochondrial −0.612 0.0302 
HK1 Hexokinase-1 −0.476 0.0313 
DSP Desmoplakin −0.788 0.0327 
STK24 Serine/threonine protein kinase 24 −1.379 0.0328 
PRDX6 Peroxiredoxin-6 −0.526 0.0331 
NDUFA9 NADH dehydrogenase (ubiquinone) 1α subcomplex subunit 9, mitochondrial −0.469 0.0332 
CTNNB1 Catenin β1 −0.792 0.0349 
NDUFS3 NADH dehydrogenase (ubiquinone) iron–sulfur protein 3, mitochondrial −1.196 0.0362 
HADH Hydroxyacyl-CoA dehydrogenase, mitochondrial −0.765 0.0374 
GLTP Glycolipid-transfer protein −0.701 0.0386 
VPS26A Vacuolar protein sorting-associated protein 26A −0.576 0.0393 
TGM3 Protein–glutamine γ-glutamyltransferase E −0.730 0.0410 
ATP5A1 ATP synthase subunit α, mitochondrial −0.319 0.0412 
IL1RN Interleukin-1 receptor antagonist protein −0.919 0.0420 
IL18 Interleukin-18 −0.948 0.0443 
NCKAP1 Nck-associated protein 1 −0.453 0.0454 
CD81 CD81 antigen −0.586 0.0467 
CYFIP1 Cytoplasmic FMR1-interacting protein 1 −0.295 0.0493 
HIBCH 3-Hydroxyisobutyryl-CoA hydrolase, mitochondrial −0.694 0.0498 

A graphical visualization of the networks of overrepresented GO terms for differentially expressed proteins is shown in Figures 1(C) and 1(D). The most significantly overrepresented GO terms for biological process and cellular component ontologies for the up-regulated proteins were de novo post-translational protein folding (GO:0051084, corrected P-value=2.08×10−10) and stress fibre (GO:0001725, corrected P-value=6.06×10−8) respectively (Figure 1B). It is interesting to note that, among the overrepresented GO terms of the molecular function category, translation factor activity (GO:0008135, corrected P-value=0.008 and GO:0003746, corrected P-value=0.02) was also overrepresented (Figure 1D), giving the idea that protein synthesis-associated events are enriched in OSCCs. In this context, the proteomic data revealed that both EEF1D and EEF1γ are up-regulated in OSCCs. In addition, CTP binding (GO:0002135, corrected P-value=2.19×10−4) was the most significantly overrepresented GO term of the molecular function category identified for the up-regulated protein dataset (Figure 1D).

Enrichment analysis also revealed enrichment of GO terms of molecular function for down-regulated proteins in OSCCs, indicating alterations of mechanisms related to the regulation of epithelial cell migration through the participation of cadherins (GO:0045296, corrected P-value=1.70×10−3) and cell adhesion molecule binding (GO:0050839, corrected P-value=4.64×10−4) events, suggesting a possible modulation of this process in OSCCs (Figure 1D and Supplementary Table S3).

Furthermore, to verify whether the 132 proteins differentially expressed had been described previously as being related to cancer or to some biomarker application, an Ingenuity (IPA) biomarker filter module analysis was performed and 34 proteins, of which 13 were down-regulated and 21 were up-regulated, were found to be associated previously with cancer or biomarker application (Supplementary Table S4).

EEF1D is up-regulated in OSCCs

Among the up-regulated proteins, on the basis of data showing that EEF1D was one of the most significant proteins, unsupervised clustering and GO analyses, we decided to study EEFD1 in more detail, although little is known about this molecule in cancer. To confirm the higher expression of EEF1D in OSCC samples compared with healthy oral mucosa, we first performed immunohistochemical analysis in the ten pairs of samples used in the LC–MS/MS analysis. Immunoreactivity for EEF1D was observed as a cytoplasmic stain restricted to the basal and suprabasal layers in the healthy mucosas, whereas a broad positivity with variable distribution and intensity was found in the OSCC samples (Figure 2A). Immunopositivity was also found in scattered stromal cells, including inflammatory cells, fibroblasts and endothelial cells. In the same samples used in the LC–MS/MS analysis, immunohistochemical analysis confirmed that OSCC samples have a higher expression of EEF1D than healthy mucosa samples (P<0.0001; Figure 2B). A significantly higher immunohistochemical expression of EEF1D in OSCCs was also observed in the second set of tumour and normal control tissues (P<0.001; Figure 2C). Next, to provide insights into the involvement of EEF1D in the development of OSCCs, we investigated its expression in potentially malignant lesions of the oral cavity. As expected, the levels of EEF1D were significantly higher in OSCCs than in fibrous hyperplasias (P<0.0001; Figure 2D). However, only mild dysplasia showed significantly higher EEF1D levels compared with fibrous hyperplasia (P=0.04; Figure 2D). Next, we performed qPCR analysis to verify the mRNA expression levels of EEF1D in fresh tissues. Although higher in OSCC samples, the expression levels of EEF1D did not reach a statistically significant difference when compared with normal oral mucosa samples (P=0.43; Figure 2E).

