Gene expression profiling in kidney cancer: combining differential expression and chromosomal and pathway analyses.
The high-throughput gene expression profiling by microarray analysis has enabled researchers to compare the relative expression levels of thousands of genes in diseases, including cancer. By identifying how these genes cluster in different carcinomas, these profiling techniques can improve the accuracy of classifying subtypes of tumors and their prognoses and could help determine which therapy is appropriate for each patient with cancer. These efforts aim to provide more effective personalized medicine. To reach this goal, the analysis of microarray data has also evolved and become more sophisticated and complex. Herein, using kidney cancer as an example, we demonstrate the use of microarray data for different bases of analysis, ie, direct differential expression, deduced chromosomal alteration, and pathways signature. We believe combining these will enhance the value of microarray studies and will better serve the goals we try to achieve using these data.
Furge
Kyle A
KA
Laboratory of Computational Biology, Van Andel Research Institute, Grand Rapids, MI, USA.
Kort
Eric J
EJ
Yang
Ximing J
XJ
Stadler
Walter M
WM
Kim
Hyung
H
Teh
Bin Tean
BT
D002292:Carcinoma, Renal Cell / Q000175:diagnosis* / Q000235:genetics / Q000628:therapy; D002869:Chromosome Aberrations; D002874:Chromosome Mapping; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D007680:Kidney Neoplasms / Q000175:diagnosis* / Q000235:genetics / Q000628:therapy; D046228:Microarray Analysis; D008957:Models, Genetic; D015398:Signal Transduction