archives of oral biology 59 (2014) 1155–1163
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The role of components of the extracellular matrix and inflammation on oral squamous cell carcinoma metastasis Tuncay Tanis a, Zeynep Birsu Cincin b, Bilge Gokcen-Rohlig c, Elif Sinem Bireller b, Murat Ulusan a, Cem Rustu Tanyel c, Bedia Cakmakoglu b,* a
Department of Otorhinolaryngology, Faculty of Medicine, Istanbul University, Istanbul, Turkey Department of Molecular Medicine, Institute of Experimental Medical Research, Istanbul University, Istanbul, Turkey c Department of Maxillofacial Prosthodontics, Faculty of Dentistry, Istanbul University, Istanbul, Turkey b
article info
abstract
Article history:
Objectives: Oral squamous cell carcinoma (OSCC) accounts for about 90% of malignant oral
Accepted 13 July 2014
lesions, and is identified as the most frequently occurring malignant tumour of oral
Keywords:
Turkish OSCC patients.
OSCC
Materials and methods: We performed whole genome expression profiling array on an
structures. We aimed to investigate the genes and pathways related with metastasis on
Microarray
Illumina platform. A total of 24 samples with 12 OSCC and 12-paired controls that had
Metastasis
no tumour were included in the study. Hierarchic clustering and heat map were used for data visualisation and p-values assessed to identify differentially expressed genes. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Ingenuity Pathway Systems (IPA) analysis were performed to consider biologic meaning of differential expression of the genes between tumour and control groups. Results: We identified 790 probe sets, corresponding to 648 genes that were effective in separating invasive and metastatic OSCC. Consequently, we found statistically relevant expression results on extracellular matrix members on MMPs such as MMP3, MMP10, MMP1 and MMP9; on laminin such as LAMC2, LAMA3 and LAMB3; several genes in the collagen family; and also on chemokines from the inflammation process. Conclusion: Statistically relevant expression changes for MMPs, laminins, collagens, and chemokines, which are components of the extracellular matrix and inflammation process, may be considered as a molecular biomarker for early prediction. Further studies are necessary to determine and understand the molecular mechanisms that underlie OSCC metastasis. # 2014 Elsevier Ltd. All rights reserved.
* Corresponding author at: Department of Molecular Medicine, Institute of Experimental Medical Research, Istanbul University, Capa, Istanbul, Turkey. Tel.: +90 212 4142000/33305; fax: +90 212 5324171. E-mail address: [email protected] (B. Cakmakoglu). http://dx.doi.org/10.1016/j.archoralbio.2014.07.005 0003–9969/# 2014 Elsevier Ltd. All rights reserved.
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1.
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Introduction
Oral squamous cell carcinoma (OSCC) is the sixth most common type of cancer with increasing rates worldwide.1 OSCC accounts for about 90% of oral cancers and can occur at different anatomic sites of the oral cavity such as the tongue, oropharynx, lip, mouth floor, gingiva, hard palate, and buccal mucosa.2–5 Tobacco smoking and alcohol exposure are accepted as the major risk factors for transformation through oral epithelia to cancer.6 Despite the significant improvements in surgical techniques and chemotherapy, the five-year survival rate for OSCC patients is still approximately 30–50% and has not changed over the past decades.7–10 OSCC has become more important not only because the survival rate is about 30–50% but also diagnosis is one of the most difficult cancer types in early stage. Owing to the relationship between tumour development and progression with malignant cells, it is well known that the extracellular matrix (ECM), including its components, has some straightforward roles.11,12 It is important to protect the ECM because it has an impact on a series of processes in important aspects of metabolism such as cell adhesion, migration, proliferation, differentiation, and gene expression. The structure of extracellular matrix is protected by some proteases that include matrix metalloproteinases (MMPs), laminine, members of the collagen family, and also chemokines, a sub-family of inflammation regulators, the latter being the focus of our study.13,14 It has been reported that tumour invasion and metastases are related to degradation of the ECM in several cancer types, including OSCC.15–19 Even though MMPs are also known for their capability of regulating apoptosis, bone remodelling and angiogenesis, they have a role in highly complex processes that is not yet clear.20,21 Collagen, which provides structural support, is the other main component of the ECM. In some pathological conditions like cancer, collagen activity becomes abnormal thus affecting its role in the ECM.22 Laminin is an extracellular glycoproteins that is considered as a molecular marker for detecting the basement membrane during tumorigenesis due to degradation of basement membrane that occurs in most tumours.23– 25 Moreover, inflammatory processes are also reported to be related with several types of cancer.26 Chemokines are a member of the inflammatory-regulator family that has important roles in inflammation processes, angiogenesis, and metastasis.27,28 Because of the relationship between the ECM and OSCC, molecular-based studies are necessary to find multiple markers. In this respect, we aimed to simultaneously show metastatic genes and pathways for components of the ECM related with metastasis: MMPs, laminins, collagens and chemokines, in Turkish patients with OSCC. We believe that the data from this important study might ultimately lead to future research and would be helpful to find new biomarkers for early prediction of OSCC using components of the extracellular matrix.
2.
Materials and methods
2.1.