Validation of the higher levels of EEF1D expression in OSCC

Figure 2
Validation of the higher levels of EEF1D expression in OSCC

(A) EEF1D expression was restricted to the cytoplasm of the basal and suprabasal layers of the normal oral tissue, whereas broad positivity with various intensities was found in the neoplastic cells. Quantification of the positive expression of EEF1D revealed a significantly higher expression in OSCC cells compared with normal oral mucosa cells. (B and C) Immunohistochemical analysis of the ten original pairs of samples used in the LC–MS/MS (B) and in the second set of tumour and normal control tissues distributed in a tissue microarray (C). (D) Quantification of the immunohistochemistry performed in the series of samples that characterize OSCC progression. As expected, the levels of EEF1D were significantly higher in OSCCs than in fibrous hyperplasia, but only mild dysplasia showed significantly higher EEF1D levels compared with fibrous hyperplasia. (E) The amount of EEF1D mRNA was higher in OSCCs than in normal oral mucosa, but did not reach a statistically significant level (P=0.43).

Figure 2
Validation of the higher levels of EEF1D expression in OSCC

(A) EEF1D expression was restricted to the cytoplasm of the basal and suprabasal layers of the normal oral tissue, whereas broad positivity with various intensities was found in the neoplastic cells. Quantification of the positive expression of EEF1D revealed a significantly higher expression in OSCC cells compared with normal oral mucosa cells. (B and C) Immunohistochemical analysis of the ten original pairs of samples used in the LC–MS/MS (B) and in the second set of tumour and normal control tissues distributed in a tissue microarray (C). (D) Quantification of the immunohistochemistry performed in the series of samples that characterize OSCC progression. As expected, the levels of EEF1D were significantly higher in OSCCs than in fibrous hyperplasia, but only mild dysplasia showed significantly higher EEF1D levels compared with fibrous hyperplasia. (E) The amount of EEF1D mRNA was higher in OSCCs than in normal oral mucosa, but did not reach a statistically significant level (P=0.43).

EEF1D participates in networks associated with proliferation and EMT

To explore the role of EEF1D, we identified its protein-interaction partners by immunoprecipitation followed by LC–MS/MS and functional annotation of interacting partners. It was observed that pathways culminating in cyclin D1 and vimentin activation are predicted to be controlled by EEF1D and its partners (Figure 3). To provide proof-of-concept for these data, we performed functional experiments in SCC-9 cells transduced with lentivirus carrying an shRNA against EEF1D transcripts (designated SCC-9-shEEF1D) or a non-specific shRNA control (SCC-9-shControl). After antibiotic selection, EEF1D mRNA and protein levels were quantified by qPCR and Western blotting respectively. The EEF1D shRNA significantly reduced the content of EEF1D at both mRNA and protein levels, whereas the shRNA control had no effect (Figure 4A). As the levels of EEF1D in SCC-9-shControl cells were quite similar to those of parental cells, functional experiments were performed only in SCC-9-shControl and SCC-9-shEEF1D cells. Knockdown of EEF1D resulted in a significant decrease in cell proliferation (P<0.01; Figure 4B). Accordingly, EEF1D down-regulation promoted an increase in the number of cells at G0/G1-phase (P=0.002) and a clear reduction at S-phase (P=0.008) in comparison with control (Figure 4C). As expected, decreased production of cyclin D1 and a slight reduction in the phosphorylation of pRB was observed in SCC-9-shEEF1D cells compared with control cells (Figure 4D). Regarding viability and apoptosis, no differences between SCC-9-shControl and SCC-9-shEEF1D cells were observed (results not shown). On the other hand, SCC-9-shEEF1D cells acquired EMT properties, such as higher levels of expression of the mesenchymal marker vimentin and lower levels of the epithelial marker E-cadherin (P<0.0001; Figures 5A and 5B). Next, we investigated whether the expression of some of the transcription factors identified as EMT regulators may correlate with these phenotypic changes. Using qPCR, we observed a significant increase in SNAIL1 (P<0.0001), ZEB1 (P=0.03) and ZEB2 (P=0.0007; Figure 5C). Although higher, the expression of SNAIL2 and TWIST did not reach a significant level (Figure 5C). The adhesive capacity on surfaces coated with Matrigel (P=0.0445; Figure 5D) was significantly associated with the reduction in EEF1D expression, whereas one of the endpoints of the EMT process, invasion, was significantly induced by EEF1D knockdown (P=0.003; Figure 5E). SCC-9-shEEF1D cells showed higher MMP-2 gelatinolytic activity than SCC-9-shControl cells (Figure 5F). In addition, a significant increase in MMP-2 mRNA was observed in SCC-9-shEEF1D cells in comparison with SCC-9-shControl cells (P<0.0001; Figure 5G). No significant differences in MMP-9 activity and mRNA were observed (Figure 5G).

