DNA microarrays in oral cancer

OTERO-REY E , GARCÍA-GARCÍA A , BARROS-ANGUEIRA F , TORRES-ESPAÑOL M , GÁNDARA-REY JM , SOMOZA-MARTÍN M. DNA MICROARRAYS IN ORAL CANCER. MED ORAL 2004;9:288-92.

SUMMARY

One of the principal aims of modern cancer research is to identify markers allowing individual prediction of prognosis or response to treatment. In this connection, the number of genes thought to be involved in the different stages of different types of oral cancer increases apace. DNA microarrays allow simultaneous evaluation of the expression of hundreds of genes in a single assay. The parallel format of microassay slides is designed to allow rapid comparison of gene expression between two samples, for example tumor cells and healthy cells. This article reviews studies that have aimed to identify genes related to oral cancer, and to classify these genes into groups that are commonly co-expressed. These studies suggest that DNA microarrays are set to become routine tools in the detection, diagnosis, characterization and treatment of oral cancers.

Key words: DNA microarrays, oral cancer, gene expression.

INTRODUCTION

Oral squamous cell carcinoma is the most frequent malignant neoplasm of the oral cavity, constituting a significant public health problem. It is the sixth most frequent malign neoplasm worldwide, and has shown a marked increase in incidence since 1970 (1). But despite the considerable advances in surgical technique and other treatment technologies over the last two decades, the prognosis for oral cancer patients has scarcely changed (2). It is widely hoped that improved understanding of the molecular basis of oral carcinogenesis will lead to improvements in diagnosis, treatment and control (3).

The etiology of oral cancer is multifactorial, with the most important risk factors being smoking and alcohol consumption. These two factors often act synergistically (4). Smoking and alcohol consumption not only increase the risk of tumorigenesis, but also worsen prognosis if these habits are maintained after tumorigenesis (5).

Oral cancer may also arise in young subjects and in non-smokers, suggesting possible genetic predisposition. In such cases mutations have been detected in tumour-suppressor genes such as p53.

It is currently accepted that most solid tumours result from multiple mutational steps, leading to the activation of various oncogenes and the loss of two or more tumour-suppressor genes (6,7). The number of genes suspected to be involved in the different transformational steps increases apace as research proceeds. Recent studies on the experimental transformation of human cells indicates that alteration of a limited number of regulatory pathways is sufficient to induce tumour phenotype in diverse cell types (8).

The recently completed sequencing of the human genome suggests that the total number of genes is much lower than was previously thought (about 33,000) (9). This implies that the great complexity of biological processes is attributable to synergistic interactions among genes. Accordingly, research aimed at improving treatment of genetically-influenced human diseases is often likely to require consideration of multiple genes expressed simultaneously (10). Research strategies of this type, involving parallel consideration of large numbers of genes, are greatly facilitated by DNA microarrays (11).

DNA microarrays are small rectangles of glass or nylon membrane to which hundreds or thousands of DNA targets are adhered in a regular grid pattern. These targets may be complementary DNA (cDNA) prepared by reverse transcription from mRNAs, or synthetic oligonucleotides representing highly diagnostic stretches of the target sequences. The sample is generally RNA reverse-transcribed to give cDNA; in the present context the researcher will typically test samples from both the tumour and an appropriate non-tumorous control tissue. The cDNA from the tumour is labeled with a fluorescent tag (e.g. red), and that from the control tissue with another fluorescent tag (e.g. green). The two samples are then mixed and incubated with the microarray. Hybridized cDNA is quantified on the basis of fluorescence, and the expression of each gene in the tumour is estimated on the basis of the relative intensities of red and green labelling (12). (Fig. 1)

Microarrays thus facilitate simultaneous analysis of the expression of hundreds or thousands of genes in a tissue sample (13), by contrast with sequencing-based techniques which require individual analyses for each gene (14). Microarrays have diverse applications in cancer research and cancer medicine, including early diagnosis of the transformation of premalign lesions, identification of malignancy in tissue biopsies, subclassification of histologically identified tumours, identification of biomarkers, and drug discovery (3).

When interpreting data obtained by DNA microarray approaches, we may focus on individual genes useful for assessing prognosis or as therapeutic targets. Alternatively, microarray data allow identification of multigene “expression profiles” for a given tumour. These expression profiles may be used to classify histologically similar tumours into subtypes (15). Alizadeh et al. (16) have demonstrated that within type-B giant-cell lymphomas, there are two subtypes that can be defined on the basis of different gene expression patterns. Perou et al. (17) proposes that breast tumours can be classified into two subtypes on the basis of gene expression profiles.

