The Clinical Potential of Oral Microbiota as a Screening Tool for Oral Squamous Cell Carcinomas
IntroductionThe oral squamous cell carcinoma (OSCC) is detrimental to patients’ physical and mental health. The prognosis of OSCC depends on the early diagnosis of OSCC in large populations.ObjectivesHere, the present study aimed to develop an early diagnostic model based on the relationship between...
Main Authors: | , , , , , , , , , , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Frontiers Media S.A.
2021-08-01
|
Series: | Frontiers in Cellular and Infection Microbiology |
Subjects: | |
Online Access: | https://www.frontiersin.org/articles/10.3389/fcimb.2021.728933/full |
id |
doaj-aa2e8d20f09f46838b5b469d412e0345 |
---|---|
record_format |
Article |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Xinxuan Zhou Yu Hao Yu Hao Xian Peng Bolei Li Bolei Li Qi Han Biao Ren Mingyun Li Longjiang Li Yi Li Guo Cheng Guo Cheng Jiyao Li Jiyao Li Yue Ma Xuedong Zhou Xuedong Zhou Lei Cheng Lei Cheng |
spellingShingle |
Xinxuan Zhou Yu Hao Yu Hao Xian Peng Bolei Li Bolei Li Qi Han Biao Ren Mingyun Li Longjiang Li Yi Li Guo Cheng Guo Cheng Jiyao Li Jiyao Li Yue Ma Xuedong Zhou Xuedong Zhou Lei Cheng Lei Cheng The Clinical Potential of Oral Microbiota as a Screening Tool for Oral Squamous Cell Carcinomas Frontiers in Cellular and Infection Microbiology oral microbiota OSCC machine learning methods diagnose sequencing |
author_facet |
Xinxuan Zhou Yu Hao Yu Hao Xian Peng Bolei Li Bolei Li Qi Han Biao Ren Mingyun Li Longjiang Li Yi Li Guo Cheng Guo Cheng Jiyao Li Jiyao Li Yue Ma Xuedong Zhou Xuedong Zhou Lei Cheng Lei Cheng |
author_sort |
Xinxuan Zhou |
title |
The Clinical Potential of Oral Microbiota as a Screening Tool for Oral Squamous Cell Carcinomas |
title_short |
The Clinical Potential of Oral Microbiota as a Screening Tool for Oral Squamous Cell Carcinomas |
title_full |
The Clinical Potential of Oral Microbiota as a Screening Tool for Oral Squamous Cell Carcinomas |
title_fullStr |
The Clinical Potential of Oral Microbiota as a Screening Tool for Oral Squamous Cell Carcinomas |
title_full_unstemmed |
The Clinical Potential of Oral Microbiota as a Screening Tool for Oral Squamous Cell Carcinomas |
title_sort |
clinical potential of oral microbiota as a screening tool for oral squamous cell carcinomas |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Cellular and Infection Microbiology |
issn |
2235-2988 |
publishDate |
2021-08-01 |
description |
IntroductionThe oral squamous cell carcinoma (OSCC) is detrimental to patients’ physical and mental health. The prognosis of OSCC depends on the early diagnosis of OSCC in large populations.ObjectivesHere, the present study aimed to develop an early diagnostic model based on the relationship between OSCC and oral microbiota.MethodsOverall, 164 samples were collected from 47 OSCC patients and 48 healthy individuals as controls, including saliva, subgingival plaque, the tumor surface, the control side (healthy mucosa), and tumor tissue. Based on 16S rDNA sequencing, data from all the five sites, and salivary samples only, two machine learning models were developed to diagnose OSCC.ResultsThe average diagnostic accuracy rates of five sites and saliva were 98.17% and 95.70%, respectively. Cross-validations showed estimated external prediction accuracies of 96.67% and 93.58%, respectively. The false-negative rate was 0%. Besides, it was shown that OSCC could be diagnosed on any one of the five sites. In this model, Actinobacteria, Fusobacterium, Moraxella, Bacillus, and Veillonella species exhibited strong correlations with OSCC.ConclusionThis study provided a noninvasive and inexpensive way to diagnose malignancy based on oral microbiota without radiation. Applying machine learning methods in microbiota data to diagnose OSCC constitutes an example of a microbial assistant diagnostic model for other malignancies. |
topic |
oral microbiota OSCC machine learning methods diagnose sequencing |
url |
https://www.frontiersin.org/articles/10.3389/fcimb.2021.728933/full |
work_keys_str_mv |
