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  • Research article
  • Open Access

Transcriptomic dissection of tongue squamous cell carcinoma

  • 1,
  • 2,
  • 3, 4,
  • 5,
  • 6,
  • 7, 8,
  • 3,
  • 9 and
  • 1, 8, 10Email author
Contributed equally
BMC Genomics20089:69

  • Received: 15 August 2007
  • Accepted: 06 February 2008
  • Published:



The head and neck/oral squamous cell carcinoma (HNOSCC) is a diverse group of cancers, which develop from many different anatomic sites and are associated with different risk factors and genetic characteristics. The oral tongue squamous cell carcinoma (OTSCC) is one of the most common types of HNOSCC. It is significantly more aggressive than other forms of HNOSCC, in terms of local invasion and spread. In this study, we aim to identify specific transcriptomic signatures that associated with OTSCC.


Genome-wide transcriptomic profiles were obtained for 53 primary OTSCCs and 22 matching normal tissues. Genes that exhibit statistically significant differences in expression between OTSCCs and normal were identified. These include up-regulated genes (MMP1, MMP10, MMP3, MMP12, PTHLH, INHBA, LAMC2, IL8, KRT17, COL1A2, IFI6, ISG15, PLAU, GREM1, MMP9, IFI44, CXCL1), and down-regulated genes (KRT4, MAL, CRNN, SCEL, CRISP3, SPINK5, CLCA4, ADH1B, P11, TGM3, RHCG, PPP1R3C, CEACAM7, HPGD, CFD, ABCA8, CLU, CYP3A5). The expressional difference of IL8 and MMP9 were further validated by real-time quantitative RT-PCR and immunohistochemistry. The Gene Ontology analysis suggested a number of altered biological processes in OTSCCs, including enhancements in phosphate transport, collagen catabolism, I-kappaB kinase/NF-kappaB signaling cascade, extracellular matrix organization and biogenesis, chemotaxis, as well as suppressions of superoxide release, hydrogen peroxide metabolism, cellular response to hydrogen peroxide, keratinization, and keratinocyte differentiation in OTSCCs.


In summary, our study provided a transcriptomic signature for OTSCC that may lead to a diagnosis or screen tool and provide the foundation for further functional validation of these specific candidate genes for OTSCC.


  • Oral Cancer
  • MMP9 Gene
  • Gene Ontology Analysis
  • Tongue Cancer
  • Matching Normal Tissue


Head and neck/oral squamous cell carcinoma (HNOSCC) is a complex disease arising in various organs, including oral cavity, tongue, pharynx, and larynx. Tumors from these different sites have distinct clinical presentations and clinical outcomes, and are associated with different risk factors [1] and genetic characteristics [2]. In this study, we focused on the oral tongue squamous cell carcinomas (OTSCC), one of the most common sites for HNOSCCs. The incidence of OTSCC is actually increasing in young and middle age groups [35]. OTSCC is significantly more aggressive than other forms of HNOSCCs, with a propensity for rapid local invasion and spread [6].

Cancer cells harbor genetic alterations which are translated into unique expression patterns. These patterns may segregate cancer cells from normal tissue of the same origin and serve as a molecular biomarker. Moreover, expression pattern changes may occur far earlier than clinical disease detection. The identification of such patterns has significant translational values for early detection and diagnosis, as well as for identifying novel therapeutic targets. While several recent studies have attempted to identify expression patterns for HNOSCCs [710], to our knowledge, no study has been devoted to identify the unique expression pattern for OTSCC. In this study, we aim to identify the specific transcriptomic/expression patterns that associated with OTSCC.

Results and discussion

Genome-wide gene expression profiles were obtained on 53 OTSCC samples and 22 normal matching samples. Principal Component Analysis (PCA) was performed based on all the probesets utilized in our microarray analysis. Apparent separation between OTSCC and normal groups was observed with a few outliers (Figure 1). Genes showing statistically significant differences in expression level were identified using RMA and a mixed-effects model as described in the Materials and Methods section. A signature gene set that consists of 35 genes was created using stringent statistical criteria (fold change > 4, and FDR values < 0.0001) (Table 1). Comprehensive lists of genes showing statistically significant upregulations (fold change > 2, and FDR values < 0.01) or downregulations in expression in OTSCC were presented in Supplement Table S1 [see additional file 1] and S2 [see additional file 2], respectively.
Figure 1
Figure 1

Principle component analysis. Global gene expression profiles on 53 OTSCC and 22 matching normal samples were obtained as described in Materials and Methods section. Principle Component (PC) analysis was performed based on the expression profiles of samples. The first 3 PCs were plotted. Red: OTSCC, Blue: normal.

Table 1

Signature gene sets for OTSCC



Probe ID

Gene Name


Upregulated genes in OTSCC




matrix metalloproteinase 1





matrix metalloproteinase 10





matrix metalloproteinase 3





matrix metallopeptidase 12





parathyroid hormone-like hormone





inhibin, beta A





laminin, gamma 2





interleukin 8





keratin 17





collagen, type I, alpha 2





interferon, alpha-inducible protein 6





ISG15 ubiquitin-like modifier





plasminogen activator, urokinase





gremlin 1





matrix metallopeptidase 9





interferon-induced protein 44





chemokine (C-X-C motif) ligand 1


Downregulated genes in OTSCC




keratin 4





mal, T-cell differentiation protein















cysteine-rich secretory protein 3





serine protease inhibitor, Kazal type 5





chloride channel, calcium activated, family member 4




209612_s_at 209613_s_at

alcohol dehydrogenase IB (class I), beta polypeptide





26 serine protease





transglutaminase 3





Rhesus blood group, C glycoprotein





protein phosphatase 1, regulatory (inhibitor) subunit 3C





carcinoembryonic antigen-related cell adhesion molecule 7





hydroxyprostaglandin dehydrogenase 15-(NAD)





D component of complement (adipsin)





ATP-binding cassette, sub-family A (ABC1), member 8










cytochrome P450, family 3, subfamily A, polypeptide 5


* The complete lists of upregulated and downregulated transcripts in OTSCCs were presented in Supplement Table S1 and S2, respectively.

