Dynamics of Fecal Microbiota with and without Invasive Cervical Cancer and Its Application in Early Diagnosis
The fecal microbiota is being increasingly implicated in the diagnosis of various diseases. However, evidence on changes in the fecal microbiota in invasive cervical cancer (ICC) remains scarce. Here, we aimed to investigate the fecal microbiota of our cohorts, develop a diagnostic model for predict...
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| Format: | Article |
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MDPI AG
2020-12-01
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| Series: | Cancers |
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| Online Access: | https://www.mdpi.com/2072-6694/12/12/3800 |
| _version_ | 1797544502859137024 |
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| author | Gi-Ung Kang Da-Ryung Jung Yoon Hee Lee Se Young Jeon Hyung Soo Han Gun Oh Chong Jae-Ho Shin |
| author_facet | Gi-Ung Kang Da-Ryung Jung Yoon Hee Lee Se Young Jeon Hyung Soo Han Gun Oh Chong Jae-Ho Shin |
| author_sort | Gi-Ung Kang |
| collection | DOAJ |
| description | The fecal microbiota is being increasingly implicated in the diagnosis of various diseases. However, evidence on changes in the fecal microbiota in invasive cervical cancer (ICC) remains scarce. Here, we aimed to investigate the fecal microbiota of our cohorts, develop a diagnostic model for predicting early ICC, and identify potential fecal microbiota-derived biomarkers using amplicon sequencing data. We obtained fecal samples from 29 healthy women (HC) and 17 women with clinically confirmed early ICC (CAN). Although Shannon’s diversity index was not reached at statistical significance, the Chao1 and Observed operational taxonomic units (OTUs) in fecal microbiota was significantly different between CAN and HC group. Furthermore, there were significant differences in the taxonomic profiles between HC and CAN; <i>Prevotella</i> was significantly more abundant in the CAN group and <i>Clostridium</i> in the HC group. Linear discriminant analysis effect size (LEfSe) analysis was applied to validate the taxonomic differences at the genus level. Furthermore, we identified a set of seven bacterial genera that were used to construct a machine learning (ML)-based classifier model to distinguish CAN from patients with HC. The model had high diagnostic utility (area under the curve [AUC] = 0.913) for predicting early ICC. Our study provides an initial step toward exploring the fecal microbiota and helps clinicians diagnose. |
| first_indexed | 2024-03-10T14:01:27Z |
| format | Article |
| id | doaj.art-dae16635995a44c2ae813317603ffe7c |
| institution | Directory Open Access Journal |
| issn | 2072-6694 |
| language | English |
| last_indexed | 2024-03-10T14:01:27Z |
| publishDate | 2020-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Cancers |
| spelling | doaj.art-dae16635995a44c2ae813317603ffe7c2023-11-21T01:10:04ZengMDPI AGCancers2072-66942020-12-011212380010.3390/cancers12123800Dynamics of Fecal Microbiota with and without Invasive Cervical Cancer and Its Application in Early DiagnosisGi-Ung Kang0Da-Ryung Jung1Yoon Hee Lee2Se Young Jeon3Hyung Soo Han4Gun Oh Chong5Jae-Ho Shin6Department of Applied Biosciences, Kyungpook National University, Daegu 41566, KoreaDepartment of Biomedical Convergence Science & Technology, Kyungpook National University, Daegu 41566, KoreaDepartment of Obstetrics and Gynecology, School of Medicine, Kyungpook National University, Daegu 41404, KoreaDepartment of Obstetrics and Gynecology, Kyungpook National University Chilgok Hospital, Daegu 41404, KoreaClinical Omics Research Center, School of Medicine, Kyungpook National University, Daegu 41940, KoreaDepartment of Obstetrics and Gynecology, School of Medicine, Kyungpook National University, Daegu 41404, KoreaDepartment of Applied Biosciences, Kyungpook National University, Daegu 41566, KoreaThe fecal microbiota is being increasingly implicated in the diagnosis of various diseases. However, evidence on changes in the fecal microbiota in invasive cervical cancer (ICC) remains scarce. Here, we aimed to investigate the fecal microbiota of our cohorts, develop a diagnostic model for predicting early ICC, and identify potential fecal microbiota-derived biomarkers using amplicon sequencing data. We obtained fecal samples from 29 healthy women (HC) and 17 women with clinically confirmed early ICC (CAN). Although Shannon’s diversity index was not reached at statistical significance, the Chao1 and Observed operational taxonomic units (OTUs) in fecal microbiota was significantly different between CAN and HC group. Furthermore, there were significant differences in the taxonomic profiles between HC and CAN; <i>Prevotella</i> was significantly more abundant in the CAN group and <i>Clostridium</i> in the HC group. Linear discriminant analysis effect size (LEfSe) analysis was applied to validate the taxonomic differences at the genus level. Furthermore, we identified a set of seven bacterial genera that were used to construct a machine learning (ML)-based classifier model to distinguish CAN from patients with HC. The model had high diagnostic utility (area under the curve [AUC] = 0.913) for predicting early ICC. Our study provides an initial step toward exploring the fecal microbiota and helps clinicians diagnose.https://www.mdpi.com/2072-6694/12/12/3800machine learninggut microbiomevaginal microbiomeprediction |
| spellingShingle | Gi-Ung Kang Da-Ryung Jung Yoon Hee Lee Se Young Jeon Hyung Soo Han Gun Oh Chong Jae-Ho Shin Dynamics of Fecal Microbiota with and without Invasive Cervical Cancer and Its Application in Early Diagnosis Cancers machine learning gut microbiome vaginal microbiome prediction |
| title | Dynamics of Fecal Microbiota with and without Invasive Cervical Cancer and Its Application in Early Diagnosis |
| title_full | Dynamics of Fecal Microbiota with and without Invasive Cervical Cancer and Its Application in Early Diagnosis |
| title_fullStr | Dynamics of Fecal Microbiota with and without Invasive Cervical Cancer and Its Application in Early Diagnosis |
| title_full_unstemmed | Dynamics of Fecal Microbiota with and without Invasive Cervical Cancer and Its Application in Early Diagnosis |
| title_short | Dynamics of Fecal Microbiota with and without Invasive Cervical Cancer and Its Application in Early Diagnosis |
| title_sort | dynamics of fecal microbiota with and without invasive cervical cancer and its application in early diagnosis |
| topic | machine learning gut microbiome vaginal microbiome prediction |
| url | https://www.mdpi.com/2072-6694/12/12/3800 |
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