Network of EEF1D protein complexes evaluated by co-immunoprecipitation followed by LC–MS/MS

Figure 3
Network of EEF1D protein complexes evaluated by co-immunoprecipitation followed by LC–MS/MS

Network analysis revealed that cyclin D1 (CCND1) and vimentin (VIM) may be activated by EEF1D and its partners. Binding partner proteins were uploaded into the IPA and the top biological networks generated a global view. Analysis of predicted pathway activation or inhibition was performed using the molecule activity predictor (MAP) tool and the legend is displayed.

Figure 3
Network of EEF1D protein complexes evaluated by co-immunoprecipitation followed by LC–MS/MS

Network analysis revealed that cyclin D1 (CCND1) and vimentin (VIM) may be activated by EEF1D and its partners. Binding partner proteins were uploaded into the IPA and the top biological networks generated a global view. Analysis of predicted pathway activation or inhibition was performed using the molecule activity predictor (MAP) tool and the legend is displayed.

EEF1D silencing in SCC-9 cells reduces cellular proliferation

Figure 4
EEF1D silencing in SCC-9 cells reduces cellular proliferation

(A) Cells expressing the specific shRNA against EEF1D (SCC-9-shEEF1D) showed a reduction in both mRNA and protein levels when compared with wild-type cells and SCC-9 cells transfected with a control shRNA (SCC-9-shControl). (B) The BrdU incorporation assay showed a statistically significant decrease in proliferation when EEF1D was down-regulated. *P=0.01. (C) Cell cycle analysis by flow cytometry demonstrated a significant retention of SCC-9-shEEF1D cells at G0/G1-phase with a reduction in number of cells in S-phase. *P=0.002, **P=0.008. (D) Western blot reactions of cyclin D1 and pRB. Values below bands represent the densitometric analysis, revealing a marked decrease in cyclin D1 expression in SCC-9-shEEF1D compared with control, and a slight decrease in phosphorylation of RB in SCC-9-shEEF1D.

Figure 4
EEF1D silencing in SCC-9 cells reduces cellular proliferation

(A) Cells expressing the specific shRNA against EEF1D (SCC-9-shEEF1D) showed a reduction in both mRNA and protein levels when compared with wild-type cells and SCC-9 cells transfected with a control shRNA (SCC-9-shControl). (B) The BrdU incorporation assay showed a statistically significant decrease in proliferation when EEF1D was down-regulated. *P=0.01. (C) Cell cycle analysis by flow cytometry demonstrated a significant retention of SCC-9-shEEF1D cells at G0/G1-phase with a reduction in number of cells in S-phase. *P=0.002, **P=0.008. (D) Western blot reactions of cyclin D1 and pRB. Values below bands represent the densitometric analysis, revealing a marked decrease in cyclin D1 expression in SCC-9-shEEF1D compared with control, and a slight decrease in phosphorylation of RB in SCC-9-shEEF1D.

EEF1D is associated with acquisition of EMT properties

Figure 5
EEF1D is associated with acquisition of EMT properties

Down-regulation of EEF1D reduced significantly (P<0.00001) the expression of E-cadherin while inducing vimentin expression as revealed by qPCR (A) and Western blotting (B). (C) SCC-9-shEEF1D cells also increased significantly the expression of EMT transcription factors SNAIL1 (P<0.0001), ZEB1 (P=0.03) and ZEB2 (P=0.0007). (D) Down-regulation of EEF1D altered the adhesive properties SCC-9 cells (P=0.0445) to Matrigel. (E) Invasion of SCC-9 cells was significantly induced after EEF1D knockdown. (F) Zymographic analysis of MMPs secreted by SCC-9-shControl and SCC-9-shEEF1D cells revealed a marked increase in the gelatinolytic activity of MMP-2 (band of ∼72 kDa), whereas the activity of MMP-9 (bands of ∼86–92 kDa) showed no differences. (G) Down-regulation of EEF1D significantly increased the expression of MMP-2 as determined by qPCR (P<0.0001).

