To investigate the vaginal microbiota signature of patients with gynecologic cancer and evaluate its diagnostic biomarker potential. We incorporated vaginal 16S rRNA-seq data from 529 women and utilized VSEARCH to analyze the raw data. α-Diversity was evaluated utilizing the Chao1, Shannon, and Simpson indices, and β-diversity was evaluated through principal component analysis using Bray-Curtis distances. Linear discriminant analysis effect size (LEfSe) was utilized to determine species differences between groups. A bacterial co-abundance network was constructed utilizing Spearman correlation analysis. A random forest model of gynecologic tumor risk based on genus was constructed and validated to test its diagnostic efficacy. In gynecologic cancer patients, vaginal α-diversity was significantly greater than in controls, and vaginal β-diversity was significantly separated from that of controls; there was no correlation between these characteristics and menopause status among the subject women. Women diagnosed with gynecological cancer exhibited a reduction in the abundance of vaginal Firmicutes and Lactobacillus, while an increase was observed in the proportions of Bacteroidetes, Proteobacteria, Prevotella, Streptococcus, and Anaerococcus. A random forest model constructed based on 56 genus achieved high accuracy (area under the curve = 84.96%) in gynecological cancer risk prediction. Furthermore, there were discrepancies observed in the community complexity of co-abundance networks between gynecologic cancer patients and the control group. Our study provides evidence that women with gynecologic cancer have a unique vaginal flora structure and microorganisms may be involved in the gynecologic carcinogenesis process. A gynecological cancer risk prediction model based on characteristic genera has good diagnostic value.

中文翻译：

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妇科癌症的高度特异性阴道微生物组特征

研究妇科癌症患者的阴道微生物群特征并评估其诊断生物标志物的潜力。我们整合了 529 名女性的阴道 16S rRNA-seq 数据，并利用 VSEARCH 分析原始数据。利用 Chao1、Shannon 和 Simpson 指数评估 α 多样性，并使用 Bray-Curtis 距离通过主成分分析评估 β 多样性。利用线性判别分析效应大小 (LEfSe) 来确定组间的物种差异。利用 Spearman 相关分析构建细菌共丰度网络。构建并验证了基于属的妇科肿瘤风险随机森林模型，以测试其诊断效果。在妇科癌症患者中，阴道α-多样性显着高于对照组，而阴道β-多样性则与对照组显着不同；这些特征与受试者女性的更年期状况之间没有相关性。被诊断患有妇科癌症的女性阴道分泌物丰度下降厚壁菌门和乳酸菌，同时观察到的比例有所增加拟杆菌门,变形菌门,普雷沃特拉,链球菌属， 和厌氧球菌属。基于56个属构建的随机森林模型在妇科癌症风险预测中取得了较高的准确率（曲线下面积=84.96%）。此外，妇科癌症患者和对照组之间的共丰度网络的群落复杂性存在差异。我们的研究提供证据表明，患有妇科癌症的女性具有独特的阴道菌群结构，微生物可能参与妇科癌变过程。基于特征属的妇科癌症风险预测模型具有良好的诊断价值。