Since the draft sequence of the human genome was published in 2001,  the Cancer Genome Anatomy Project index of tumor genes has classified more than 40,000 genes directly or indirectly involved in one or more cancers. [2, 3] Conventional techniques of gene investigation in cancer rely on the identification of single genetic alterations associated with disease. This has proven to be both time consuming and cost ineffective. The introduction of complementary DNA (cDNA) microarray technology in 1995 has helped to facilitate the identification and classification of DNA sequence information and the assignment of functions to these new genes by allowing investigators to analyze expression of thousands of genes simultaneously in a single experiment. 
Microarrays are a significant advance because of their small size and are therefore useful when one wants to survey a large number of genes quickly or when the study sample is small. Microarrays may be used to assay gene expression within a single sample or to compare gene expression in 2 different cell types or tissue samples, such as in healthy versus diseased tissue. Because a microarray can be used to examine the expression of hundreds or thousands of genes at once, it promises to revolutionize the way gene expression is examined.
DNA microarrays are simply platforms that consist of small solid supports onto which the sequences from thousands of different genes are attached at fixed locations. The technology allows evaluation of many gene transcripts at one time. The individual DNA strands are called probes. The supports themselves are usually glass microscope slides but can also be silicon chips or nylon membranes. The DNA is printed, spotted, or actually synthesized directly onto the support (see image below.)
Messenger RNA (mRNA) from the sample of interest can serve as a template for producing complementary DNA (cDNA) in the presence of a reverse transcriptase enzyme. This cDNA can then be fluorescently labeled and hybridized to the target gene sequences on the microarray. Because the locations of the probes are known, the intensity and pattern of the labeled mRNA can be used to measure the expression of the targeted gene. A confocal scanner then reads the fluorescent intensity of each hybridized sequence in the array. The scanner that records the intensity value is linked to digital image analysis software, which produces a color-coded image of the array, and a quantitative value is recorded for each target gene. The intensity of fluorescence is analyzed and correlates with expression of the gene.
The data produced from a microarray experiment typically constitute a long list of measurements of spot intensities and intensity ratios, generated either by a pair-wise comparison of 2 samples or by a comparison of several samples with a common control. The challenge is to sort through these data to find meaningful results. Because of the complexity of the data sets generated by microarray experiments, the use of data-analysis software is essential. Several commercial and public data-analysis tools have been developed for this purpose.
The 2 most common microarray technologies in use are the oligonucleotide microarrays and the robotically spotted complementary DNA (cDNA) microarrays. Both technologies use hybridization of labeled nucleic acid transcripts to measure the gene expression.
Oligonucleotide microarrays are manufactured by Affymetrix (Santa Clara, Calif) using photolithographic techniques.  They consist of a glass surface onto which oligonucleotides consisting of 25 bases are built a single nucleotide at a time. The chemical addition of nucleotides is controlled by exposing certain strands to light while masking others. This process is repeated to build specific oligonucleotide sequences. Each chip contains thousands of probe sets, each representing single genes. Each probe set consists of 16-20 probe pairs that represent specific coding regions for a given gene. The oligonucleotide sequence on the array should be complementary and specific to the messenger RNA (mRNA) being investigated.
Robotically spotted cDNA microarrays
This technique was first developed at Stanford University by robotically spotting purified cDNA samples onto a glass slide or nylon membrane.  The sequences are amplified by polymerase chain reaction and printed onto the slide using robotic techniques. These DNA probes are transferred as intact DNA strands, compared with individual bases of oligonucleotide microarrays.
This technique uses 2 fluorescent labels. Cy3 fluoresces green when exposed to light, while Cy5 fluoresces red. The mRNA samples are reverse-transcribed to cDNA using fluorescently labeled nucleotides. These are then combined to the microarray, and the target cDNA is hybridized to the corresponding probe on the microarray. The nonhybridized DNA is washed off the slide, and the intensity of fluorescence is measured.
Current Applications in Head and Neck Oncology
DNA microarrays are relatively new techniques in the field of oncology and are used to better understand and diagnose various malignancies. Its use is promising in the advancement of tumor detection and therapeutics. In recent years, the use of microarray technology has been of great interest in head and neck squamous cell carcinoma (HNSCCa). Despite the advancement of the diagnosis and treatment of HNSCCa, survival has not improved.
Microarrays may eventually help in the understanding of the disease and ultimately lead to improvements in diagnosis, treatment, and outcome.  Furthermore, the quantitative and qualitative aspect of microarrays may eventually be exploited to screen for molecular markers of head and neck cancer. [8, 9] Ideally, their use will aid in the identification of progression from dysplasia to invasive carcinoma, distant metastisis, and clinically important outcome measures. Numerous expression studies of HNSCCa have been performed. [8, 10, 11, 12, 13, 14, 15, 16]
Currently, there is much heterogeneity within large amounts of data available, which leads to contradictory findings between studies. In their review, Choi et al  identified simultaneous up- or down-regulation of genes encoding for cell cycle regulation, matrix metalloproteinases, inflammatory response mediators, enzymes of the mevalonate pathway, or ribosomal proteins.
Belbin et al  used complementary DNA (cDNA) microarrays that contained 9216 clones to measure global patterns of gene expression in HNSCCa. Through the use of statistical analysis, they identified 375 differentially expressed genes, which divided 17 patients with head and neck tumors into 2 clinically distinct subgroups based on gene-expression patterns. The results of their analysis demonstrated that gene-expression profiling can be used as a predictor of outcome and highlighted pathways, meriting exploration for possible links to outcome in HNSCCa.
Using cDNA subtractive methodology in conjunction with microarray technology to screen for HNSCCa-specific genes, Villaret et al  were able to identify 9 known genes that were significantly overexpressed in HNSCCa compared with healthy tissue specimens. In addition, they found 4 previously unidentified genes that were overexpressed in a subset of tumors.
Using a cDNA array of 588 known human cancer-related genes and 9 housekeeping genes, Leethanakul et al  demonstrated a consistent decrease in the expression of differentiation markers, such as cytokeratins, and an increase in the expression of numerous signal-transducing and cell-cycle regulatory molecules, as well as growth and angiogenic factors and tissue-degrading proteases. The authors also found that most HNSCCas over-express members of the Wnt and Notch growth and differentiation regulatory system, suggesting that the Wnt and Notch pathways may contribute to squamous cell carcinogenesis.
Spectral karyotyping (SKY), comparative genomic hybridization (CGH), and microarrays were used by Squire et al  to identify consensus regions of chromosomal imbalance and structural rearrangement in HNSCCa.  The authors were able to demonstrate recurrent chromosomal alterations using CGH and SKY and to correlate them to expression array analysis.
Sok et al  used hierarchical clustering analysis to reveal that the gene-expression profiles obtained from a panel of 12,000 genes could distinguish tumor from nonmalignant tissues.  Gene expression changes were reproducibly observed in 227 genes, representing previously identified factors associated with neoplasia. Furthermore, significant expression of the collagen type XI alpha-1 gene and a novel gene were reproducibly observed in all 9 tumors, whereas these genes were virtually undetectable in their corresponding, adjacent nonmalignant tissues.
Despite strides in prevention and advances in treatment, cancer of the head and neck remains a disease of considerable morbidity and mortality. The use of complementary DNA (cDNA) microarray technology to explore gene expression on a global level is rapidly evolving. Although microarray technology is still in its infancy, further investigation may prove helpful in the diagnosis, prognosis, and management of head and neck cancer.