Highly specific vaginal microbiome signature for gynecological cancers
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 Simp...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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De Gruyter
2024-04-01
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| Series: | Open Life Sciences |
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| Online Access: | https://doi.org/10.1515/biol-2022-0850 |
| _version_ | 1797197575836663808 |
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| author | Han Mengzhen Wang Na Han Wenjie Liu Xiaolin Sun Tao Xu Junnan |
| author_facet | Han Mengzhen Wang Na Han Wenjie Liu Xiaolin Sun Tao Xu Junnan |
| author_sort | Han Mengzhen |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-24T06:46:09Z |
| format | Article |
| id | doaj.art-178efb410ca4415099f9d71f592d574d |
| institution | Directory Open Access Journal |
| issn | 2391-5412 |
| language | English |
| last_indexed | 2024-04-24T06:46:09Z |
| publishDate | 2024-04-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Open Life Sciences |
| spelling | doaj.art-178efb410ca4415099f9d71f592d574d2024-04-22T19:39:30ZengDe GruyterOpen Life Sciences2391-54122024-04-01191174810.1515/biol-2022-0850Highly specific vaginal microbiome signature for gynecological cancersHan Mengzhen0Wang Na1Han Wenjie2Liu Xiaolin3Sun Tao4Xu Junnan5Department of Breast Medicine 1, Cancer Hospital of China Medical University, Liaoning Cancer Hospital, Shenyang110000, ChinaDepartment of Breast Medicine 1, Cancer Hospital of China Medical University, Liaoning Cancer Hospital, Shenyang110000, ChinaDepartment of Breast Medicine 1, Cancer Hospital of China Medical University, Liaoning Cancer Hospital, Shenyang110000, ChinaLiaoning Microhealth Biotechnology Co., Ltd, Shanlin Road, Dadong District, Shenyang110000, ChinaDepartment of Breast Medicine 1, Cancer Hospital of Dalian University of Technology, Liaoning Cancer Hospital and Institute, No. 44 Xiaoheyan Road, Dadong District, Shenyang, Liaoning110000, ChinaDepartment of Breast Medicine 1, Cancer Hospital of Dalian University of Technology, Liaoning Cancer Hospital and Institute, No. 44 Xiaoheyan Road, Dadong District, Shenyang, Liaoning110000, ChinaTo 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.https://doi.org/10.1515/biol-2022-0850gynecological cancer16s rrna-seqmicrobiomevaginarandom forest |
| spellingShingle | Han Mengzhen Wang Na Han Wenjie Liu Xiaolin Sun Tao Xu Junnan Highly specific vaginal microbiome signature for gynecological cancers Open Life Sciences gynecological cancer 16s rrna-seq microbiome vagina random forest |
| title | Highly specific vaginal microbiome signature for gynecological cancers |
| title_full | Highly specific vaginal microbiome signature for gynecological cancers |
| title_fullStr | Highly specific vaginal microbiome signature for gynecological cancers |
| title_full_unstemmed | Highly specific vaginal microbiome signature for gynecological cancers |
| title_short | Highly specific vaginal microbiome signature for gynecological cancers |
| title_sort | highly specific vaginal microbiome signature for gynecological cancers |
| topic | gynecological cancer 16s rrna-seq microbiome vagina random forest |
| url | https://doi.org/10.1515/biol-2022-0850 |
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