title: Supervised classification by filter methods and recursive feature elimination predicts risk of radiotherapy-related fatigue in patients with prostate cancer.
abstract: BACKGROUND
  Fatigue is a common side effect of cancer (CA) treatment. We used a novel analytical method to identify and validate a specific gene cluster that is predictive of fatigue risk in prostate cancer patients (PCP) treated with radiotherapy (RT).
  
  
  METHODS
  A total of 44 PCP were categorized into high-fatigue (HF) and low-fatigue (LF) cohorts based on fatigue score change from baseline to RT completion. Fold-change differential and Fisher's linear discriminant analyses (LDA) from 27 subjects with gene expression data at baseline and RT completion generated a reduced base of most discriminatory genes (learning phase). A nearest-neighbor risk (k-NN) prediction model was developed based on small-scale prognostic signatures. The predictive model validity was tested in another 17 subjects using baseline gene expression data (validation phase).
  
  
  RESULTS
  The model generated in the learning phase predicted HF classification at RT completion in the validation phase with 76.5% accuracy.
  
  
  CONCLUSIONS
  The results suggest that a novel analytical algorithm that incorporates fold-change differential analysis, LDA, and a k-NN may have applicability in predicting regimen-related toxicity in cancer patients with high reliability, if we take into account these results and the limited amount of data that we had at disposal. It is expected that the accuracy will be improved by increasing data sampling in the learning phase.
authors.affiliation: National Institute of Nursing Research, National Institutes of Health, Bethesda, Maryland, USA.
authors.forename: Leorey N
authors.initials: LN
authors.lastname: Saligan
authors.affiliation: Universidad de Oviedo, Spain.
authors.forename: Juan Luis
authors.initials: JL
authors.lastname: Fernández-Martínez
authors.affiliation: Universidad de Oviedo, Spain.
authors.forename: Enrique J
authors.initials: EJ
authors.lastname: deAndrés-Galiana
authors.affiliation: Biomodels, LLC, Watertown, MA, USA.
authors.forename: Stephen
authors.initials: S
authors.lastname: Sonis
keywords: Fisher’s linear discriminant analysis (LDA); cancer-related fatigue; k-NN backward recursive feature elimination; prostate cancer; radiation therapy