| dc.description.abstract | The purpose of this study was to classify oral squamous cell carcinomas (OSCCs) based
on their gene expression profiles, to identify differentially expressed genes in these
cancers, and to correlate genetic deregulation with clinical-histopathological data and
patient outcome. After conducting proof of principle experiments utilizing six head and
neck squamous cell carcinomas (HNSCCs) cell lines, the gene expression profiles of 20
OSCCs and subsequently an additional 8 OSCCs were determined using cDNA
microarrays containing 19,200 sequences and the Binary Tree-Structured Vector
Quantization (BTSVQ) method of data analysis. Two sample clusters were identified in
the group of 20 tumors that correlated with T3-T4 category of disease (P=0.035) and
nodal metastasis( p=0.035). Samplec lustering of 28 OSCCsa nd the 6 cell lines revealed
a correlation with disease free survival. BTSVQ analysis identified a subset of 23
differentially expressed genes with the lowest quantization error scores in the cluster
containing more advanceds taget umors from the 20 OSCC dataset.T he expressiono f six
of these differentially expressedg enesw as validated by quantitative real-time RT-PCR.
Statistical analysis of quantitative real-time RT-PCR data was performed and, after
Bonferroni correction, CLDNI (p = 0.007) over-expressionw as significantly correlated
with the cluster containing more advanced stage tumors. Despite the clinical
heterogeneity of OSCC, molecular subtyping by cDNA microarray analysis was able to
identify distinct patternso f genee xpressiona ssociatedw ith relevant clinical parameters.
The application of this methodology represents an advance in the classification of oral
cavity tumors, and may ultimately aid in the development of more tailored therapies for
oral carcinoma. | en_US |
