Systematic analyses uncover robust salivary microbial signatures and host-microbiome perturbations in oral squamous cell carcinoma

The oral cavity hosts a diverse microbial community that plays a crucial role in systemic and oral health. Accumulated research has investigated significant differences in the saliva microbiota associated with oral cancer, suggesting that microbiome dysbiosis may contribute to the pathogenesis of or...

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Published inmSystems Vol. 10; no. 2; p. e0124724
Main Authors Han, Zewen, Hu, Yichen, Lin, Xin, Cheng, Hongyu, Dong, Biao, Liu, Xuan, Wu, Buling, Xu, Zhenjiang Zech
Format Journal Article
LanguageEnglish
Published United States American Society for Microbiology 18.02.2025
Subjects
age
Biomarkers
Cancer
Carcinoma, Squamous Cell - microbiology
Clinical Microbiology
Dysbacteriosis
Dysbiosis - microbiology
Female
Gender
Host Microbial Interactions
Humans
Invasiveness
Learning algorithms
Machine learning
Male
Malignancy
Maxillofacial
Meta-analysis
Microbiomes
Microbiota
Microorganisms
Mouth Neoplasms - microbiology
Oral carcinoma
Oral cavity
Oral squamous cell carcinoma
Pathogenesis
Research Article
Saliva
Saliva - microbiology
salivary microbiome
Squamous cell carcinoma
Squamous Cell Carcinoma of Head and Neck - microbiology
meta-analysis
oral squamous cell carcinoma
gender
salivary microbiome
age
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Abstract The oral cavity hosts a diverse microbial community that plays a crucial role in systemic and oral health. Accumulated research has investigated significant differences in the saliva microbiota associated with oral cancer, suggesting that microbiome dysbiosis may contribute to the pathogenesis of oral squamous cell carcinoma (OSCC). However, the specific microbial alterations linked to OSCC remain controversial. This meta-analysis reveals robust salivary microbiome alterations. Machine learning models using differential operational taxonomic units accurately predicted OSCC status, highlighting the potential of the salivary microbiome as a non-invasive diagnostic biomarker. Interestingly, age- and gender-associated signatures in the normal salivary microbiome were disrupted in OSCC, suggesting perturbations in host-microbe interactions.
AbstractList The oral cavity hosts a diverse microbial community that plays a crucial role in systemic and oral health. Accumulated research has investigated significant differences in the saliva microbiota associated with oral cancer, suggesting that microbiome dysbiosis may contribute to the pathogenesis of oral squamous cell carcinoma (OSCC). However, the specific microbial alterations linked to OSCC remain controversial. This meta-analysis reveals robust salivary microbiome alterations. Machine learning models using differential operational taxonomic units accurately predicted OSCC status, highlighting the potential of the salivary microbiome as a non-invasive diagnostic biomarker. Interestingly, age- and gender-associated signatures in the normal salivary microbiome were disrupted in OSCC, suggesting perturbations in host-microbe interactions.
