Machine learning-derived diagnostic model of epithelial ovarian cancer based on gut microbiome signatures

Prior studies have elucidated that alterations in gut microbiota are associated with a spectrum of tumors and metabolic disorders. However, the diagnostic value of gut microbiota in epithelial ovarian cancer remains insufficiently investigated.

A total of 34 patients with a diagnosis of epithelial ovarian cancer (EOC), 15 patients with benign ovarian tumors (TB), and 30 healthy volunteers (NOR) were enrolled in this study. Fecal samples were collected, followed by sequencing of the V3-V4 region of the 16S rRNA gene. The clinical data and pathological characteristics were comprehensively recorded for further analysis, PICRUSt2 was utilized to conduct an analysis of microbial functional predictions, WGCNA networks were constructed by integrating microbiome and clinical data. LEfSe analysis was employed to identify microbial diagnostic markers, LASSO and SVM analyses were used to screen microbial diagnostic markers in conjunction with the Cally index, to establish a Microbial-Cally diagnostic model. Bootstrap resampling was utilized for the internal validation of the model, whereas the Hosmer-Lemeshow test and decision curve analysis (DCA) were employed to evaluate the diagnostic performance of the model. Plasma samples were subjected to untargeted metabolomics profiling, followed by differential analysis to identify key metabolites that are significantly altered in epithelial ovarian cancer. At the same time, Spearman correlation analysis was used to study the association between key microbiota and differential metabolites. The supernatants from Escherichia coli and Bifidobacterium cultures were co-cultured with SKOV3 cells. Cell proliferation, migration, and invasion were evaluated using Cell Counting Kit-8 (CCK-8) assay, Transwell migration and invasion assays. Apoptosis was assessed by flow cytometry analysis of fluorescence signals from Annexin V and propidium iodide (PI) staining.

Compared to Nor and TB populations, individuals diagnosed with EOC demonstrated a significantly diminished gut microbiota diversity when contrasted with both normal controls and those presenting benign conditions. Specifically, the relative abundance of Bilophila, Bifidobacterium, and other probiotics was significantly reduced in patients diagnosed with epithelial ovarian cancer (EOC), while Escherichia and Shigella demonstrated a marked enrichment within this cohort. Differential microorganisms were identified through the application of machine learning techniques to delineate the characteristic microbial profiles associated with the EOC patients. A significant correlation was identified between the Cally index and microorganisms. In conclusion, we utilized microbial biomarkers alongside the Cally to establish a diagnostic model for epithelial ovarian cancer, receiver operating characteristic (ROC) curve Area Under Curve (AUC) of 0.976 (95%CI 0.943-1.00), The AUC obtained from the Bootstrap internal validation was 0.974. The Hosmer-Lemeshow test revealed a robust concordance between the observed probabilities and the predicted probabilities generated by the model. The decision curve analysis revealed that the model provided a significant net clinical benefit. A total of 233 differential metabolites were identified between the EOC group and the NT (NOR and TB) groups. Among these, eight specific metabolites (HMDB0243492, C09265, HMDB0242046, HMDB0240606, C04171, HMDB0060557, HMDB0252797, and C21412) were exclusively derived from the microbiome. Notably, metabolite HMDB0240606 exhibited a significant positive correlation with Escherichia coli and Shigella, while it showed a significant negative correlation with Ruminococcus. In vitro studies demonstrated that Bifidobacterium possessed anti-tumor activity, whereas Escherichia coli exhibited pro-tumor activity.

This study provides the inaugural comprehensive analysis of gut microbiota composition and its differential profiles among patients with epithelial ovarian cancer, those with benign ovarian tumors, and healthy controls in Hunan province, China.