Tissue collection
Samples of squamous cell carcinoma were collected through the Department of Otolaryngology and the Department of Oral and Maxillofacial Surgery of Istanbul University after obtaining written informed consent from the participants and approval of Istanbul University’s Ethics Committee. Tumour samples were obtained from patients with OSCC at the time of resection or biopsy prior to chemo/radiation therapy. Paired control nontumor tissues from patients were collected from the clinically unaffected side and were histologically normal. The patients and controls were matched for smoking status. Tumours were pathologically staged according to the guidelines issued by the American Joint Committee on Cancer. Tissue samples were soaked in RNAlaterTM (Ambion, Austin, TX) immediately after surgical removal and transferred to the Department of Molecular Medicine of Istanbul University for long-term storage at 80 8C prior to use.
2.2.
RNA extraction and target sample generation
Tissue samples were homogenised and total RNA was extracted using High Pure Tissue RNA Isolation Kit (Roche, Germany) according to the manufacturer’s instructions. Quality and quantity of RNA was measured by using Nanodrop (Thermo Scientific, USA). RNA integrity was evaluated by microfluidics analysis using the Agilent1 2100 bioanalyzer (Santaclara, CA) and an RNA LabChip1 Kit. The Illumina1 TotalPrepTM RNA Amplification Kit was used for generating biotinylated, amplified RNA for hybridisation with Illumina Sentrix1. The procedure consisted of reverse transcription with an oligo(dT) primer bearing a T7 promoter using ArrayScript,TM a reverse transcriptase (RT) engineered to produce higher yields of first strand cDNA than wild-type enzymes. The cDNA then underwent second strand synthesis and cleanup to become a template for in vitro transcription with T7 RNA Polymerase. To maximise cRNA yield, Ambion1 MEGAscript1 (Invitrogen, Life Technologies) in vitro transcription (IVT) technology along with biotin-UTP (provided in the kit) was used to generate hundreds to thousands of biotinylated, antisense RNA copies of each mRNA in a sample.
2.3.
Array hybridisation
cRNA (500 ng) was fragmented and added to a hybridisation mixture. Expression profiles were created using Illumina Human HT-12V4 microchip, targeting more than 47,000 probes derived from the National Center for Biotechnology Information Reference Sequence (NCBI) RefSeq Release 38 (November 7, 2009) and other sources. Hybridisation procedures were performed using the procedures described by Illumina. Arrays were scanned on iScan System (Illumina).
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2.4.
Data analysis
Data collected by iScan were carried out background correction for each sample by GenomeStudio Gene Expression (GX) Module. A normalised signal for each transcript was then obtained through quantile normalisation. The signal intensities corresponding to gene expression levels of individual arrays were background corrected and imported into text files using the Illumina GenomeStudio. Hierarchical cluster analysis was applied to datasets to evaluate the ‘‘proximity’’ between the time points. For data visualisation, hierarchical clusters were constructed with the statistically significant ( p < 0.05) genes. Computing a p-value for each gene assessed the statistical significance of the differential expression of genes. Genes were considered differentially expressed when logarithmic gene expression ratios in three independent hybridisations were more than 1.5-fold difference in expression level, and when the p-values were less than 0.05. Using the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Ingenuity Pathway Analysis (IPA; Ingenuity Systems, Redwood City, CA), network, biofunction, gene ontology and pathway analyses were performed to consider biological meaning of differential expression of genes between the tumour and control groups.
3.
Results
We compared the gene expression profiles obtained from metastatic primary squamous cell carcinomas of the oral cavity (OSCC) with paired control tissue samples. Tissue samples were collected from 12 patients who underwent surgical treatment for OSCC, and 12 paired controls from clinically unaffected sides. Clinical characteristics of all patients are outlined in Table 1. Gene expression profiling of samples was determined with Illumina HT-12V4 arrays. After background correction, quantile normalisation was applied to probe sets. Boxplot (Fig. 1) and density plots (Fig. 2) before and after normalisation was drawn, respectively. Tumour and control samples were
Table 1 – Characteristics of the OSCC patients, with TNM staging. Patient code T1 T2 T3 T4 T5 T6 T7 T8 T9 T10 T11 T12
Primary site Floor of mouth Tongue Floor of mouth Tongue Gingiva Hard palate Upper lip Buccal mucosa Buccal mucosa Tongue Tongue Gingiva
Sex
TNM staging
M M M M F F M M F M F F
T4 T4 T4 T1 T4 T3 T1 T2 T2 T2 T2 T2
N2 MX N2MX N0 MX N1 MX N0 MX N0 MX N0 MX N0 MX N2 MX N0 MX N0 MX N2 MX
grouped to perform differential expression analysis with obtained data from normalisation. We identified 790 probes (648 genes) as being differentially expressed between the controls and cases that had at least a 1.5-fold difference or greater gene expression. To investigate whether different experimental groups could be classified with significant genes, the hierarchic clustering method was applied to the differentially expressed genes set. As expected, with the exception of one patient identified as patient 11 who exhibited features of both groups, the heat map showed that all metastatic primary tumours clustered together and all control tissues clustered together (Fig. 3). IPA software was used to determine how the expression pattern-clustered differentially expressed genes interconnected with each other to facilitate cellular biofunctions and signalling. According to the core analysis of all differentially expressed genes (n = 648) using the IPA, the most significant top 5 cellular functions were cellular movement, cell death and survival, cellular growth and proliferation, cellular morphology, and cell-to-cell interaction (Table 2). Annotation using Ingenuity Systems was also performed in an attempt to have a molecular understanding or models to explain functionality. The results showed that cancer, immunological disease and
Fig. 1 – Box-plots before (a) and after (b) normalisation (tumour and control samples were grouped to perform differential expression analysis with obtained data from normalisation).