Figure 5
EEF1D is associated with acquisition of EMT properties

Down-regulation of EEF1D reduced significantly (P<0.00001) the expression of E-cadherin while inducing vimentin expression as revealed by qPCR (A) and Western blotting (B). (C) SCC-9-shEEF1D cells also increased significantly the expression of EMT transcription factors SNAIL1 (P<0.0001), ZEB1 (P=0.03) and ZEB2 (P=0.0007). (D) Down-regulation of EEF1D altered the adhesive properties SCC-9 cells (P=0.0445) to Matrigel. (E) Invasion of SCC-9 cells was significantly induced after EEF1D knockdown. (F) Zymographic analysis of MMPs secreted by SCC-9-shControl and SCC-9-shEEF1D cells revealed a marked increase in the gelatinolytic activity of MMP-2 (band of ∼72 kDa), whereas the activity of MMP-9 (bands of ∼86–92 kDa) showed no differences. (G) Down-regulation of EEF1D significantly increased the expression of MMP-2 as determined by qPCR (P<0.0001).

DISCUSSION

The present study demonstrates that 132 proteins were significantly expressed in OSCCs by using LCM coupled to MS-based proteomics, of which 63 proteins were down-regulated and 69 were up-regulated in tumour tissues. A total of 98 proteins were not previously found to be associated with cancer or had a biomarker application and, among them, the present study showed for the first time that EEF1D is one of the most significantly overexpressed in OSCCs. Nevertheless, EEF1D overexpression was previously associated with other malignant tumours, including liver carcinoma [6], oesophageal carcinoma [7], non-small-cell lung cancers [8] and meduloblastoma [9]. In addition, previous studies have suggested, on the basis of its canonical and non-canonical functions, that EEF1D can function as a proto-oncogene when overexpressed [17,18]. Indeed, alterations in EEF1D expression, as part of translational cellular machinery, may result in susceptibility to transformation and acquisition of oncogenic properties. Actually, the signalling role of each individual subunit of the EEF1 complex is not clearly demonstrated, but expression of EEF1 subunits was found to be unbalanced in tumours, indicating that components of the EEF1 complex may execute individual and relevant functions in cancer [19].

The mechanisms for the overexpression of the proteins belonging to the EEF1 superfamily in cancer are unknown, but regions containing or surrounding EEFA2 are amplified in breast and ovarian cancers [2022], suggesting that gene amplification is one of the mechanisms related to EEF1A2 overexpression. However, Tomlinson et al. [23] showed no alterations in DNA copy number at the EEF1A2 locus in ovarian cancer. Moreover, this study did not find activating mutations in the EEF1A2-coding sequence in tumours and the methylation status of the EEF1A2 promoter was unrelated to expression level [23]. Down-regulation of miR-663 and miR-744 mediated up-regulation of EEF1A2 and promoted the proliferation of MCF7 breast cancer cells [24]. In a limited number of samples, however, our findings revealed no differences at the mRNA level between normal and tumour samples, suggesting the possibility of the regulation of EEF1D occurring at the post-translational level.

To gain insights into EEF1D signalling, we evaluated EEF1D-interacting partners by co-immunoprecipitation followed by proteomics analysis. This analysis identified 39 proteins in the EEF1D complex, pointing to cyclin D1 and vimentin as the main activated nodes regulated by EEF1D. Indeed, we showed that EEF1D knockdown in OSCC cells significantly decreased cell cycling and proliferation, which were concomitant with a decrease in cyclin D1 expression and RB phosphorylation. In line with this finding, a previous study has demonstrated that phosphorylation of EEF1D at Ser133 plays an essential regulatory role in human cells during mitosis [25], and we have identified phosphorylated or unphosphorylated Ser133 residue in OSCC cells (Supplementary Figure S2). Interestingly, knockdown of EEF1A and its isoform EEF1A2 inhibited proliferation of cells from prostate cancer [26] and plasmacytoma [27]. Together, those results suggest that EEF1D overexpression may induce proliferation of OSCC cells.