AT xinxuanzhou theclinicalpotentialoforalmicrobiotaasascreeningtoolfororalsquamouscellcarcinomas AT yuhao theclinicalpotentialoforalmicrobiotaasascreeningtoolfororalsquamouscellcarcinomas AT yuhao theclinicalpotentialoforalmicrobiotaasascreeningtoolfororalsquamouscellcarcinomas AT xianpeng theclinicalpotentialoforalmicrobiotaasascreeningtoolfororalsquamouscellcarcinomas AT boleili theclinicalpotentialoforalmicrobiotaasascreeningtoolfororalsquamouscellcarcinomas AT boleili theclinicalpotentialoforalmicrobiotaasascreeningtoolfororalsquamouscellcarcinomas AT qihan theclinicalpotentialoforalmicrobiotaasascreeningtoolfororalsquamouscellcarcinomas AT biaoren theclinicalpotentialoforalmicrobiotaasascreeningtoolfororalsquamouscellcarcinomas AT mingyunli theclinicalpotentialoforalmicrobiotaasascreeningtoolfororalsquamouscellcarcinomas AT longjiangli theclinicalpotentialoforalmicrobiotaasascreeningtoolfororalsquamouscellcarcinomas AT yili theclinicalpotentialoforalmicrobiotaasascreeningtoolfororalsquamouscellcarcinomas AT guocheng theclinicalpotentialoforalmicrobiotaasascreeningtoolfororalsquamouscellcarcinomas AT guocheng theclinicalpotentialoforalmicrobiotaasascreeningtoolfororalsquamouscellcarcinomas AT jiyaoli theclinicalpotentialoforalmicrobiotaasascreeningtoolfororalsquamouscellcarcinomas AT jiyaoli theclinicalpotentialoforalmicrobiotaasascreeningtoolfororalsquamouscellcarcinomas AT yuema theclinicalpotentialoforalmicrobiotaasascreeningtoolfororalsquamouscellcarcinomas AT xuedongzhou theclinicalpotentialoforalmicrobiotaasascreeningtoolfororalsquamouscellcarcinomas AT xuedongzhou theclinicalpotentialoforalmicrobiotaasascreeningtoolfororalsquamouscellcarcinomas AT leicheng theclinicalpotentialoforalmicrobiotaasascreeningtoolfororalsquamouscellcarcinomas AT leicheng theclinicalpotentialoforalmicrobiotaasascreeningtoolfororalsquamouscellcarcinomas AT xinxuanzhou clinicalpotentialoforalmicrobiotaasascreeningtoolfororalsquamouscellcarcinomas AT yuhao clinicalpotentialoforalmicrobiotaasascreeningtoolfororalsquamouscellcarcinomas AT yuhao clinicalpotentialoforalmicrobiotaasascreeningtoolfororalsquamouscellcarcinomas AT xianpeng clinicalpotentialoforalmicrobiotaasascreeningtoolfororalsquamouscellcarcinomas AT boleili clinicalpotentialoforalmicrobiotaasascreeningtoolfororalsquamouscellcarcinomas AT boleili clinicalpotentialoforalmicrobiotaasascreeningtoolfororalsquamouscellcarcinomas AT qihan clinicalpotentialoforalmicrobiotaasascreeningtoolfororalsquamouscellcarcinomas AT biaoren clinicalpotentialoforalmicrobiotaasascreeningtoolfororalsquamouscellcarcinomas AT mingyunli clinicalpotentialoforalmicrobiotaasascreeningtoolfororalsquamouscellcarcinomas AT longjiangli clinicalpotentialoforalmicrobiotaasascreeningtoolfororalsquamouscellcarcinomas AT yili clinicalpotentialoforalmicrobiotaasascreeningtoolfororalsquamouscellcarcinomas AT guocheng clinicalpotentialoforalmicrobiotaasascreeningtoolfororalsquamouscellcarcinomas AT guocheng clinicalpotentialoforalmicrobiotaasascreeningtoolfororalsquamouscellcarcinomas AT jiyaoli clinicalpotentialoforalmicrobiotaasascreeningtoolfororalsquamouscellcarcinomas AT jiyaoli clinicalpotentialoforalmicrobiotaasascreeningtoolfororalsquamouscellcarcinomas AT yuema clinicalpotentialoforalmicrobiotaasascreeningtoolfororalsquamouscellcarcinomas AT xuedongzhou clinicalpotentialoforalmicrobiotaasascreeningtoolfororalsquamouscellcarcinomas AT xuedongzhou clinicalpotentialoforalmicrobiotaasascreeningtoolfororalsquamouscellcarcinomas AT leicheng clinicalpotentialoforalmicrobiotaasascreeningtoolfororalsquamouscellcarcinomas AT leicheng clinicalpotentialoforalmicrobiotaasascreeningtoolfororalsquamouscellcarcinomas |
_version_ |
1721203570508824576 |
spelling |
doaj-aa2e8d20f09f46838b5b469d412e03452021-08-18T05:02:33ZengFrontiers Media S.A.Frontiers in Cellular and Infection Microbiology2235-29882021-08-011110.3389/fcimb.2021.728933728933The Clinical Potential of Oral Microbiota as a Screening Tool for Oral Squamous Cell CarcinomasXinxuan Zhou0Yu Hao1Yu Hao2Xian Peng3Bolei Li4Bolei Li5Qi Han6Biao Ren7Mingyun Li8Longjiang Li9Yi Li10Guo Cheng11Guo Cheng12Jiyao Li13Jiyao Li14Yue Ma15Xuedong Zhou16Xuedong Zhou17Lei Cheng18Lei Cheng19State Key Laboratory of Oral Diseases & West China Hospital of Stomatology & National Clinical Research Center for Oral Diseases, Sichuan University, Chengdu, ChinaState Key Laboratory of Oral Diseases & West China Hospital of Stomatology & National