Serum Fingerprinting-Based Integrative Dual-Omics Machine Learning for Endometriosis-Associated Ovarian Cancer

Dual-omics, by integrating molecular information from two distinct dimensions, can offer more comprehensive perspective for complex disease. Herein, we developed an efficient functionalized mesoporous nanoparticle-coupled laser desorption/ionization mass spectrometry (fMNPLDI-MS) platform capable of extracting high-quality serum metabolic fingerprints (SMFs) and serum peptide fingerprints (SPFs) from trace serum samples within 50 s. Integrating these SMFs and SPFs markedly improved early screening and subtyping of endometriosis-associated ovarian cancer (EAOC) more than single omics. Specifically, leveraging machine learning on the identified 6 metabolites and 6 peptides, the integration strategy attained an area under the receiver operating characteristic curve (AUC) value of 0.989 and accuracy of 93.1%, compared with 0.964/89.7% for metabolomics alone and 0.960/87.9% for peptidomics alone in distinguishing EAOC from benign controls. Similarly, the dual-omics integration strategy achieved an AUC value of 0.875 and accuracy of 86.7%, surpassing individual metabolomics (AUC: 0.869, accuracy: 83.3%) and peptidomics (AUC: 0.733, accuracy: 76.7%), in subtype classification. This high-throughput fMNPLDI-MS dual-omics platform provides a powerful tool for EAOC screening and subtyping, paving the way toward early detection and precision management of this malignancy.