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Fig. 2 – Density plots before (a) and after (b) normalisation (tumour and control samples were grouped to perform differential expression analysis with obtained data from normalisation).
Fig. 3 – Heatmap graphic. All the relevant genes are grouped by hierarchical clustering based on the expression regulation (log 2 ratios) across all the samples (12 tumour and 12 control). The colour red on the heat map indicates down-regulation during infection, while the colour green indicates up-regulation. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
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Table 2 – Molecular and cellular biofunction analyses of differentially expressed genes. Molecular and cellular functions Cellular movement Cell morphology Cell death and survival Cellular growth and proliferation Cell-to-cell signalling ˇ andˇ interaction
p-value 3.25E 6.90E 9.25E 1.08E
22 13 13 12
to to to to
1.29E 1.34E 1.07E 1.14E
# Molecules 04 04 04 04
189 116 225 236
1.31E 11 to 1.25E 04
159
inflammatory response were the top three diseases associated with the differentially expressed genes, respectively (Table 3). Differentially expressed molecules in relation to cancer clustered into GO (Gene Ontology) analysis by IPA. We have found that metastasis, invasion and neoplasm of cells were the most significant functions related with OSCC (Fig. 4). Based on all the identified genes, new and expanded pathway maps and connections and specific gene-gene interactions were inferred, functionally analysed, and used to build on the existing pathway knowledge base using IPA. To generate networks in this study, the knowledge base was queried for interactions between the identified genes and all other genes stored in the
Table 3 – Diseases and disorders analyses of differentially expressed genes. Diseases and disorders Cancer Immunological disease Inflammatory response
p-value
# Molecules
1.59E 13 to 5.62E 05 1.79E 13 to 5.02E 05 7.53E 13 to 5.71E 05
378 133 160
database. Five networks were found to be significant in OSCC (Table 4). The network with the highest score (Network 1, score = 32) was generated with 24 identified genes (Fig. 5). According to microarray analysis in 648 genes, we found that expressions of MMP3, MMP10, MMP1 and MMP9 genes significantly increased in OSCC patients compared with controls. We found that LAMC2 (laminin, gamma 2), LAMA3 (laminin, alpha 3) and LAMB3 (laminin, beta 3) were differentially expressed in OSCC tissues more than controls. Our results demonstrated that in the collagen family some gene expressions were significantly increased in OSCC such as COL1A1, COL1A2, COL3A1, COL4A1, COL4A2, COL4A6, COL5A1, COL5A2, COL6A3, COL7A1, COL8A1, COL12A1 and COL16A1. Moreover, we also found that CXCL1, CXCL9, CXCL10 and CXCL13 expression levels were significantly increased in OSCC tissue samples while CCL14 and CCL 15 were decreased.
Fig. 4 – Gene ontology analysis (invasion and neoplasm of cells related with OSCC).
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Table 4 – Network analysis. Differantially expressed genes between OSCC and uninvolved buccal mucosa with GO analysis. Genes ingenuity networks
Function
Akt, ANTXR1, ANTXR2, ATP1B2, BMP1, COL1A2, COL3A1, COL4A1, COL4A2, COL4A6, COL5A2, COL6A3, COL7A1, COL8A1, Collagen type III, Collagen type IV, Collagen type V, Collagen type VI, Collagen type VII, FAM3B, FOXF2, FUT6, gelatinase, LAMB3, LAMC1, LAMC2, Laminin1, Laminin, MMP10, Na-k-atpase, Pdgf Ab, PLOD3, PTK6, SERPINH1, WISP1 26s Proteasome, ANO1, ATP5D, ATP5J, BASP1, Calcineurin protein(s), Calmodulin, CBLB, Ck2, CLEC3B, COL12A1, CTSL1, cytochrome C, DBI, DLGAP5, EIF5A2, oestrogen receptor, FAM176A, GPD1, Hsp27, Hsp90, ID3, IFI16, IGF2R, MAPT, NFIX, NOL3, Nuclear factor 1, PLEK2, RAB30, SAT1, Smad, SP100, SUSD4, TGFBR3 CALU, DDAH2, EIF2AK2, FNDC3B, IFI27, IFI35, IFI44, IFIT1, IFIT3, IFITM1, IFITM2, IFN type 1, Ikb, Ikk (family), IL-1R, Il8r, Interferon-a Induced, IRF7, IRF9, ISGF3, Jnk, MAD2L2, NfkB1-RelA, PARP12, PPM1L, QARS, REC8, RHOC, SNAI2, STAT2, Stat1-Stat2, STK3, Tnf receptor, TNFSF10, TRIP13 Adaptor protein 1, AGTRAP, CD80/CD86, CDC25B, CDCA5, CDKN3, CTLA4, DUSP6, EGFL6, ERK, EYA2, Fc receptor, FCER1G, Fcgr3, FOXM1, HAVCR2, ICOS, INTERLEUKIN, KIR, LAMP3, LIMA1, MAL, MKNK2, NMDA Receptor, OAS2, Oas, PEBP1, PTPase, PTPRK, SH2B3, SIGLEC7, SIRPA, Sod, TH2 Cytokine, tyrosine kinase APC (complex), ASAP1, AURKA, BIRC2, BIRC5, CCNE1, Cdc2, CDC20, CDC45, Cdk, CDR2, CKAP5, CKS1B, Cyclin A, Cyclin B, Cyclin E, DIAPH2, DPT, E2f, ELF4, ERBB, Histone H1, IL17RC, LYVE1, MELK, MPC1, NDRG2, Rb, RPA3, RPA, Timp, TRIP10, UBE2C, Vegf, XAF1
4.