In addition, our current understanding of cancer is based on the view that tumour behaviour is dictated by the expression of thousands of genes, and thus microarray approaches should allow better prediction of tumour behaviour and clinical consequences (18).

Van de Vijver et al. (19) used microarray techniques to select 70 genes as a prognostic profile in women with breast cancer, showing that the profile was a more effective predictor of prognosis than conventional clinical or histological criteria. Similary, van´t Veer et al. (20) found that microarray profiles were effective predictors of prognosis in breast cancer patients.

Since microarrays offer a “snapshot” of the activity of thousands of genes, it is easy to compare data from different times or different samples, and thus draw conclusions about co-expression patterns. This leads us to hope that microarray approaches will enable us to identify the genes characterizing the particular cellular state of malignancy (18).

DNA MICROARRAYS AND GENE EXPRESSION ANALYSIS

Efforts to characterize and predict the behaviour of oral squamous cell carcinomas have placed great emphasis on the study of heterozygosity loss and microsatellite instability. Structural alterations of the chromosome have been associated with dysplasia, in situ carcinoma and invasive carcinoma (21). However, the precise molecular mechanisms of these processes remain unknown (22).

Various authors have used DNA microarray approaches to try to identify genes related to squamous cell carcinoma of the head and neck (23-25). Villaret et al. (23) found 13 independent genes that were over-expressed in these carcinomas with respect to normal tissues. Of these genes, 9 had been identified previously, while 4 were unknown. These genes will be investigated as tumour markers and as potential sources of vaccines. Leethanakul (24) compared gene expression profiles in squamous cell carcinoma of the head and neck and in equivalent normal tissues from the same patients, finding 59 genes with differential expression. Al Moustafa et al. (25) found significant changes in the expression of 213 genes, with 91 genes over-expressed and 122 under-expressed. The affected genes included genes for signal transduction proteins and growth factors, for proteins involved in cell-cycle control, transcription, and apoptosis, and for structural proteins and proteins involved in cell-cell adhesion.

Belbin et al. (26) have suggested that gene expression profiles may be used to classify cancer patients into subgroups. On the basis of data obtained with a 375-gene cDNA microarray, these authors divided patients with squamous cell carcinoma of the head and neck into two groups with well-differentiated clinical characteristics. This grouping was a useful predictor of tumour behaviour and prognosis.

To date, however, few studies have been published about microarrays and their application in oral cancer (27). Kuo et al. (28) suggest the possibility of classifying oral squamous cell carcinomas on the basis of gene expression patterns. Using a microarray of over 4000 genes, they identified 210 genes as possibly related to oral cancer. Many of these genes (e.g. CKS1, TSPY, CBK, TLE4 and BCHE) have previously been related to other types of cancer, but not to oral cancer. These authors also present a list of genes whose expression was correlated with other classic prognostic factors, such as p53, MST1, HLA-DBQ1 and UBA52.

Mendez et al. (29) identified 314 genes that were differentially expressed in oral squamous cell carcinomas. Of these, 239 were over-expressed and 75 under-expressed with respect to normal tissues. Expression profiles for carcinomas were readily distinguishable from those for normal tissues; however, no differences were detected between incipient stages and advanced stages, or between non-metastatic and metastatic tumours.

Alevizos et al. (30) examined DNA microarray data for 5 cases of oral cancer using three different software packages (GeneChip, GeneCluster and Matlab). Their analysis revealed that approximately 600 genes are associated with oral cancer, including oncogenes, tumour suppressor genes, transcription factors, xenobiotic enzymes, metastatic proteins, and differential markers. A number of the genes identified have not been implicated previously in oral cancer. These results provide a verifiable profile of gene expression in oral carcinogenesis.

At present, the vastly complex relationships between gene expression profiles and cell behaviour remain poorly understood. Despite this, it seems likely that it will soon become routine practice to use purpose-designed commercial DNA microarrays to obtain “gene expression fingerprints” for individual tumours, with the aim of improving prognosis, and of designing individualized treatment strategies. As noted, DNA microarray strategies are also likely to prove very useful for drug development (31). In short, DNA microarray technologies seem set to have major impacts on the management of oral cancer.

Supported by a grant from the Xunta de Galicia, Spain, PGIDT01PX120804PR

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