In this study, we identified and validated several interesting potential biomarkers for OTSCC diagnosis. One interesting observation is that 5 members of the Matrix Metalloproteinase (MMP) family (MMP1 MMP3, MMP9, MMP10, and MMP12) are among the genes that most significantly upregulated, which may contribute to the aggressive nature of the OTSCC. MMPs are a large family of proteinases which remodel extracellular matrix (ECM) components and play a significant role in tumor development, survival, invasion and metastasis [1113]. Several members of the MMP family have been considered to be important biomarkers for diagnosis and prognosis as well as potential therapeutic targets for many types of cancers, including HNOSCC [14]. Our recent study suggested that up regulation of the MMP9 gene is associated with advanced OTSCC and has predictive value for the identification of lymph node metastasis [15]. Here, our data further suggested that MMP9 is one of the biomarkers for the detection of OTSCC. We also observed 2 chemokines (IL8 and CXCL1) to be among the most significantly upregulated genes in OTSCC. The increase in the protein and mRNA of IL8 gene has been suggested as a biomarker for the early detection of oral cancer [16, 17]. Our data provided independent validation for this biomarker at the disease tissue level, and suggested that the increase of IL8 molecules (mRNA and protein) is due at least in part to the increased expression of gene in the disease tissues. CXCL1, also known as growth-regulated oncogene 1 (Gro-1), is vital for the survival, progression and invasion of several cancer types [18, 19], including oral cancer [20]. Our results here further confirmed the importance of CXCL1 in the tumorgenesis of tongue cancer. Other interesting observations include the up-regulation of KRT17 (associated with invasion and proliferation) and down-regulation of KRT4 (which is associated with squamous cell differentiation), suggesting the potentially distinct roles of KRT genes in tongue SCC development and progression. In addition to those identified biomarkers, our results will also serve as a valuable reference data set for future development and validation of biomarkers for detection, diagnosis and prognosis of tongue cancer.

To test the utility of this 35-gene signature gene set for classifying OTSCC and normal groups, average linkage hierarchical clustering analysis was performed. As illustrated in Figure 2, our results demonstrated that this 35-gene set provides classification power for OTSCC based on gene expression analyses, which misclassified two cases for each of two groups (a 95% overall accuracy rate).
Figure 2
Figure 2

Classification of OTSCC and matching normal using global gene expression analysis. A signature gene set of 35 genes was created based on RMA analysis of the expression profiles on 53 OTSCC and 22 matching normal samples. Hierarchical clustering was performed based on this signature gene set. To removed the effect of baseline and scale in the color image, the data from every transcript was standardized (remove mean, divide by standard deviation) before plotting.

Our analysis demonstrated that OTSCC can be identified based on the gene expression signature. This finding should provide a foundation for the creation of a specific screen tool for OTSCC. One of the major factors accounting for the poor outcome of OTSCC patients is that a great proportion of oral cancers are diagnosed at advanced stages. Patients diagnosed at an early stage of the disease typically have a better chance for cure and functional outcome. Early detection of tongue cancer lesion will greatly improve patient survival and the quality of life. Current clinical diagnosis and histopathologic examinations are usually based on biopsied material, which requires invasive procedures and surgical techniques. The emerging technology of saliva-based diagnosis may provide an alternative strategy for early diagnosis and screening of the subjects at risk [21]. The markers identified here may be suitable for the saliva-based early diagnosis and screening strategy [17, 21]. Additional validation studies will be needed to fully explore this possibility.

To gain a better understanding of the underlying molecular biological processes that dictate the observed expressional pattern alterations in OTSCC development, the Gene Ontology analysis was performed. It was carried out using the GOstats package in the Bioconductor [22] and Gene Ontology Consortium database [23], based on the complete list of 365 differentially expressed transcripts (Supplement Table S1 and S2). Among those 365 genes, 306 were mapped to ENTREZ genes. The gene universe in the analysis consists of 13125 transcripts that were mapped to ENTREZ genes. The Gene Ontology analysis suggested a number of altered biological processes in OTSCC. These include enhancement in phosphate transport, collagen catabolism, I-kappaB kinase/NF-kappaB signaling cascade, extracellular matrix organization and biogenesis, chemotaxis, as well as suppression of superoxide release, hydrogen peroxide metabolism, cellular response to hydrogen peroxide, keratinization, keratinocyte differentiation in OTSCCs (Table 2). The complete lists of enhanced and suppressed biological processes, molecular functions and cellular components in OTSCCs were presented in Supplement Table S3 [see additional file 3] and S4 [see additional file 4], respectively.
Table 2

Selected biological processes that altered in OTSCCs *

GO Name


% Change **




Collagen catabolism



2.7 E-09

Positive regulation of chemotaxis




Phosphate transport



1.5 E-12

Extracellular matrix organization and biogenesis




Positive regulation of I-kappaB kinase/NF-kappaB cascade






Superoxide release







1.1 E-05

Hydrogen peroxide metabolism




Response to hydrogen peroxide




Keratinocyte differentiation



8.0 E-05

* The complete lists of enhanced and suppressed biological processes (BP), molecular functions (MF) and cellular components (CC) in OTSCCs were presented in Supplement Table S3 and S4, respectively.

