A novel analytical method for identifying gene clusters predictive of post-radiotherapy chronic fatigue symptoms in prostate cancer patients

Authors

• SA Raheem
• RL Feng
• LN Saligan

Abstract

Fatigue is a common side effect of cancer 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). A total of 41 PCP were categorized into chronic fatigue (CF) and non-fatigue (NF) cohorts based on fatigue score change from baseline to 1 year post-RT. Fold-change differential and Fisher’s linear discriminant analyses (LDA) from 29 subjects with gene expression data at baseline 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 the rest of the subjects during the validation phase. Pathway analysis using Ingenuity® Pathway Analysis (IPA®, Qiagen) was conducted to identify functional pathways associated with the selected genes. An in vitro model involving GFP-tagged mGluR transfected into Jurkat cells (T cells) was used to explore the pathogenic mechanism of the identified genes. The model generated in the learning phase predicted CF classification at RT completion in the validation phase with 85% accuracy and many of the genes generated in this model are involved in the glutamate signaling pathway. Differential expression of mGluR genes influenced T cell activation after radiation suggesting a possible involvement of mGluRs in fatigue pathogenesis. The results suggest that a novel analytical may have applicability in predicting regimen-related toxicity in cancer patients, as well important information that can explain the etiology of radiotherapy-related fatigue.

Scientific Focus Area: Immunology

This page was last updated on Friday, March 26, 2021