Endometriosis is one of the most prevalent gynecological disorders, affecting over 190 million women worldwide. While typically benign, a subset of cases progresses to endometriosis-associated ovarian cancer (EAOC), a distinct subtype of epithelial ovarian cancer. Despite this established link, current clinical practice lacks reliable methods to identify women at elevated risk of EAOC. This translational research project applies a personalized medicine framework to discover molecular markers predictive of EAOC risk using a comprehensive multi-omics approach. We will leverage rare, longitudinally archived tissue samples—including endometrium, endometrioma, ovary, and EAOC—from the same individuals collected over time. Through spatial transcriptomics and next-generation sequencing, we will profile mRNAs, small RNAs, proteins, somatic mutations, and DNA methylation patterns. Integrated bioinformatic analysis will enable identification of a predictive molecular panel applicable at the time of endometrioma excision. The ultimate goal is to develop a clinically viable risk assessment tool that facilitates early identification and personalized management of high-risk individuals. This project has the potential to transform EAOC prevention strategies, improving clinical outcomes while reducing the physical, emotional, and economic burden on women.