5 research outputs found
Urinary peptide panel for prognostic assessment of bladder cancer relapse
Non-invasive tools stratifying bladder cancer (BC) patients according to the risk of relapse are urgently
needed to guide clinical intervention. As a follow-up to the previously published study on CE-MSbased urinary biomarkers for BC detection and recurrence monitoring, we expanded the investigation
towards BC patients with longitudinal data. Profling datasets of BC patients with follow-up information
regarding the relapse status were investigated. The peptidomics dataset (n=98) was split into training
and test set. Cox regression was utilized for feature selection in the training set. Investigation of the
entire training set at the single peptide level revealed 36 peptides being strong independent prognostic
markers of disease relapse. Those features were further integrated into a Random Forest-based model
evaluating the risk of relapse for BC patients. Performance of the model was assessed in the test cohort,
showing high signifcance in BC relapse prognosis [HR=5.76, p-value=0.0001, c-index=0.64]. Urinary
peptide profles integrated into a prognostic model allow for quantitative risk assessment of BC relapse
highlighting the need for its incorporation in prospective studies to establish its value in the clinical
management of BC