Conclusions
The results presented herein offer encouraging preliminary data that may provide a basis for a more precise prognosis of HNSCC, as well as a molecular-based therapy decision for the management of these cancers.
Methods
Residual biopsy samples from eight complete responders (CR) and six nonresponders (NR) were evaluated by genome-wide gene expression profiling using HG-U133A 2.0 arrays. Univariate parametric t-tests with proportion of false discoveries controlled by multivariate permutation tests were used to identify genes with significantly different gene expression levels between CR and NR cases. Six different prediction algorithms were used to build gene predictor lists. Three representative genes showing 100% crossvalidation support after leave-one-out crossvalidation (LOOCV) were further validated using real-time QRT-PCR.
Results
We identified 167 significant probe sets that discriminate between the two classes, which were used to build gene predictor lists. Thus, 142 probe sets showed an accuracy of prediction ranging from 93% to 100% across all six prediction algorithms. The genes represented by these 142 probe sets were further classified into different functional networks that included cellular development, cellular movement, and cancer. Conclusions: The results presented herein offer encouraging preliminary data that may provide a basis for a more precise prognosis of HNSCC, as well as a molecular-based therapy decision for the management of these cancers.