Using high-throughput gene-expression profiling technology, we can now gaina better understanding of the complex biology that is taking place in cancer cells. This complexity is largely dictated by the abnormal genetic makeup ofthe cancer cells. This abnormal genetic makeup can have profound effectson cellular activities such as cell growth, cell survival and other regulatory processes. Based on the pattern of gene expression, or molecular signatures of the tumours, we can distinguish or subclassify different types of cancers according to their cell of origin, behaviour, and the way they respond to therapeuticagents and radiation. These approaches will lead to better molecularsubclassification of tumours, the basis of personalized medicine. We have, todate, done whole-genome microarray gene-expression profiling on several hundredsof kidney tumours. We adopt a combined bioinformatic approach, based on an integrative analysis of the gene-expression data. These data are used toidentify both cytogenetic abnormalities and molecular pathways that are deregulatedin renal cell carcinoma (RCC). For example, we have identified the deregulationof the VHL-hypoxia pathway in clear-cell RCC, as previously known,and the c-Myc pathway in aggressive papillary RCC. Besides the more commonclear-cell, papillary and chromophobe RCCs, we are currently characterizingthe molecular signatures of rarer forms of renal neoplasia such ascarcinoma of the collecting ducts, mixed epithelial and stromal tumours, chromosomeXp11 translocations associated with papillary RCC, renal medullarycarcinoma, mucinous tubular and spindle-cell carcinoma, and a group of unclassified tumours. Continued development and improvement in the field of molecular profiling will better characterize cancer and provide more accurate diagnosis, prognosis and prediction of drug response.
Gene expression profiling of microRNAs associated with UCA1 in bladder cancer cells