** The percent of genes exhibiting expressional changes among all genes that constitutes a specific GO entity.

Among the identified alteration in biological activities in OTSCC, the most significantly enhanced are related to the extracellular matrix remodeling (GO:0030574, GO:0030198), I-kappaB kinase/NF-kappaB cascade (GO:0043123) and chemotaxis (GO:0050921), which are known to be related to tumorgenesis and progression of the cancer. One interesting observation is the enhancement in phosphate transport (GO:0006817) in OTSCC. This may be related directly to the enhanced metabolic activity and energy consumption rate in OTSCCs. It has also been suggested that phosphate can act as a signaling molecule on the extracellular signal-regulated kinase (ERK1/2) [24] and adenylate cyclase/cAMP signaling pathways [25], and ultimately affect cell growth. However, the precise role of enhanced phosphate transport in tumorgenesis is largely unclear. The significantly suppressed biological activities, such as superoxide release (GO:0042554), hydrogen peroxide metabolism (GO:0042743), and response to hydrogen peroxide (GO:0042542) are all appeared to be related to the cellular redox state. The effects of redox state in malignancies are somewhat contradictory. In theory, reducing the oxidative stress may prevent DNA degeneration and therefore prevent the development of cancer. However, doing so may also offer increased growth potential to tumor cells and protect them from excess of reactive oxygen species (ROS), which would otherwise lead to apoptosis or necrosis. At the center of this apparent controversy is superoxide dismutase 2 (SOD2), which has been considered as one of the most important antioxidant enzymes. The role of SODs in carcinogenesis has been widely studied but is still rather ambiguous. While the majority of in vitro studies have reported a protective role of SOD2 against tumor progression in cancer cell lines [2630], including oral cancer cell lines [31], the in vivo studies indicate more complicated roles. Increased SOD2 levels have been observed from esophageal, gastric, brain astrocytic and colorectal carcinomas, and often associated with metastasis and poor prognosis [3240]. The status of SOD2 in breast cancer is not clear, with some studies showing an increase [41], while others showing a decrease in SOD2 level [42]. Reduction in SOD2 level has been observed in prostatic carcinomas [43, 44]. Our microarray results indicated a significant increase in expression of SOD2 gene (probset: 215223_s_at; fold change = 2.37; p value = 0.00014; and probeset: 216841_s_at; fold change = 2.24; p value = 0.000197) in OTSCC. These findings are in agreement with the recent observation in oral cancer [45]. Additional studies will be needed to fully understand the role(s) of redox state and SOD2 in OTSCC.

To visualize the changes in gene expression patterns in relationship with the alteration of biological processes and cellular functions in OTSCC, gene expression heat maps for each identified GO entities were generated based on the microarray results of 53 OTSCC and 22 normal samples (Figure 3). Apparent differences in expression patterns can be observed between OTSCC and normal groups for all the altered GO entities. The summarized statistical values on differential expression for each individual gene of GO:0030574 (collagen catabolism) and GO:0050921 (positive regulation of chemotaxis) are presented in Table 3. The complete expressional analysis for all the altered biological processes identified in Table 2 is presented in Supplement Table S5 [see additional file 5].
Figure 3
Figure 3

Expression values of the genes constitute of the altered biological processes in OTSCC. The altered biological processes in OTSCC were identified by Gene Ontology analysis (Table 2) as described in the Materials and Methods section. The expression values of the genes consisting of the altered biological processes were extracted from the RMA based expression index, and individual heat maps were generated for the identified altered biological processes in OTSCC. To removed the effect of baseline and scale in the color image, the data from every transcript was standardized (remove mean, divide by standard deviation) before plotting. To increase the contrast, values lower than the 10th percentile were replaced with the 10th percentile, and values higher than the 95th percentile were replaced with the 95th percentile.

Table 3

Expression values of genes that constitute the collagen catabolism (GO:0030574) and positive regulation of chemotaxis (GO:0050921) processes in OTSCC.

GO term


Gene Symbol

Fold change

FDR level



























































































































Among these differentially expressed genes, some have potential value as diagnosis and prognosis markers, and may be indicative of their respective biological pathways. For example, IL8 is a prototypical chemokine (chemotactic cytokine) and is known for its involvements in the positive regulation of chemotaxis (GO:0050921), which is enhanced in tongue cancers as indicated by our Gene Ontology analysis. IL8 has also been suggested to be a potential biomarker for the early detection of oral cancer [16, 17]. The over-expression of the MMP9 gene has been shown to be associated with progression of oral dysplasia to cancer [14]. Our recent study suggested that over-expression of the MMP9 gene is associated with advanced OTSCC and has predictive value for OTSCC lymph node metastasis [15]. The upregulation of MMP9 in OTSCC is also involved in the enhanced collagen catabolism (GO:0030574), as indicated by our Gene Ontology analysis. The qRT-PCR analyze were performed to validate the expressional differences of the IL8 and MMP9 genes between tongue SCCs and normal matching tissues. As shown in Figure 4A and 4B, the differences of both IL8 and MMP9 mRNA levels are statistically significant (p < .05) between OTSCC and normal tissues. The expression of MMP9 and IL8 was further confirmed by immunohistochemistry tests performed using monoclonal antibody to IL8 and MMP9 on 10 OTSCC cases (Figure 4C and 4D). Strong positive stained SCC cells were observed in 4 cases for IL8 and 8 cases for MMP9. The observation of positive staining of IL8 in OTSCC cells confirmed that SCC cells are one of the major sources of IL8 production at the site of oral cancer lesion.
Figure 4
Figure 4

Elevated expressions of IL8 and MMP9 genes in OTSCC. The qRT-PCR tests were performed with primer sets specific for IL8 gene (A) and MMP9 gene (B) on 25 OTSCC cases and 12 normal matching samples. The p values (Wilcoxon test) were presented. Immunohistochemistry tests were performed using monoclonal antibody to IL8 and MMP9 and detected using peroxidase-antiperoxidase and diaminebenzadine (DAB) on 10 OTSCC cases. Positive stained SCC cells were observed in 4 cases for IL8 and 8 cases for MMP9. Representative images for IL8 (C) and MMP9 (D) were presented.


