Abstract
INTRODUCTION: Epithelial ovarian cancer (OC) is typically diagnosed at advanced stages and accounts for more deaths than any other gynecological cancer. Early detection of OC may improve prognosis. Microbial profiling has identified various diseases and may be utilized to detect OC. We hypothesize that patients with OC have a unique microbial profile, which in conjunction with biomarkers Cancer Antigen-125 (CA-125) and Human Epididymis protein 4 (HE4), would enable a novel screening mechanism to effectively detect OC. METHODS: Inclusion: Consented patients ≥30 yrs presenting at SIU Gynecologic/Oncology for management of an adnexal mass or suspected OC. Exclusion: patients with a previous malignancy, pregnant/intending to conceive. CA-125/HE4 were analyzed in serum and peritoneal fluid (PF). Extracted DNA from PF was sequenced (V4 region of bacterial 16S rRNA gene), then identified using Greengenes database and clustered into operational taxonomical units (OTUs). Microbial profile associations with CA-125/HE4 in serum and PF were calculated utilizing machine learning models for biomarker discovery. RESULTS: CA-125 and HE4 levels were elevated in serum and PF from patients with OC. Patients with OC also exhibited microbial profile clustering. Machine learning models identified 37 microbial features correlative with OC. Overall, performance of biological features (age, BMI, serum [CA-125/HE4], PF [CA-125/HE4], OTUs [taxa]), indicated that serum [CA-125/HE4]+age/BMI had the highest (0.96) accuracy for OC detection. Interestingly, OTUs [family/genus level+age/BMI] (0.87) outperformed PF [CA-125/HE4+age/BMI] (0.78). CONCLUSION: Microbial feature analysis may improve OC screening modalities and warrants further investigations into the urogenital and/or gastrointestinal microbiome profiles to improve detection/survival rates for OC.
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