Discussion
The goal of our study was to find possible biomarkers from the ECM that could be easily used for the testing of biopsies or surgical margins for early diagnosis and
Score
Connective tissue disorders, dermatological diseases and conditions, gastrointestinal disease
32
Connective tissue development and function, embryonic development, organ development
30
Infectious disease, dermatological diseases and conditions, immunological disease
29
Cell-to-cell signalling and interaction, inflammatory response, haematological system development and function
28
Cell cycle, cancer, reproductive system disease
27
prognosis to possibly improve survival rates. As such, we have confirmed the distribution of component ECM such as MMPs, laminins, collagens, and chemokines in OCSS in the same study. This is the first study by doing 640 genes in microarray analysis on OSCC in Turkish patient.
Fig. 5 – Network analyses (the network with the highest score (Network 1, score = 32) was generated with 24 identified genes).
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In microarray analysis, we found that expressions of MMP3, MMP10, MMP1 and MMP9 genes were significantly increased in OSCC compared with controls. Matrix metalloproteinase are zinc-dependent endopeptidases that are shown to have an important role in tumour invasion and the metastasis process in OSCC.29–35 MMP1 is shown to act as a type I collagen proteolytic enzyme for degrading collagen I to gelatin in OSCC.36 MMP2 and MMP9 were reported to play gelatinases role to complete degradation process of collagen I to gelatin.37 Moreover, studies have indicated that MMP1 and MMP9 have important roles in the OSCC invasion process.37,38 Overexpression of MMP3 was also reported to be related with invasive and metastatic OSCC.39 Laminins are included in extracellular matrix glycoproteins found in all basement membranes and have important roles in cellular processes such as adhesion, differentiation, migration and metastasis.40 Laminin involves three chains (alpha, beta, gamma) and each chain is encoded by a distinct gene. We found that LAMC2 (laminin, gamma 2), LAMA3 (laminin, alpha 3) and LAMB3 (laminin, beta 3) were differentially expressed in OSCC tissues. When laminin binds to cell surface receptors, tumour cells release collagenase IV that degrades type IV collagen to facilitate cell migration.41 Increasing laminin levels have been associated with different carcinomas.42–45 Chen and colleagues reported that LAMC2 was associated with OSCC, which is in accordance with our results.28 The collagen family is reported to play structural roles in cellular assembly and organisation via its receptors.46 Collagens are extracellular matrix proteins that allow the basement membrane to integrate with other molecules.47 Our results demonstrated that expressions of COL1A1, COL1A2, COL3A1, COL4A1, COL4A2, COL4A6, COL5A1, COL5A2, COL6A3, COL7A1, COL8A1, COL12A1 and COL16A1 genes were significantly increased in OSCC. Degradation of these proteins could facilitate tumour cell migration by spreading tumour cells. Previous studies of over-expression of genes have shown that the balance between degradation and overproduction plays an important role in OSCC development.28–45 Chemokines have been demonstrated as mediators of inflammation that play important roles including regulation, leukocyte activation, and recruitment in areas of inflammation via their interactions with chemokine receptors.46 Experimental and clinical data have suggested that inflammatory mechanisms can contribute to OSCC tumour development.47 We found that CXCL1, CXCL9, CXCL10 and CXCL13 expression levels were significantly increased in OSCC tissue samples. Shintani et al.48 showed that high levels of CXCL1 expression were related with tumour angiogenesis and metastasis mechanisms. Chang et al.49 also indicated that OSCC samples had high expression and serum levels of CXCL9 compared with controls. Pandruvada et al.50 presented that increased expression levels of CXCL13 were associated with osteolysis in OSCC and this situation could be in correlation with MMP-9 production, which marks the degree of osteoclastogenesis. We also found that gene expressions CCL14 and CCL15 were significantly decreased in OSCC samples. Li et al.51,52 found that inhibition of CCL14 expression was related with suppression of breast cancer. They suggested that CCL14 could be a new promoter target for breast cancer angiogenesis and metastasis.
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Although the current study has some novel findings, there are some limitations of our study. The microarray results could not be measured to reconfirm by real time PCR, in addition, we have limited number of patient group. Depending on studies about tumour invasion and metastasis is related to degradation of ECM in several cancer types including OSCC and inflammation processes relation with several cancer types, future research needs to focus on these molecules to find or choose the specific markers. Based on this aim, as a good candidate for marker, our study in Turkish patients has for the first time simultaneously shown expression changes for MMPs, laminins, collagens and chemokines to be associated with OSCC metastasis in the same time.
Funding This work was supported by the Scientific Research Projects Coordination Unit of Istanbul University (Project number 15992).
Competing interests None declared.
Ethical approval Istanbul University after obtaining written informed consent from the participants and approval of Istanbul University’s Ethics Committee.
Acknowledgement We would like to thank to Mr. David Chapman for English editing.
