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Abstract
May 18, 2016
Background Clinical
and pathological parameters of patients with epithelial ovarian cancer
(EOC) do not thoroughly predict patient outcome.
Despite the good outcome of stage I EOC compared
to stage III-IV, the risk assessment and treatments are almost the
same.
However, only 20% of stage I EOC relapse and
die, meaning that only a proportion of patients need intensive treatment
and
closer follow-up. Thus, the identification of
cell mechanisms that could improve outcome prediction and rationalize
therapeutic
options is an urgent need in the clinical
practice.
Patients and methods
We have gathered together 203 patients with stage I EOC diagnosis, from
whom snap-frozen tumor biopsies were available at
time of primary surgery before any treatment.
Patients, with median follow-up of seven years, were stratified into a
training
set and a validation set.
Results and conclusions Integrated analysis of miRNA and gene expression profiles, allowed to identify a prognostic cell pathway, composed of 16
miRNAs and 10 genes, wiring the cell cycle, Activins/Inhibins and Hedgehog
signaling pathways. Once validated by an independent technique, all the
elements of the circuit resulted associated to overall
(OS) and progression free survival (PFS), in
both univariate and multivariate models. For each patient, the circuit
expressions
have been translated into an activation state
index (ISC), used to stratify patients into classes of risk. This
prediction
is 89.7% sensitive and 96.6% of specific for the
detection of PFS events. The prognostic value was then confirmed in the
external
independent validation set in which the PFS
events are predicted with 75% sensitivity and 94.7% specificity.
Moreover, the
ISC shows higher classification performance than
conventional clinical classifiers. Thus, the identified circuit
enhances
the understanding of the molecular mechanisms
lagging behind stage I EOC and the ISC improve our capabilities to
assess, at
the time of diagnosis, the patient risk of
relapse.
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