ABSTRACT Oral squamous cell carcinoma (OSCC) is a prevalent malignancy in the oral-maxillofacial region with a poor prognosis. Oral microbiomes play a potential role in the pathogenesis of this disease. However, findings from individual studies have been inconsistent, and a comprehensive understanding of OSCC-associated microbiome dysbiosis remains elusive. Here, we conducted a large-scale meta-analysis by integrating 11 publicly available data sets comprising salivary microbiome profiles of OSCC patients and healthy controls. After correcting for batch effects, we observed significantly elevated alpha diversity and distinct beta-diversity patterns in the OSCC salivary microbiome compared to healthy controls. Leveraging random effects models, we identified robust microbial signatures associated with OSCC across data sets, including enrichment of taxa such as Streptococcus, Lactobacillus, Prevotella, Bulleidia moorei, and Haemophilus in OSCC samples. The machine learning models constructed from these microbial markers accurately predicted OSCC status, highlighting their potential as non-invasive diagnostic biomarkers. Intriguingly, our analyses revealed that the age- and gender-associated signatures in normal saliva microbiome were disrupted in the OSCC, suggesting perturbations in the intricate host-microbe interactions. Collectively, our findings uncovered complex alterations in the oral microbiome in OSCC, providing novel insights into disease etiology and paving the way for microbiome-based diagnostic and therapeutic strategies. Given that the salivary microbiome can reflect the overall health status of the host and that saliva sampling is a safe, non-invasive approach, it may be worthwhile to conduct broader screening of the salivary microbiome in high-risk OSCC populations as implications for early detection.IMPORTANCEThe oral cavity hosts a diverse microbial community that plays a crucial role in systemic and oral health. Accumulated research has investigated significant differences in the saliva microbiota associated with oral cancer, suggesting that microbiome dysbiosis may contribute to the pathogenesis of oral squamous cell carcinoma (OSCC). However, the specific microbial alterations linked to OSCC remain controversial. This meta-analysis reveals robust salivary microbiome alterations. Machine learning models using differential operational taxonomic units accurately predicted OSCC status, highlighting the potential of the salivary microbiome as a non-invasive diagnostic biomarker. Interestingly, age- and gender-associated signatures in the normal salivary microbiome were disrupted in OSCC, suggesting perturbations in host-microbe interactions.
ABSTRACTOral squamous cell carcinoma (OSCC) is a prevalent malignancy in the oral-maxillofacial region with a poor prognosis. Oral microbiomes play a potential role in the pathogenesis of this disease. However, findings from individual studies have been inconsistent, and a comprehensive understanding of OSCC-associated microbiome dysbiosis remains elusive. Here, we conducted a large-scale meta-analysis by integrating 11 publicly available data sets comprising salivary microbiome profiles of OSCC patients and healthy controls. After correcting for batch effects, we observed significantly elevated alpha diversity and distinct beta-diversity patterns in the OSCC salivary microbiome compared to healthy controls. Leveraging random effects models, we identified robust microbial signatures associated with OSCC across data sets, including enrichment of taxa such as Streptococcus, Lactobacillus, Prevotella, Bulleidia moorei, and Haemophilus in OSCC samples. The machine learning models constructed from these microbial markers accurately predicted OSCC status, highlighting their potential as non-invasive diagnostic biomarkers. Intriguingly, our analyses revealed that the age- and gender-associated signatures in normal saliva microbiome were disrupted in the OSCC, suggesting perturbations in the intricate host-microbe interactions. Collectively, our findings uncovered complex alterations in the oral microbiome in OSCC, providing novel insights into disease etiology and paving the way for microbiome-based diagnostic and therapeutic strategies. Given that the salivary microbiome can reflect the overall health status of the host and that saliva sampling is a safe, non-invasive approach, it may be worthwhile to conduct broader screening of the salivary microbiome in high-risk OSCC populations as implications for early detection.IMPORTANCEThe oral cavity hosts a diverse microbial community that plays a crucial role in systemic and oral health. Accumulated research has investigated significant differences in the saliva microbiota associated with oral cancer, suggesting that microbiome dysbiosis may contribute to the pathogenesis of oral squamous cell carcinoma (OSCC). However, the specific microbial alterations linked to OSCC remain controversial. This meta-analysis reveals robust salivary microbiome alterations. Machine learning models using differential operational taxonomic units accurately predicted OSCC status, highlighting the potential of the salivary microbiome as a non-invasive diagnostic biomarker. Interestingly, age- and gender-associated signatures in the normal salivary microbiome were disrupted in OSCC, suggesting perturbations in host-microbe interactions.