Clinical Research Center for Oral Diseases, Sichuan University, Chengdu, ChinaDepartment of Operative Dentistry and Endodontics, West China Hospital of Stomatology, Sichuan University, Chengdu, ChinaState Key Laboratory of Oral Diseases & West China Hospital of Stomatology & National Clinical Research Center for Oral Diseases, Sichuan University, Chengdu, ChinaState Key Laboratory of Oral Diseases & West China Hospital of Stomatology & National Clinical Research Center for Oral Diseases, Sichuan University, Chengdu, ChinaDepartment of Operative Dentistry and Endodontics, West China Hospital of Stomatology, Sichuan University, Chengdu, ChinaState Key Laboratory of Oral Diseases & West China Hospital of Stomatology & National Clinical Research Center for Oral Diseases, Sichuan University, Chengdu, ChinaState Key Laboratory of Oral Diseases & West China Hospital of Stomatology & National Clinical Research Center for Oral Diseases, Sichuan University, Chengdu, ChinaState Key Laboratory of Oral Diseases & West China Hospital of Stomatology & National Clinical Research Center for Oral Diseases, Sichuan University, Chengdu, ChinaState Key Laboratory of Oral Diseases & West China Hospital of Stomatology & National Clinical Research Center for Oral Diseases, Sichuan University, Chengdu, ChinaState Key Laboratory of Oral Diseases & West China Hospital of Stomatology & National Clinical Research Center for Oral Diseases, Sichuan University, Chengdu, ChinaWest China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, ChinaLaboratory of Molecular Translational Medicine, Centre for Translational Medicine, Key Laboratory of Birth Defects and Related Diseases of Women and Children, Ministry of Education, West China Second University Hospital, Sichuan University, Chengdu, ChinaState Key Laboratory of Oral Diseases & West China Hospital of Stomatology & National Clinical Research Center for Oral Diseases, Sichuan University, Chengdu, ChinaDepartment of Operative Dentistry and Endodontics, West China Hospital of Stomatology, Sichuan University, Chengdu, ChinaWest China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, ChinaState Key Laboratory of Oral Diseases & West China Hospital of Stomatology & National Clinical Research Center for Oral Diseases, Sichuan University, Chengdu, ChinaDepartment of Operative Dentistry and Endodontics, West China Hospital of Stomatology, Sichuan University, Chengdu, ChinaState Key Laboratory of Oral Diseases & West China Hospital of Stomatology & National Clinical Research Center for Oral Diseases, Sichuan University, Chengdu, ChinaDepartment of Operative Dentistry and Endodontics, West China Hospital of Stomatology, Sichuan University, Chengdu, ChinaIntroductionThe oral squamous cell carcinoma (OSCC) is detrimental to patients’ physical and mental health. The prognosis of OSCC depends on the early diagnosis of OSCC in large populations.ObjectivesHere, the present study aimed to develop an early diagnostic model based on the relationship between OSCC and oral microbiota.MethodsOverall, 164 samples were collected from 47 OSCC patients and 48 healthy individuals as controls, including saliva, subgingival plaque, the tumor surface, the control side (healthy mucosa), and tumor tissue. Based on 16S rDNA sequencing, data from all the five sites, and salivary samples only, two machine learning models were developed to diagnose OSCC.ResultsThe average diagnostic accuracy rates of five sites and saliva were 98.17% and 95.70%, respectively. Cross-validations showed estimated external prediction accuracies of 96.67% and 93.58%, respectively. The false-negative rate was 0%. Besides, it was shown that OSCC could be diagnosed on any one of the five sites. In this model, Actinobacteria, Fusobacterium, Moraxella, Bacillus, and Veillonella species exhibited strong correlations with OSCC.ConclusionThis study provided a noninvasive and inexpensive way to diagnose malignancy based on oral microbiota without radiation. Applying machine learning methods in microbiota data to diagnose OSCC constitutes an example of a microbial assistant diagnostic model for other malignancies.https://www.frontiersin.org/articles/10.3389/fcimb.2021.728933/fulloral microbiotaOSCCmachine learning methodsdiagnosesequencing |