| Autor: |
Han Mengzhen, Wang Na, Han Wenjie, Liu Xiaolin, Sun Tao, Xu Junnan |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Open Life Sciences, Vol 19, Iss 1, Pp 17-48 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2391-5412 |
| DOI: |
10.1515/biol-2022-0850 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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