EMT, the biological process by which epithelial cells lose cell–cell adhesions, gain expression of mesenchymal proteins, increase the production of extracellular matrix-degrading enzymes and enhance the migratory capacity, allows the tumour cells to acquire invasive and metastatic properties [28]. Although EMT is dependent on a series of cellular events, the reduction of E-cadherin expression in consonance with up-regulation of mesenchymal proteins such as vimentin have been the most common markers of EMT [29]. In addition, EMT-inducing transcription factors, including SNAIL1, SNAIL2, TWIST, ZEB1 and ZEB2, are associated with the repression of E-cadherin and the activation of vimentin, directly or indirectly [30,31]. Accordingly, we have shown that EEF1D knockdown enhances invasion through the direct activation of the EMT process and propose that EEF1D controls migration and invasion of tumour cells via expression, probably among others, of SNAIL1, ZEB1 and ZEB2, which were down-regulated in EEF1D-knockdown cells. In addition, we demonstrated that MMP-2 was up-regulated in EEF1D-knockdown cells and, especially, ZEB1 has also previously been associated with up-regulation of MMP expression [32].

The relationship between EEF1D and E-cadherin was found before on normal mouse mammary epithelial NMuMG cells, where EEF1D and SIP1 down-regulated the transcription of E-cadherin through direct binding to the E-cadherin promoter. However, EEF1D knockdown did not affect expression of mesenchymal markers, including N-cadherin and vimentin [33]. Another subunit of the complex, EEF1γ, regulates vimentin directly by interacting with RNA polymerase II in the vimentin gene promoter and its depletion induces the incorrect compartmentalization of the protein [34]. It should be noted that members of the EEF1 complex share common functions and the gain or loss of expression of one member is likely to be compensated for by the presence of others. Interestingly, the contrasting effects of EEF1D knockdown on proliferation and EMT and invasion reinforce that the phenomenon of proliferation and invasion are uncoupled in cancer [35]. Taken together, we suggest that EEF1D may also have an important role in mediating EMT in OSCC cells. Since EMT shows a tight association with cancer invasion and metastasis, a complete understanding of the molecular mechanisms by which tumour cells evoke this process is relevant for cancer therapy.

In conclusion, the evidence of the present study suggests that the EEF1D signalling pathway leads to modulation of proliferation via cyclin D1 and EMT and invasion, which strongly support a role for this protein in oral carcinogenesis. Furthermore, these findings expand our knowledge of events related to the EEF1 complex.

AUTHOR CONTRIBUTION

Ricardo Coletta and Adriana Paes Leme conceived and designed the experiments. Isadora Flores, Cristiane Salmon and Francisco Nociti conducted microdissection. Isadora Flores, Adriana Paes Leme and Romênia Domingues conducted proteomic work. Isadora Flores, Rebeca Kawahara, Carolina Carnielli, Flavia Winck and Adriana Paes Leme conducted MS data analysis. Márcia Miguel, Daniela Granato, Carolina Macedo, Sami Yokoo, Bárbara Monteiro and Carine Oliveira conducted culture cell experiments. Márcia Miguel, Daniela Granato, Carolina Macedo, Priscila Rodrigues and Alan Santos-Silva performed immunohistochemistry analysis. Márcio Lopes, Alan Santos-Silva, Ricardo Coletta and Adriana Paes Leme contributed reagents/materials/analysis tools. Isadora Flores, Rebeca Kawahara, Flavia Winck, Daniela Granato, Ricardo Coletta and Adriana Paes Leme wrote the paper.

FUNDING

This work was partially supported by the Fundação de Amparo a Pesquisa do Estado de São Paulo-FAPESP, São Paulo, Brazil [grant numbers 2009/54067-3, 2010/19278-0, 2011/22421-2 and 2009/53839-2] and the Conselho Nacional de Desenvolvimento Científico e Tecnológico-CNPq, Brasilia, Brazil [grant numbers 470567/2009-0, 470549/2011-4, 301702/2011-0 and 470268/2013-1 (to A.F.P.L.)] and the Fundação de Amparo à Pesquisa do Estado de São Paulo-FAPESP, São Paulo, Brasil [grant number 2013/01607-6 (to R.D.C.)].

Abbreviations

     
  • BrdU

    bromodeoxyuridine

  •  
  • E-cadherin

    epithelial cadherin

  •  
  • EEF1

    eukaryotic translation elongation factor 1

  •  
  • EGFR

    epidermal growth factor receptor

  •  
  • EMT

    epithelial–mesenchymal transition

  •  
  • ERK

    extracellular-signal-regulated kinase

  •  
  • GO

    gene ontology

  •  
  • LCM

    laser-capture microdissection

  •  
  • LFQ

    label-free quantification

  •  
  • MMP

    matrix metalloproteinase

  •  
  • OSCC

    oral squamous cell carcinoma

  •  
  • qPCR

    quantitative PCR

  •  
  • RB

    retinoblastoma protein

References

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Supplementary data