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The role of components of the extracellular matrix and inflammation on oral squamous cell carcinoma metastasis Tuncay Tanis a, Zeynep Birsu Cincin b, Bilge Gokcen-Rohlig c, Elif Sinem Bireller b, Murat Ulusan a, Cem Rustu Tanyel c, Bedia Cakmakoglu b,* a
Department of Otorhinolaryngology, Faculty of Medicine, Istanbul University, Istanbul, Turkey Department of Molecular Medicine, Institute of Experimental Medical Research, Istanbul University, Istanbul, Turkey c Department of Maxillofacial Prosthodontics, Faculty of Dentistry, Istanbul University, Istanbul, Turkey b
article info
abstract
Article history:
Objectives: Oral squamous cell carcinoma (OSCC) accounts for about 90% of malignant oral
Accepted 13 July 2014
lesions, and is identified as the most frequently occurring malignant tumour of oral
Keywords:
Turkish OSCC patients.
OSCC
Materials and methods: We performed whole genome expression profiling array on an
structures. We aimed to investigate the genes and pathways related with metastasis on
Microarray
Illumina platform. A total of 24 samples with 12 OSCC and 12-paired controls that had
Metastasis
no tumour were included in the study. Hierarchic clustering and heat map were used for data visualisation and p-values assessed to identify differentially expressed genes. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Ingenuity Pathway Systems (IPA) analysis were performed to consider biologic meaning of differential expression of the genes between tumour and control groups. Results: We identified 790 probe sets, corresponding to 648 genes that were effective in separating invasive and metastatic OSCC. Consequently, we found statistically relevant expression results on extracellular matrix members on MMPs such as MMP3, MMP10, MMP1 and MMP9; on laminin such as LAMC2, LAMA3 and LAMB3; several genes in the collagen family; and also on chemokines from the inflammation process. Conclusion: Statistically relevant expression changes for MMPs, laminins, collagens, and chemokines, which are components of the extracellular matrix and inflammation process, may be considered as a molecular biomarker for early prediction. Further studies are necessary to determine and understand the molecular mechanisms that underlie OSCC metastasis. # 2014 Elsevier Ltd. All rights reserved.
* Corresponding author at: Department of Molecular Medicine, Institute of Experimental Medical Research, Istanbul University, Capa, Istanbul, Turkey. Tel.: +90 212 4142000/33305; fax: +90 212 5324171. E-mail address: [email protected] (B. Cakmakoglu). http://dx.doi.org/10.1016/j.archoralbio.2014.07.005 0003–9969/# 2014 Elsevier Ltd. All rights reserved.
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1.
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Introduction
Oral squamous cell carcinoma (OSCC) is the sixth most common type of cancer with increasing rates worldwide.1 OSCC accounts for about 90% of oral cancers and can occur at different anatomic sites of the oral cavity such as the tongue, oropharynx, lip, mouth floor, gingiva, hard palate, and buccal mucosa.2–5 Tobacco smoking and alcohol exposure are accepted as the major risk factors for transformation through oral epithelia to cancer.6 Despite the significant improvements in surgical techniques and chemotherapy, the five-year survival rate for OSCC patients is still approximately 30–50% and has not changed over the past decades.7–10 OSCC has become more important not only because the survival rate is about 30–50% but also diagnosis is one of the most difficult cancer types in early stage. Owing to the relationship between tumour development and progression with malignant cells, it is well known that the extracellular matrix (ECM), including its components, has some straightforward roles.11,12 It is important to protect the ECM because it has an impact on a series of processes in important aspects of metabolism such as cell adhesion, migration, proliferation, differentiation, and gene expression. The structure of extracellular matrix is protected by some proteases that include matrix metalloproteinases (MMPs), laminine, members of the collagen family, and also chemokines, a sub-family of inflammation regulators, the latter being the focus of our study.13,14 It has been reported that tumour invasion and metastases are related to degradation of the ECM in several cancer types, including OSCC.15–19 Even though MMPs are also known for their capability of regulating apoptosis, bone remodelling and angiogenesis, they have a role in highly complex processes that is not yet clear.20,21 Collagen, which provides structural support, is the other main component of the ECM. In some pathological conditions like cancer, collagen activity becomes abnormal thus affecting its role in the ECM.22 Laminin is an extracellular glycoproteins that is considered as a molecular marker for detecting the basement membrane during tumorigenesis due to degradation of basement membrane that occurs in most tumours.23– 25 Moreover, inflammatory processes are also reported to be related with several types of cancer.26 Chemokines are a member of the inflammatory-regulator family that has important roles in inflammation processes, angiogenesis, and metastasis.27,28 Because of the relationship between the ECM and OSCC, molecular-based studies are necessary to find multiple markers. In this respect, we aimed to simultaneously show metastatic genes and pathways for components of the ECM related with metastasis: MMPs, laminins, collagens and chemokines, in Turkish patients with OSCC. We believe that the data from this important study might ultimately lead to future research and would be helpful to find new biomarkers for early prediction of OSCC using components of the extracellular matrix.
2.
Materials and methods
2.1.
