Microarray technologies in the diagnosis and treatment of head and neck cancer

Source: emedicine.medscape.com Authors: Perminder S Parmar, MD et al. Introduction Since the draft sequence of the human genome was published in 2001 (Lander, 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 (Strausberg, 2001; Strausberg, 2000). 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 (Schena, 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 they contain a very large number of genes and because of their small size. Therefore, microarrays are 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 and 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. Methods DNA microarrays are small solid supports onto which the sequences from thousands of different genes are attached at [...]

2009-11-06T21:36:37-07:00November, 2009|Oral Cancer News|

Gene Expression Profiling Identifies Genes Predictive of Oral Squamous Cell Carcinoma

Source: Cancer Epidemiology Biomarkers & Prevention, 10.1158/1055-9965 Authors: Chu Chen et al Oral squamous cell carcinoma (OSCC) is associated with substantial mortality and morbidity. To identify potential biomarkers for the early detection of invasive OSCC, we compared the gene expressions of incident primary OSCC, oral dysplasia, and clinically normal oral tissue from surgical patients without head and neck cancer or preneoplastic oral lesions (controls), using Affymetrix U133 2.0 Plus arrays. We identified 131 differentially expressed probe sets using a training set of 119 OSCC patients and 35 controls. Forward and stepwise logistic regression analyses identified 10 successive combinations of genes which expression differentiated OSCC from controls. The best model included LAMC2, encoding laminin-2 chain, and COL4A1, encoding collagen, type IV 1 chain. Subsequent modeling without these two markers showed that COL1A1, encoding collagen, type I 1 chain, and PADI1, encoding peptidyl arginine deiminase, type 1, could also distinguish OSCC from controls. We validated these two models using an internal independent testing set of 48 invasive OSCC and 10 controls and an external testing set of 42 head and neck squamous cell carcinoma cases and 14 controls (GEO GSE6791), with sensitivity and specificity above 95%. These two models were also able to distinguish dysplasia (n = 17) from control (n = 35) tissue. Differential expression of these four genes was confirmed by quantitative reverse transcription-PCR. If confirmed in larger studies, the proposed models may hold promise for monitoring local recurrence at surgical margins and the development of second primary oral cancer [...]

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