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

作者：Cheng Chen, Chengyuan Deng, Yanwen Li, Shuguang He, Yunhong Liu, Shuwen Pan, Wenqian Xu, Fang Lü, Yixi Zhu, Yingying Wang, Xiaoxin Jiang · 发表于：Journal of Translational Medicine · 年份：2025 · DOI：10.1186/s12967-025-06339-z · 被引用次数：15 · 研究领域：Gut microbiota and health、Ferroptosis and cancer prognosis、Cancer Research and Treatments

BACKGROUND: 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. METHODS: 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 ...