Tissue collection
Samples of squamous cell carcinoma were collected through the Department of Otolaryngology and the Department of Oral and Maxillofacial Surgery of Istanbul University after obtaining written informed consent from the participants and approval of Istanbul University’s Ethics Committee. Tumour samples were obtained from patients with OSCC at the time of resection or biopsy prior to chemo/radiation therapy. Paired control nontumor tissues from patients were collected from the clinically unaffected side and were histologically normal. The patients and controls were matched for smoking status. Tumours were pathologically staged according to the guidelines issued by the American Joint Committee on Cancer. Tissue samples were soaked in RNAlaterTM (Ambion, Austin, TX) immediately after surgical removal and transferred to the Department of Molecular Medicine of Istanbul University for long-term storage at 80 8C prior to use.
2.2.
RNA extraction and target sample generation
Tissue samples were homogenised and total RNA was extracted using High Pure Tissue RNA Isolation Kit (Roche, Germany) according to the manufacturer’s instructions. Quality and quantity of RNA was measured by using Nanodrop (Thermo Scientific, USA). RNA integrity was evaluated by microfluidics analysis using the Agilent1 2100 bioanalyzer (Santaclara, CA) and an RNA LabChip1 Kit. The Illumina1 TotalPrepTM RNA Amplification Kit was used for generating biotinylated, amplified RNA for hybridisation with Illumina Sentrix1. The procedure consisted of reverse transcription with an oligo(dT) primer bearing a T7 promoter using ArrayScript,TM a reverse transcriptase (RT) engineered to produce higher yields of first strand cDNA than wild-type enzymes. The cDNA then underwent second strand synthesis and cleanup to become a template for in vitro transcription with T7 RNA Polymerase. To maximise cRNA yield, Ambion1 MEGAscript1 (Invitrogen, Life Technologies) in vitro transcription (IVT) technology along with biotin-UTP (provided in the kit) was used to generate hundreds to thousands of biotinylated, antisense RNA copies of each mRNA in a sample.
2.3.
Array hybridisation
cRNA (500 ng) was fragmented and added to a hybridisation mixture. Expression profiles were created using Illumina Human HT-12V4 microchip, targeting more than 47,000 probes derived from the National Center for Biotechnology Information Reference Sequence (NCBI) RefSeq Release 38 (November 7, 2009) and other sources. Hybridisation procedures were performed using the procedures described by Illumina. Arrays were scanned on iScan System (Illumina).
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2.4.
Data analysis
Data collected by iScan were carried out background correction for each sample by GenomeStudio Gene Expression (GX) Module. A normalised signal for each transcript was then obtained through quantile normalisation. The signal intensities corresponding to gene expression levels of individual arrays were background corrected and imported into text files using the Illumina GenomeStudio. Hierarchical cluster analysis was applied to datasets to evaluate the ‘‘proximity’’ between the time points. For data visualisation, hierarchical clusters were constructed with the statistically significant ( p < 0.05) genes. Computing a p-value for each gene assessed the statistical significance of the differential expression of genes. Genes were considered differentially expressed when logarithmic gene expression ratios in three independent hybridisations were more than 1.5-fold difference in expression level, and when the p-values were less than 0.05. Using the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Ingenuity Pathway Analysis (IPA; Ingenuity Systems, Redwood City, CA), network, biofunction, gene ontology and pathway analyses were performed to consider biological meaning of differential expression of genes between the tumour and control groups.
3.
Results
We compared the gene expression profiles obtained from metastatic primary squamous cell carcinomas of the oral cavity (OSCC) with paired control tissue samples. Tissue samples were collected from 12 patients who underwent surgical treatment for OSCC, and 12 paired controls from clinically unaffected sides. Clinical characteristics of all patients are outlined in Table 1. Gene expression profiling of samples was determined with Illumina HT-12V4 arrays. After background correction, quantile normalisation was applied to probe sets. Boxplot (Fig. 1) and density plots (Fig. 2) before and after normalisation was drawn, respectively. Tumour and control samples were
Table 1 – Characteristics of the OSCC patients, with TNM staging. Patient code T1 T2 T3 T4 T5 T6 T7 T8 T9 T10 T11 T12
Primary site Floor of mouth Tongue Floor of mouth Tongue Gingiva Hard palate Upper lip Buccal mucosa Buccal mucosa Tongue Tongue Gingiva
Sex
TNM staging
M M M M F F M M F M F F
T4 T4 T4 T1 T4 T3 T1 T2 T2 T2 T2 T2
N2 MX N2MX N0 MX N1 MX N0 MX N0 MX N0 MX N0 MX N2 MX N0 MX N0 MX N2 MX
grouped to perform differential expression analysis with obtained data from normalisation. We identified 790 probes (648 genes) as being differentially expressed between the controls and cases that had at least a 1.5-fold difference or greater gene expression. To investigate whether different experimental groups could be classified with significant genes, the hierarchic clustering method was applied to the differentially expressed genes set. As expected, with the exception of one patient identified as patient 11 who exhibited features of both groups, the heat map showed that all metastatic primary tumours clustered together and all control tissues clustered together (Fig. 3). IPA software was used to determine how the expression pattern-clustered differentially expressed genes interconnected with each other to facilitate cellular biofunctions and signalling. According to the core analysis of all differentially expressed genes (n = 648) using the IPA, the most significant top 5 cellular functions were cellular movement, cell death and survival, cellular growth and proliferation, cellular morphology, and cell-to-cell interaction (Table 2). Annotation using Ingenuity Systems was also performed in an attempt to have a molecular understanding or models to explain functionality. The results showed that cancer, immunological disease and
Fig. 1 – Box-plots before (a) and after (b) normalisation (tumour and control samples were grouped to perform differential expression analysis with obtained data from normalisation).