The HNOSCCs are a diverse group of cancers, that develop from many different anatomic sites and are associated with different risk factors [1] and genetic characteristics [2]. This is the first high-resolution genomic profiling study to our knowledge that has focused on identifying unique expression patterns for tongue cancer (OTSCC). OTSCC is one of the most common types of HNOSCC, and is significantly more aggressive than other forms of HNOSCCs, with a propensity for rapid local invasion and spread [6]. Recent epidemiological studies suggested that the incidence of OTSCC is actually increasing in young and middle age groups [35]. In this study, we utilized a relatively large sample size (53 OTSCCs and 22 normal matching samples), which enabled us to capture a precise picture of the genome-wide expression pattern for this disease. It is possible that the genomic portrait of HNOSCC originating from different anatomic sites may be different. More studies will be needed to address this important question.

In summary, we identified the unique expression pattern for OTSCC. Several interesting candidate genes associated with OTSCC were identified. The Gene Ontology analysis indicated that several biological processes and cellular functions are consistently altered in OTSCC. Our results demonstrate the feasibility of utilizing biomarkers discovered by global expression profiling analyses for the detection and diagnosis of OTSCC. In addition, we also provided a valuable reference dataset for future identification and validation of biomarkers for detection, diagnosis and prognosis of OTSCC.



Microarray data were generated from 26 microdissected OTSCC tissues and 12 matching normal tissue samples. Three additional microarray datasets from 27 OTSCC cases and 10 matching normal control tissues that published previously were either downloaded from GEO database (GSE2280, [46] and GSE3524, [47]) or requested from the authors [7]. Clinical characterizations of these patients are outlined in Table 4. The tumor stages were determined according to the American Joint Committee on Cancer (AJCC) designated classification. This study is approved by Institutional Review Boards at University of California at Los Angeles and at University of Illinois at Chicago.
Table 4

Clinical Characterization of the OTSCC Patients*


OTSCC (n = 53)

Normal (n = 22)










Male (%)




Female (%)



Anatomic Site

Tongue (%)



Pathological T Stage

Stage 4 (%)



Stage 3 (%)



Stage 2 (%)



Stage 1 (%)



Pathological N Stage

Stage 2 (%)



Stage 1 (%)



Stage 0 (%)



* The M stage data is not available.

Tumor procurement, RNA extraction and microarray hybridization

The OTSCC tissues and their matching normal samples were obtained for this study. These tissues were snap frozen. Cancer tissues containing more than 80% tumor cells based on haematoxylin and eosin (H&E) staining and pathological examination were identified and selectively microdissected by a trained pathologist. The total RNA was isolated using RNeasy Mini kit (Qiagen), and quantified by the RiboGreen RNA Quantitation Reagent (Molecular Probes). A total of 150–200 ng of purified total RNA was amplified by a modified T7 RNA amplification protocol as described previously [15, 17]. The Enzo BioArray High Yield RNA Transcript Labeling System (Enzo) was used for labeling the sample prior to hybridization. The biotinylated cRNA (IVT product) was purified using the RNeasy kit (Qiagen). The quantity and purity of the biotinylated cRNA was determined by spectrophotometry and an aliquot of the sample was checked by gel electrophoresis. The samples were hybridized to the Affymetrix Human Genome U133 Plus 2.0 GeneChip arrays according to the Affymetrix protocols. The arrays were scanned with a GeneChip Scanner 3000. The scanned array images were processed with GeneChip Operating software (GCOS), and the CEL files were extracted for further analysis.

Array data analysis and gene ontology analysis

The CEL files from all datasets (newly generated array data from 26 OTSCCs and 12 matching normals, and additional 27 OTSCCs and 10 normals from published studies [7, 46, 47]) were imported into the statistical software R 2.4.1 [48] using Bioconductor [49]. The meta-analysis was performed as described [50]. In brief, the Robust Multi-Array Average (RMA) expression measures [51] were computed after background correction and quantile normalization for each microarray dataset. Then, expression values of the overlapping probesets between U133A and U133 Plus 2.0 arrays were extracted. Probeset-level quantile normalization was performed across all samples to make the effect sizes similar among the four datasets. To visualize the overall expression patterns, we performed Principal Component Analysis (PCA) after removing the normal group mean vector separately from each of the four datasets. Finally, for every probeset, a mixed effects model was applied to identify differential expression. For gene i in sample k of experiment j,
y i j k = μ i + α i j + β i x i j k + ε i j k , α i j ~ N ( 0 , δ i ) , ε i j k ~ N ( 0 , σ i ) x i j k = { 1 , cancer 0 , normal MathType@MTEF@5@5@+=feaagaart1ev2aaatCvAUfKttLearuWrP9MDH5MBPbIqV92AaeXatLxBI9gBaebbnrfifHhDYfgasaacPC6xNi=xI8qiVKYPFjYdHaVhbbf9v8qqaqFr0xc9vqFj0dXdbba91qpepeI8k8fiI+fsY=rqGqVepae9pg0db9vqaiVgFr0xfr=xfr=xc9adbaqaaeGacaGaaiaabeqaaeqabiWaaaGcbaqbaeaabiGaaaqaaiabdMha5naaBaaaleaacqWGPbqAcqWGQbGAcqWGRbWAaeqaaOGaeyypa0dcciGae8hVd02aaSbaaSqaaiabdMgaPbqabaGccqGHRaWkcqWFXoqydaWgaaWcbaGaemyAaKMaemOAaOgabeaakiabgUcaRiab=j7aInaaBaaaleaacqWGPbqAaeqaaOGaemiEaG3aaSbaaSqaaiabdMgaPjabdQgaQjabdUgaRbqabaGccqGHRaWkcqWF1oqzdaWgaaWcbaGaemyAaKMaemOAaOMaem4AaSgabeaakiabcYcaSaqaaiab=f7aHnaaBaaaleaacqWGPbqAcqWGQbGAaeqaaOGaeiOFa4NaemOta4KaeiikaGIaeGimaaJaeiilaWIae8hTdq2aaSbaaSqaaiabdMgaPbqabaGccqGGPaqkcqGGSaalcqWF1oqzdaWgaaWcbaGaemyAaKMaemOAaOMaem4AaSgabeaakiabc6ha+jabd6eaojabcIcaOiabicdaWiabcYcaSiab=n8aZnaaBaaaleaacqWGPbqAaeqaaOGaeiykaKcabaaabaGaemiEaG3aaSbaaSqaaiabdMgaPjabdQgaQjabdUgaRbqabaGccqGH9aqpdaGabaqaauaabaqaciaaaeaacqaIXaqmcqGGSaalaeaacqqGJbWycqqGHbqycqqGUbGBcqqGJbWycqqGLbqzcqqGYbGCaeaacqaIWaamcqGGSaalaeaacqqGUbGBcqqGVbWBcqqGYbGCcqqGTbqBcqqGHbqycqqGSbaBaaaacaGL7baaaaaaaa@8736@

