Introduction/Background High-grade serous ovarian cancer, despite its high lethality, lacks reliable biomarkers for predicting poor prognosis, and Limited progression has been made in personalized treatment. Genomic profile-based targeted therapy has not met expectations, as genomic alterations alone do not exclusively determine cancer cell phenotypes. Protein expression critically influences cellular processes. Recognizing proteomic alterations is even more crucial. This study proposes a novel technique, utilizing statistical deviation and unsupervised learning to select protein factors determining ovarian cancer prognosis.

Methodology The good prognosis group comprised 24 cases, characterized by no relapse for 5 years after the initial treatment or relapse with no further progression for the subsequent 5 years. The poor prognosis group included 23 cases, defined by expiration within 2 years, relapse within 1 year with expiration within 3 years, or refractory and resistant cases with expiration within 2 years. We utilized fresh-frozen tissue and Olink’s Proximity Extension Assay(PEA) technology, involving pairs of antibodies binding to specific targets and generating double-stranded DNA amplicons. The proteomic profile was analyzed using the unsupervised learning-based method after comparing the expression levels with the proposed data analysis method.

Results Five proteins significantly differed between poor and good prognosis groups by t-test and confirmed by real-time PCR. Applying a learning-based clustering method with proteins A and B produced promising outcomes, with precision, recall, and F-1 score values of 0.85, 0.81, and 0.83, respectively. Consequently, we proceeded to conduct in vitro and in vivo studies to assess the relationship between candidate proteins and the prognosis of patients.

Conclusion Our findings suggest that selecting factors with substantial differences between patients’ groups, particularly those amenable to clustering based on identified proteins, is feasible. The research underscores the potential of proteomic markers in guiding personalized therapeutic strategies for improved patient outcomes.

Disclosures The author declares that they have no conflict of interest.