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Fig. 2 – Density plots before (a) and after (b) normalisation (tumour and control samples were grouped to perform differential expression analysis with obtained data from normalisation).
Fig. 3 – Heatmap graphic. All the relevant genes are grouped by hierarchical clustering based on the expression regulation (log 2 ratios) across all the samples (12 tumour and 12 control). The colour red on the heat map indicates down-regulation during infection, while the colour green indicates up-regulation. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
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Table 2 – Molecular and cellular biofunction analyses of differentially expressed genes. Molecular and cellular functions Cellular movement Cell morphology Cell death and survival Cellular growth and proliferation Cell-to-cell signalling ˇ andˇ interaction
p-value 3.25E 6.90E 9.25E 1.08E
22 13 13 12
to to to to
1.29E 1.34E 1.07E 1.14E
# Molecules 04 04 04 04
189 116 225 236
1.31E 11 to 1.25E 04
159
inflammatory response were the top three diseases associated with the differentially expressed genes, respectively (Table 3). Differentially expressed molecules in relation to cancer clustered into GO (Gene Ontology) analysis by IPA. We have found that metastasis, invasion and neoplasm of cells were the most significant functions related with OSCC (Fig. 4). Based on all the identified genes, new and expanded pathway maps and connections and specific gene-gene interactions were inferred, functionally analysed, and used to build on the existing pathway knowledge base using IPA. To generate networks in this study, the knowledge base was queried for interactions between the identified genes and all other genes stored in the
Table 3 – Diseases and disorders analyses of differentially expressed genes. Diseases and disorders Cancer Immunological disease Inflammatory response
p-value
# Molecules
1.59E 13 to 5.62E 05 1.79E 13 to 5.02E 05 7.53E 13 to 5.71E 05
378 133 160
database. Five networks were found to be significant in OSCC (Table 4). The network with the highest score (Network 1, score = 32) was generated with 24 identified genes (Fig. 5). According to microarray analysis in 648 genes, we found that expressions of MMP3, MMP10, MMP1 and MMP9 genes significantly increased in OSCC patients compared with controls. We found that LAMC2 (laminin, gamma 2), LAMA3 (laminin, alpha 3) and LAMB3 (laminin, beta 3) were differentially expressed in OSCC tissues more than controls. Our results demonstrated that in the collagen family some gene expressions were significantly increased in OSCC such as COL1A1, COL1A2, COL3A1, COL4A1, COL4A2, COL4A6, COL5A1, COL5A2, COL6A3, COL7A1, COL8A1, COL12A1 and COL16A1. Moreover, we also found that CXCL1, CXCL9, CXCL10 and CXCL13 expression levels were significantly increased in OSCC tissue samples while CCL14 and CCL 15 were decreased.
Fig. 4 – Gene ontology analysis (invasion and neoplasm of cells related with OSCC).
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Table 4 – Network analysis. Differantially expressed genes between OSCC and uninvolved buccal mucosa with GO analysis. Genes ingenuity networks
Function
Akt, ANTXR1, ANTXR2, ATP1B2, BMP1, COL1A2, COL3A1, COL4A1, COL4A2, COL4A6, COL5A2, COL6A3, COL7A1, COL8A1, Collagen type III, Collagen type IV, Collagen type V, Collagen type VI, Collagen type VII, FAM3B, FOXF2, FUT6, gelatinase, LAMB3, LAMC1, LAMC2, Laminin1, Laminin, MMP10, Na-k-atpase, Pdgf Ab, PLOD3, PTK6, SERPINH1, WISP1 26s Proteasome, ANO1, ATP5D, ATP5J, BASP1, Calcineurin protein(s), Calmodulin, CBLB, Ck2, CLEC3B, COL12A1, CTSL1, cytochrome C, DBI, DLGAP5, EIF5A2, oestrogen receptor, FAM176A, GPD1, Hsp27, Hsp90, ID3, IFI16, IGF2R, MAPT, NFIX, NOL3, Nuclear factor 1, PLEK2, RAB30, SAT1, Smad, SP100, SUSD4, TGFBR3 CALU, DDAH2, EIF2AK2, FNDC3B, IFI27, IFI35, IFI44, IFIT1, IFIT3, IFITM1, IFITM2, IFN type 1, Ikb, Ikk (family), IL-1R, Il8r, Interferon-a Induced, IRF7, IRF9, ISGF3, Jnk, MAD2L2, NfkB1-RelA, PARP12, PPM1L, QARS, REC8, RHOC, SNAI2, STAT2, Stat1-Stat2, STK3, Tnf receptor, TNFSF10, TRIP13 Adaptor protein 1, AGTRAP, CD80/CD86, CDC25B, CDCA5, CDKN3, CTLA4, DUSP6, EGFL6, ERK, EYA2, Fc receptor, FCER1G, Fcgr3, FOXM1, HAVCR2, ICOS, INTERLEUKIN, KIR, LAMP3, LIMA1, MAL, MKNK2, NMDA Receptor, OAS2, Oas, PEBP1, PTPase, PTPRK, SH2B3, SIGLEC7, SIRPA, Sod, TH2 Cytokine, tyrosine kinase APC (complex), ASAP1, AURKA, BIRC2, BIRC5, CCNE1, Cdc2, CDC20, CDC45, Cdk, CDR2, CKAP5, CKS1B, Cyclin A, Cyclin B, Cyclin E, DIAPH2, DPT, E2f, ELF4, ERBB, Histone H1, IL17RC, LYVE1, MELK, MPC1, NDRG2, Rb, RPA3, RPA, Timp, TRIP10, UBE2C, Vegf, XAF1
4.