In the model, the random effect α ij is the laboratory effect, and β i is the first-order cancer effect, which is our major focus in the identification of cancer-associated genes. After obtaining the estimates and the p-values of the β i 's of each probeset, we corrected the p-values for false discovery rate (FDR) [52]. We selected genes at the FDR level of 0.01, and with cancer effect size > 1 (> 2 fold change between cancer and normal samples). Functional analysis of the differentially expressed genes was carried out using the GOstats package in Bioconductor [22] based on the Gene Ontology Consortium database [23].

Quantitative RT-PCR(qRT-PCR)

The mRNA levels of interleukin-8 (IL8) and matrix metalloproteinases 9 (MMP9) in OTSCCs and normal tissues were further validated using qRT-PCR as previously described [15, 17]. The RNA was converted to first strand cDNA using MuLV reverse transcriptase (Applied Biosystems) and the quantitative PCR was performed using iQ SYBR Green Supermix (Bio-Rad) in a BIO-RAD iCycler iQ real-time PCR detection system. The primer sets specific for IL8 (Forward: 5'-GAGGGTTGTGGAGAAGTTTTTG-3', Reverse: 5'-CTGGCATCTTCACTGATTCTTG-3') and for MMP9 (Forward: 5'-GCACGACGTCTTCCAGTACC-3', Reverse: 5'-TCAACTCACTCCGGGAACTC-3') were used. All reactions were performed in triplicate. The melting curve analyses were performed to ensure the specificity of the qRT-PCR reactions. The data analysis was performed using the 2-deltadelta Ct method described previously [53], where beta-actin was used as reference gene. The qRT-PCR based gene expression values between two groups were compared by the nonparametric Wilcoxon test.


The expression of IL8 and MMP9 in OTSCCs were further examined using immunohistochemistry tests as previously described [54]. In brief, the OTSCC tissues were processed, embedded, and sectioned at 5 μm. Tissue sections were stained using monoclonal antibody to IL8 (MAB208) (R & D Systems) and MMP9 (ab51203) (Abcam, Inc) and detected using peroxidase-antiperoxidase and diaminebenzadine (DAB) with a Discovery XT automated instrument (Ventana Medical Systems, Inc).




This work was supported in part by NIH PHS grants K22 DE014847, RO3 DE016569, and RO3 CA114688 (to X. Zhou), R01 DE015970 (to D. Wong), P01 CA106451 Project 3 (to L. Mao), and a Foundation de France grant (to S. Temam). We thank Drs. O'Donnell and Muschel at Children's Hospital of Philadelphia and Dr Dermody at University of Medicine and Dentistry of New Jersey for sharing their microarray datasets with us. We thank Mr. Hui Zhou and Ms. Nisa Pungpravat for their excellent technical assistance, and Ms. Katherine Long for editorial assistance.

Authors’ Affiliations

Center for Molecular Biology of Oral Diseases, College of Dentistry, University of Illinois at Chicago, Chicago, IL, USA
Department of Biostatistics, Rollins School of Public Health, Emory University, Atlanta, GA, USA
Department of Thoracic/Head and Neck Medical Oncology, University of Texas, MD Anderson Cancer Center, Houston, TX, USA
Department of Head and Neck Surgery, Institut Gustave-Roussy, Villejuif, France
Department of Otorhinolaryngology – Head and Neck Surgery, University of Pennsylvania Health System, Philadelphia, PA, USA
Department of Oral & Maxillofacial Surgery, the Second Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
Oral Medicine & Diagnostic Sciences, College of Dentistry, University of Illinois at Chicago, Chicago, IL, USA
Graduate College, UIC Cancer Center, University of Illinois at Chicago, Chicago, IL, USA
Dental Research Institute, School of Dentistry, Jonsson Comprehensive Cancer Center, University of California at Los Angeles, Los Angeles, CA, USA
Guanghua School & Research Institute of Stomatology, Sun Yat-sen University, Guangzhou, China


