Signaling Networks of Activated Oncogenic and Altered XXXX Suppressor Genes in Head and Neck Cancer.

@entity9486 @entity650 @entity11968 @entity1 @entity1356 @entity5 @entity4104 @entity4206 @entity470 @entity10152 @entity584 @entity4909 @entity876 @entity3470 @entity10497 @entity11346 @entity5738 @entity1916 @entity3 @entity213
UNASSIGNED: @entity213 ( @entity213 ) arises from the upper aerodigestive tract and is the six most common @entity5 worldwide. @entity213 is associated with high morbidity and mortality, as standard surgery, radiation, and chemotherapy can cause significant disfigurement and only provide 5-year survival rates of ~50-60%. The heterogeneity of @entity213 subsets with different potentials for recurrence and @entity3 challenges the traditional pathological classification system, thereby increasing demand for the development of new diagnostic, prognostic, and therapeutic tools based on global molecular signatures of @entity213 . Historically, using classical biological techniques, it has been extremely difficult and time-consuming to survey hundreds or thousands of genes in a given disease. However, the development of high throughput technologies and high-powered computation throughout the last two decades has enabled us to investigate hundreds or thousands of genes simultaneously. Using high throughput technologies, our laboratory has identified the gene signatures and protein networks, which significantly affect @entity213 malignant phenotypes, including @entity876 / @entity4206 / @entity9486 family members, @entity1356 / @entity4909 /NF-kB, PI3K/ @entity584 / @entity3470 , @entity650 / @entity10497 /JAK/ @entity4104 , @entity470 /MAPK/ @entity11346 , @entity11968 / @entity1916 / @entity5738 , and TGFb/TGFbR/ @entity10152 /SMAD pathways. This review summarizes the results from high-throughput technological assays conducted on @entity213 samples, including microarray, DNA methylation, miRNA profiling, and protein array, using primarily experimental data and conclusions generated in our own laboratory. The use of bioinformatics and integrated analyses of data sets from different platforms, as well as meta-analysis of large datasets pulled from multiple publicly available studies, provided significantly higher statistical power to extract biologically relevant information. The data suggested that the heterogeneity of @entity213 genotype and phenotype are much more complex than we previously thought. Understanding of global molecular signatures and disease classification for specific subsets of @entity213 will be essential to provide accurate diagnoses for targeted therapy and personalized treatment, which is an important effort toward improving @entity1 outcomes.