Discussion
The goal of our study was to find possible biomarkers from the ECM that could be easily used for the testing of biopsies or surgical margins for early diagnosis and
Score
Connective tissue disorders, dermatological diseases and conditions, gastrointestinal disease
32
Connective tissue development and function, embryonic development, organ development
30
Infectious disease, dermatological diseases and conditions, immunological disease
29
Cell-to-cell signalling and interaction, inflammatory response, haematological system development and function
28
Cell cycle, cancer, reproductive system disease
27
prognosis to possibly improve survival rates. As such, we have confirmed the distribution of component ECM such as MMPs, laminins, collagens, and chemokines in OCSS in the same study. This is the first study by doing 640 genes in microarray analysis on OSCC in Turkish patient.
Fig. 5 – Network analyses (the network with the highest score (Network 1, score = 32) was generated with 24 identified genes).
archives of oral biology 59 (2014) 1155–1163
In microarray analysis, we found that expressions of MMP3, MMP10, MMP1 and MMP9 genes were significantly increased in OSCC compared with controls. Matrix metalloproteinase are zinc-dependent endopeptidases that are shown to have an important role in tumour invasion and the metastasis process in OSCC.29–35 MMP1 is shown to act as a type I collagen proteolytic enzyme for degrading collagen I to gelatin in OSCC.36 MMP2 and MMP9 were reported to play gelatinases role to complete degradation process of collagen I to gelatin.37 Moreover, studies have indicated that MMP1 and MMP9 have important roles in the OSCC invasion process.37,38 Overexpression of MMP3 was also reported to be related with invasive and metastatic OSCC.39 Laminins are included in extracellular matrix glycoproteins found in all basement membranes and have important roles in cellular processes such as adhesion, differentiation, migration and metastasis.40 Laminin involves three chains (alpha, beta, gamma) and each chain is encoded by a distinct gene. We found that LAMC2 (laminin, gamma 2), LAMA3 (laminin, alpha 3) and LAMB3 (laminin, beta 3) were differentially expressed in OSCC tissues. When laminin binds to cell surface receptors, tumour cells release collagenase IV that degrades type IV collagen to facilitate cell migration.41 Increasing laminin levels have been associated with different carcinomas.42–45 Chen and colleagues reported that LAMC2 was associated with OSCC, which is in accordance with our results.28 The collagen family is reported to play structural roles in cellular assembly and organisation via its receptors.46 Collagens are extracellular matrix proteins that allow the basement membrane to integrate with other molecules.47 Our results demonstrated that expressions of COL1A1, COL1A2, COL3A1, COL4A1, COL4A2, COL4A6, COL5A1, COL5A2, COL6A3, COL7A1, COL8A1, COL12A1 and COL16A1 genes were significantly increased in OSCC. Degradation of these proteins could facilitate tumour cell migration by spreading tumour cells. Previous studies of over-expression of genes have shown that the balance between degradation and overproduction plays an important role in OSCC development.28–45 Chemokines have been demonstrated as mediators of inflammation that play important roles including regulation, leukocyte activation, and recruitment in areas of inflammation via their interactions with chemokine receptors.46 Experimental and clinical data have suggested that inflammatory mechanisms can contribute to OSCC tumour development.47 We found that CXCL1, CXCL9, CXCL10 and CXCL13 expression levels were significantly increased in OSCC tissue samples. Shintani et al.48 showed that high levels of CXCL1 expression were related with tumour angiogenesis and metastasis mechanisms. Chang et al.49 also indicated that OSCC samples had high expression and serum levels of CXCL9 compared with controls. Pandruvada et al.50 presented that increased expression levels of CXCL13 were associated with osteolysis in OSCC and this situation could be in correlation with MMP-9 production, which marks the degree of osteoclastogenesis. We also found that gene expressions CCL14 and CCL15 were significantly decreased in OSCC samples. Li et al.51,52 found that inhibition of CCL14 expression was related with suppression of breast cancer. They suggested that CCL14 could be a new promoter target for breast cancer angiogenesis and metastasis.
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Although the current study has some novel findings, there are some limitations of our study. The microarray results could not be measured to reconfirm by real time PCR, in addition, we have limited number of patient group. Depending on studies about tumour invasion and metastasis is related to degradation of ECM in several cancer types including OSCC and inflammation processes relation with several cancer types, future research needs to focus on these molecules to find or choose the specific markers. Based on this aim, as a good candidate for marker, our study in Turkish patients has for the first time simultaneously shown expression changes for MMPs, laminins, collagens and chemokines to be associated with OSCC metastasis in the same time.
Funding This work was supported by the Scientific Research Projects Coordination Unit of Istanbul University (Project number 15992).
Competing interests None declared.
Ethical approval Istanbul University after obtaining written informed consent from the participants and approval of Istanbul University’s Ethics Committee.
Acknowledgement We would like to thank to Mr. David Chapman for English editing.
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