  1. Dobrossy L: Epidemiology of head and neck cancer: magnitude of the problem. Cancer Metastasis Rev. 2005, 24 (1): 9-17. 10.1007/s10555-005-5044-4.PubMedView ArticleGoogle Scholar
  2. Timar J, Csuka O, Remenar E, Repassy G, Kasler M: Progression of head and neck squamous cell cancer. Cancer Metastasis Rev. 2005, 24 (1): 107-127. 10.1007/s10555-005-5051-5.PubMedView ArticleGoogle Scholar
  3. Mackenzie J, Ah-See K, Thakker N, Sloan P, Maran AG, Birch J, Macfarlane GJ: Increasing incidence of oral cancer amongst young persons: what is the aetiology?. Oral Oncol. 2000, 36 (4): 387-389. 10.1016/S1368-8375(00)00009-9.PubMedView ArticleGoogle Scholar
  4. Annertz K, Anderson H, Biorklund A, Moller T, Kantola S, Mork J, Olsen JH, Wennerberg J: Incidence and survival of squamous cell carcinoma of the tongue in Scandinavia, with special reference to young adults. Int J Cancer. 2002, 101 (1): 95-99. 10.1002/ijc.10577.PubMedView ArticleGoogle Scholar
  5. Schantz SP, Yu GP: Head and neck cancer incidence trends in young Americans, 1973-1997, with a special analysis for tongue cancer. Arch Otolaryngol Head Neck Surg. 2002, 128 (3): 268-274.PubMedView ArticleGoogle Scholar
  6. Franceschi D, Gupta R, Spiro RH, Shah JP: Improved survival in the treatment of squamous carcinoma of the oral tongue. Am J Surg. 1993, 166 (4): 360-365. 10.1016/S0002-9610(05)80333-2.PubMedView ArticleGoogle Scholar
  7. Ziober AF, Patel KR, Alawi F, Gimotty P, Weber RS, Feldman MM, Chalian AA, Weinstein GS, Hunt J, Ziober BL: Identification of a gene signature for rapid screening of oral squamous cell carcinoma. Clin Cancer Res. 2006, 12 (20 Pt 1): 5960-5971. 10.1158/1078-0432.CCR-06-0535.PubMedView ArticleGoogle Scholar
  8. Mendez E, Cheng C, Farwell DG, Ricks S, Agoff SN, Futran ND, Weymuller EA, Maronian NC, Zhao LP, Chen C: Transcriptional expression profiles of oral squamous cell carcinomas. Cancer. 2002, 95 (7): 1482-1494. 10.1002/cncr.10875.PubMedView ArticleGoogle Scholar
  9. Arora S, Matta A, Shukla NK, Deo SV, Ralhan R: Identification of differentially expressed genes in oral squamous cell carcinoma. Mol Carcinog. 2005, 42 (2): 97-108. 10.1002/mc.20048.PubMedView ArticleGoogle Scholar
  10. Kuo WP, Hasina R, Ohno-Machado L, Lingen MW: Classification and identification of genes associated with oral cancer based on gene expression profiles. A preliminary study. N Y State Dent J. 2003, 69 (2): 23-26.PubMedGoogle Scholar
  11. Moilanen M, Sorsa T, Stenman M, Nyberg P, Lindy O, Vesterinen J, Paju A, Konttinen YT, Stenman UH, Salo T: Tumor-associated trypsinogen-2 (trypsinogen-2) activates procollagenases (MMP-1, -8, -13) and stromelysin-1 (MMP-3) and degrades type I collagen. Biochemistry. 2003, 42 (18): 5414-5420. 10.1021/bi020582s.PubMedView ArticleGoogle Scholar
  12. Rosenthal EL, Matrisian LM: Matrix metalloproteases in head and neck cancer. Head Neck. 2006, 28 (7): 639-648. 10.1002/hed.20365.PubMedPubMed CentralView ArticleGoogle Scholar
  13. Fingleton B: Matrix metalloproteinases: roles in cancer and metastasis. Front Biosci. 2006, 11: 479-491. 10.2741/1811.PubMedView ArticleGoogle Scholar
  14. Jordan RC, Macabeo-Ong M, Shiboski CH, Dekker N, Ginzinger DG, Wong DT, Schmidt BL: Overexpression of matrix metalloproteinase-1 and -9 mRNA is associated with progression of oral dysplasia to cancer. Clin Cancer Res. 2004, 10 (19): 6460-6465. 10.1158/1078-0432.CCR-04-0656.PubMedView ArticleGoogle Scholar
  15. Zhou X, Temam S, Oh M, Pungpravat N, Huang BL, Mao L, Wong DT: Global expression-based classification of lymph node metastasis and extracapsular spread of oral tongue squamous cell carcinoma. Neoplasia. 2006, 8 (11): 925-932. 10.1593/neo.06430.PubMedPubMed CentralView ArticleGoogle Scholar
  16. St John MA, Li Y, Zhou X, Denny P, Ho CM, Montemagno C, Shi W, Qi F, Wu B, Sinha U, Jordan R, Wolinsky L, Park NH, Liu H, Abemayor E, Wong DT: Interleukin 6 and interleukin 8 as potential biomarkers for oral cavity and oropharyngeal squamous cell carcinoma. Arch Otolaryngol Head Neck Surg. 2004, 130 (8): 929-935. 10.1001/archotol.130.8.929.PubMedView ArticleGoogle Scholar
  17. Li Y, St John MA, Zhou X, Kim Y, Sinha U, Jordan RC, Eisele D, Abemayor E, Elashoff D, Park NH, Wong DT: Salivary transcriptome diagnostics for oral cancer detection. Clin Cancer Res. 2004, 10 (24): 8442-8450. 10.1158/1078-0432.CCR-04-1167.PubMedView ArticleGoogle Scholar
  18. Minn AJ, Gupta GP, Siegel PM, Bos PD, Shu W, Giri DD, Viale A, Olshen AB, Gerald WL, Massague J: Genes that mediate breast cancer metastasis to lung. Nature. 2005, 436 (7050): 518-524. 10.1038/nature03799.PubMedPubMed CentralView ArticleGoogle Scholar
