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Classification and prognosis of larynx squamous cell carcinoma (LSCC) depends on clinical and histopathological examination. Currently, expression profiling harbors the potential to investigate, classify, and better manage cancer. Gene expression profiles of 22 primary LSCCs were analyzed by microarrays containing 19,200 cDNAs. GOAL functionally classified differentially expressed genes, and a novel "in silico" procedure identified physical gene clusters differentially transcribed. A signature of 158 genes differentiated tumors with nodal metastasis. A novel statistical method allowed categorization of metastatic tumors into 2 distinct subgroups of differential gene expression patterns. Among genes correlated to nodal metastatic progression, we verified in vitro that NM23-H3 reduced cell motility and TRIM8 were a growth suppressor. Six chromosomal regions were specifically downregulated in metastatic tumors. This large-scale gene expression analysis in LSCC provides information on changes in genomic activity associated with lymphonodal metastasis and identifies molecules that might prove useful as novel therapeutic targets.

Odontogenic tumours are rare neoplasms whose classification is sometime controversial. Among these entities, granular cell odontogenic tumour (GCOT) is extremely rare and usually has a benign clinical behaviour. While the histogenesis of GCOT remains to be clarified, we documented the existence of a malignant counterpart of this neoplasm and proposed the name of malignant GCOT. Expression profiling by cDNA microarrays is a molecular technology that enables a global gene expression analysis. By using cDNA microarrays, we identified in malignant GCOT several genes with significantly differentially regulated genes when compared to non neoplastic tissues. These cancer specific genes include a range of functional activities: (1) transcription, (2) signaling transduction, (3) cell-cycle regulation, (4) apoptosis, (5) differentiation and (6) angiogenesis. In conclusion, we show that cDNA microarrays is a useful approach to investigate the biology of tumours. Moreover, this technology might lead to identification of gene targets for cancer therapy and to molecular classification of odontogenic tumours.