Metabolic-Related Gene Prognostic Index for Predicting Prognosis, Immunotherapy Response, and Candidate Drugs in Ovarian Cancer

Ovarian
cancer (OC) is a highly heterogeneous disease,
with patients
at different tumor staging having different survival times. Metabolic
reprogramming is one of the key hallmarks of cancer; however, the
significance of metabolism-related genes in the prognosis and therapy
outcomes of OC is unclear. In this study, we used weighted gene coexpression
network analysis and differential expression analysis to screen for
metabolism-related genes associated with tumor staging. We constructed
the metabolism-related gene prognostic index (MRGPI), which demonstrated
a stable prognostic value across multiple clinical trial end points
and multiple validation cohorts. The MRGPI population had its distinct
molecular features, mutational characteristics, and immune phenotypes.
In addition, we investigated the response to immunotherapy in MRGPI
subgroups and found that patients with low MRGPI were prone to benefit
from anti-PD-1 checkpoint blockade therapy and exhibited a delayed
treatment effect. Meanwhile, we identified four candidate therapeutic
drugs (ABT-737, crizotinib, panobinostat, and regorafenib) for patients
with high MRGPI, and we evaluated the pharmacokinetics and safety
of the candidate drugs. In summary, the MRGPI was a robust clinical
feature that could predict patient prognosis, immunotherapy response,
and candidate drugs, facilitating clinical decision making and therapeutic
strategy of OC.