  19. Yang G, Rosen DG, Zhang Z, Bast RC, Mills GB, Colacino JA, Mercado-Uribe I, Liu J: The chemokine growth-regulated oncogene 1 (Gro-1) links RAS signaling to the senescence of stromal fibroblasts and ovarian tumorigenesis. Proc Natl Acad Sci U S A. 2006, 103 (44): 16472-16477. 10.1073/pnas.0605752103.PubMedPubMed CentralView ArticleGoogle Scholar
  20. Shintani S, Ishikawa T, Nonaka T, Li C, Nakashiro K, Wong DT, Hamakawa H: Growth-regulated oncogene-1 expression is associated with angiogenesis and lymph node metastasis in human oral cancer. Oncology. 2004, 66 (4): 316-322. 10.1159/000078333.PubMedView ArticleGoogle Scholar
  21. Wong DT: Towards a simple, saliva-based test for the detection of oral cancer 'oral fluid (saliva), which is the mirror of the body, is a perfect medium to be explored for health and disease surveillance'. Expert Rev Mol Diagn. 2006, 6 (3): 267-272. 10.1586/14737159.6.3.267.PubMedView ArticleGoogle Scholar
  22. Falcon S, Gentleman R: Using GOstats to test gene lists for GO term association. Bioinformatics. 2007, 23 (2): 257-258. 10.1093/bioinformatics/btl567.PubMedView ArticleGoogle Scholar
  23. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, Harris MA, Hill DP, Issel-Tarver L, Kasarskis A, Lewis S, Matese JC, Richardson JE, Ringwald M, Rubin GM, Sherlock G: Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 2000, 25 (1): 25-29. 10.1038/75556.PubMedPubMed CentralView ArticleGoogle Scholar
  24. Beck GR, Knecht N: Osteopontin regulation by inorganic phosphate is ERK1/2-, protein kinase C-, and proteasome-dependent. J Biol Chem. 2003, 278 (43): 41921-41929. 10.1074/jbc.M304470200.PubMedView ArticleGoogle Scholar
  25. Naviglio S, Spina A, Chiosi E, Fusco A, Illiano F, Pagano M, Romano M, Senatore G, Sorrentino A, Sorvillo L, Illiano G: Inorganic phosphate inhibits growth of human osteosarcoma U2OS cells via adenylate cyclase/cAMP pathway. J Cell Biochem. 2006, 98 (6): 1584-1596. 10.1002/jcb.20892.PubMedView ArticleGoogle Scholar
  26. Yan T, Oberley LW, Zhong W, St Clair DK: Manganese-containing superoxide dismutase overexpression causes phenotypic reversion in SV40-transformed human lung fibroblasts. Cancer Res. 1996, 56 (12): 2864-2871.PubMedGoogle Scholar
  27. Zhong W, Oberley LW, Oberley TD, Yan T, Domann FE, St Clair DK: Inhibition of cell growth and sensitization to oxidative damage by overexpression of manganese superoxide dismutase in rat glioma cells. Cell Growth Differ. 1996, 7 (9): 1175-1186.PubMedGoogle Scholar
  28. Church SL, Grant JW, Ridnour LA, Oberley LW, Swanson PE, Meltzer PS, Trent JM: Increased manganese superoxide dismutase expression suppresses the malignant phenotype of human melanoma cells. Proc Natl Acad Sci U S A. 1993, 90 (7): 3113-3117. 10.1073/pnas.90.7.3113.PubMedPubMed CentralView ArticleGoogle Scholar
  29. Cullen JJ, Weydert C, Hinkhouse MM, Ritchie J, Domann FE, Spitz D, Oberley LW: The role of manganese superoxide dismutase in the growth of pancreatic adenocarcinoma. Cancer Res. 2003, 63 (6): 1297-1303.PubMedGoogle Scholar
  30. Ough M, Lewis A, Zhang Y, Hinkhouse MM, Ritchie JM, Oberley LW, Cullen JJ: Inhibition of cell growth by overexpression of manganese superoxide dismutase (MnSOD) in human pancreatic carcinoma. Free Radic Res. 2004, 38 (11): 1223-1233. 10.1080/10715760400017376.PubMedView ArticleGoogle Scholar
  31. Liu R, Oberley TD, Oberley LW: Transfection and expression of MnSOD cDNA decreases tumor malignancy of human oral squamous carcinoma SCC-25 cells. Hum Gene Ther. 1997, 8 (5): 585-595.PubMedView ArticleGoogle Scholar
  32. Izutani R, Asano S, Imano M, Kuroda D, Kato M, Ohyanagi H: Expression of manganese superoxide dismutase in esophageal and gastric cancers. J Gastroenterol. 1998, 33 (6): 816-822. 10.1007/s005350050181.PubMedView ArticleGoogle Scholar
  33. Malafa M, Margenthaler J, Webb B, Neitzel L, Christophersen M: MnSOD expression is increased in metastatic gastric cancer. J Surg Res. 2000, 88 (2): 130-134. 10.1006/jsre.1999.5773.PubMedView ArticleGoogle Scholar
  34. Kim JJ, Chae SW, Hur GC, Cho SJ, Kim MK, Choi J, Nam SY, Kim WH, Yang HK, Lee BL: Manganese superoxide dismutase expression correlates with a poor prognosis in gastric cancer. Pathobiology. 2002, 70 (6): 353-360. 10.1159/000071276.PubMedView ArticleGoogle Scholar
  35. Janssen AM, Bosman CB, Sier CF, Griffioen G, Kubben FJ, Lamers CB, van Krieken JH, van de Velde CJ, Verspaget HW: Superoxide dismutases in relation to the overall survival of colorectal cancer patients. Br J Cancer. 1998, 78 (8): 1051-1057.PubMedPubMed CentralView ArticleGoogle Scholar
  36. Toh Y, Kuninaka S, Oshiro T, Ikeda Y, Nakashima H, Baba H, Kohnoe S, Okamura T, Mori M, Sugimachi K: Overexpression of manganese superoxide dismutase mRNA may correlate with aggressiveness in gastric and colorectal adenocarcinomas. Int J Oncol. 2000, 17 (1): 107-112.PubMedGoogle Scholar
  37. Haapasalo H, Kylaniemi M, Paunul N, Kinnula VL, Soini Y: Expression of antioxidant enzymes in astrocytic brain tumors. Brain Pathol. 2003, 13 (2): 155-164.PubMedView ArticleGoogle Scholar
  38. Korenaga D, Yasuda M, Honda M, Nozoe T, Inutsuka S: MnSOD expression within tumor cells is closely related to mode of invasion in human gastric cancer. Oncol Rep. 2003, 10 (1): 27-30.PubMedGoogle Scholar
  39. Nozoe T, Honda M, Inutsuka S, Yasuda M, Korenaga D: Significance of immunohistochemical expression of manganese superoxide dismutase as a marker of malignant potential in colorectal carcinoma. Oncol Rep. 2003, 10 (1): 39-43.PubMedGoogle Scholar
  40. Qi Y, Chiu JF, Wang L, Kwong DL, He QY: Comparative proteomic analysis of esophageal squamous cell carcinoma. Proteomics. 2005, 5 (11): 2960-2971. 10.1002/pmic.200401175.PubMedView ArticleGoogle Scholar
  41. Bianchi MS, Bianchi NO, Bolzan AD: Superoxide dismutase activity and superoxide dismutase-1 gene methylation in normal and tumoral human breast tissues. Cancer Genet Cytogenet. 1992, 59 (1): 26-29. 10.1016/0165-4608(92)90152-X.PubMedView ArticleGoogle Scholar
  42. Soini Y, Vakkala M, Kahlos K, Paakko P, Kinnula V: MnSOD expression is less frequent in tumour cells of invasive breast carcinomas than in in situ carcinomas or non-neoplastic breast epithelial cells. J Pathol. 2001, 195 (2): 156-162. 10.1002/path.946.PubMedView ArticleGoogle Scholar
  43. Baker AM, Oberley LW, Cohen MB: Expression of antioxidant enzymes in human prostatic adenocarcinoma. Prostate. 1997, 32 (4): 229-233. 10.1002/(SICI)1097-0045(19970901)32:4<229::AID-PROS1>3.0.CO;2-E.PubMedView ArticleGoogle Scholar
  44. Bostwick DG, Alexander EE, Singh R, Shan A, Qian J, Santella RM, Oberley LW, Yan T, Zhong W, Jiang X, Oberley TD: Antioxidant enzyme expression and reactive oxygen species damage in prostatic intraepithelial neoplasia and cancer. Cancer. 2000, 89 (1): 123-134. 10.1002/1097-0142(20000701)89:1<123::AID-CNCR17>3.0.CO;2-9.PubMedView ArticleGoogle Scholar
  45. Lo WY, Tsai MH, Tsai Y, Hua CH, Tsai FJ, Huang SY, Tsai CH, Lai CC: Identification of over-expressed proteins in oral squamous cell carcinoma (OSCC) patients by clinical proteomic analysis. Clin Chim Acta. 2007, 376 (1-2): 101-107. 10.1016/j.cca.2006.06.030.PubMedView ArticleGoogle Scholar
  46. O'Donnell RK, Kupferman M, Wei SJ, Singhal S, Weber R, O'Malley B, Cheng Y, Putt M, Feldman M, Ziober B, Muschel RJ: Gene expression signature predicts lymphatic metastasis in squamous cell carcinoma of the oral cavity. Oncogene. 2005, 24 (7): 1244-1251. 10.1038/sj.onc.1208285.PubMedView ArticleGoogle Scholar
  47. Toruner GA, Ulger C, Alkan M, Galante AT, Rinaggio J, Wilk R, Tian B, Soteropoulos P, Hameed MR, Schwalb MN, Dermody JJ: Association between gene expression profile and tumor invasion in oral squamous cell carcinoma. Cancer Genet Cytogenet. 2004, 154 (1): 27-35. 10.1016/j.cancergencyto.2004.01.026.PubMedView ArticleGoogle Scholar
  48. R_Development_Core_Team: R: A language and environment for statistical computing. R Foundation for Statistical Computing. 2006, Vienna, AustriaGoogle Scholar
  49. Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, Dudoit S, Ellis B, Gautier L, Ge Y, Gentry J, Hornik K, Hothorn T, Huber W, Iacus S, Irizarry R, Leisch F, Li C, Maechler M, Rossini AJ, Sawitzki G, Smith C, Smyth G, Tierney L, Yang JY, Zhang J: Bioconductor: open software development for computational biology and bioinformatics. Genome Biol. 2004, 5 (10): R80-10.1186/gb-2004-5-10-r80.PubMedPubMed CentralView ArticleGoogle Scholar
  50. Yu T, Ye H, Chen Z, Ziober BL, Zhou X: Dimension reduction and mixed-effects model for microarray meta-analysis of cancer. Front Biosci. 2008, 13: 2714-2720. 10.2741/2878.PubMedView ArticleGoogle Scholar
  51. Irizarry RA, Bolstad BM, Collin F, Cope LM, Hobbs B, Speed TP: Summaries of Affymetrix GeneChip probe level data. Nucleic Acids Res. 2003, 31 (4): e15-10.1093/nar/gng015.PubMedPubMed CentralView ArticleGoogle Scholar
  52. Benjamini Y, Hochberg Y: Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society Series B. 1995, 57: 289-300.Google Scholar
  53. Livak KJ, Schmittgen TD: Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods. 2001, 25 (4): 402-408. 10.1006/meth.2001.1262.PubMedView ArticleGoogle Scholar
  54. Peng H, Shintani S, Kim Y, Wong DT: Loss of p12CDK2-AP1 expression in human oral squamous cell carcinoma with disrupted transforming growth factor-beta-Smad signaling pathway. Neoplasia. 2006, 8 (12): 1028-1036. 10.1593/neo.06580.PubMedPubMed CentralView ArticleGoogle Scholar


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