Oral squamous cell carcinoma (OSCC) is a prevalent malignancy in the oral-maxillofacial region with a poor prognosis. Oral microbiomes play a potential role in the pathogenesis of this disease. However, findings from individual studies have been inconsistent, and a comprehensive understanding of OSCC-associated microbiome dysbiosis remains elusive. Here, we conducted a large-scale meta-analysis by integrating 11 publicly available data sets comprising salivary microbiome profiles of OSCC patients and healthy controls. After correcting for batch effects, we observed significantly elevated alpha diversity and distinct beta-diversity patterns in the OSCC salivary microbiome compared to healthy controls. Leveraging random effects models, we identified robust microbial signatures associated with OSCC across data sets, including enrichment of taxa such as Streptococcus , Lactobacillus , Prevotella , Bulleidia moorei , and Haemophilus in OSCC samples. The machine learning models constructed from these microbial markers accurately predicted OSCC status, highlighting their potential as non-invasive diagnostic biomarkers. Intriguingly, our analyses revealed that the age- and gender-associated signatures in normal saliva microbiome were disrupted in the OSCC, suggesting perturbations in the intricate host-microbe interactions. Collectively, our findings uncovered complex alterations in the oral microbiome in OSCC, providing novel insights into disease etiology and paving the way for microbiome-based diagnostic and therapeutic strategies. Given that the salivary microbiome can reflect the overall health status of the host and that saliva sampling is a safe, non-invasive approach, it may be worthwhile to conduct broader screening of the salivary microbiome in high-risk OSCC populations as implications for early detection.
Oral squamous cell carcinoma (OSCC) is a prevalent malignancy in the oral-maxillofacial region with a poor prognosis. Oral microbiomes play a potential role in the pathogenesis of this disease. However, findings from individual studies have been inconsistent, and a comprehensive understanding of OSCC-associated microbiome dysbiosis remains elusive. Here, we conducted a large-scale meta-analysis by integrating 11 publicly available data sets comprising salivary microbiome profiles of OSCC patients and healthy controls. After correcting for batch effects, we observed significantly elevated alpha diversity and distinct beta-diversity patterns in the OSCC salivary microbiome compared to healthy controls. Leveraging random effects models, we identified robust microbial signatures associated with OSCC across data sets, including enrichment of taxa such as Streptococcus, Lactobacillus, Prevotella, Bulleidia moorei, and Haemophilus in OSCC samples. The machine learning models constructed from these microbial markers accurately predicted OSCC status, highlighting their potential as non-invasive diagnostic biomarkers. Intriguingly, our analyses revealed that the age- and gender-associated signatures in normal saliva microbiome were disrupted in the OSCC, suggesting perturbations in the intricate host-microbe interactions. Collectively, our findings uncovered complex alterations in the oral microbiome in OSCC, providing novel insights into disease etiology and paving the way for microbiome-based diagnostic and therapeutic strategies. Given that the salivary microbiome can reflect the overall health status of the host and that saliva sampling is a safe, non-invasive approach, it may be worthwhile to conduct broader screening of the salivary microbiome in high-risk OSCC populations as implications for early detection.IMPORTANCEThe oral cavity hosts a diverse microbial community that plays a crucial role in systemic and oral health. Accumulated research has investigated significant differences in the saliva microbiota associated with oral cancer, suggesting that microbiome dysbiosis may contribute to the pathogenesis of oral squamous cell carcinoma (OSCC). However, the specific microbial alterations linked to OSCC remain controversial. This meta-analysis reveals robust salivary microbiome alterations. Machine learning models using differential operational taxonomic units accurately predicted OSCC status, highlighting the potential of the salivary microbiome as a non-invasive diagnostic biomarker. Interestingly, age- and gender-associated signatures in the normal salivary microbiome were disrupted in OSCC, suggesting perturbations in host-microbe interactions.
Oral squamous cell carcinoma (OSCC) is a prevalent malignancy in the oral-maxillofacial region with a poor prognosis. Oral microbiomes play a potential role in the pathogenesis of this disease. However, findings from individual studies have been inconsistent, and a comprehensive understanding of OSCC-associated microbiome dysbiosis remains elusive. Here, we conducted a large-scale meta-analysis by integrating 11 publicly available data sets comprising salivary microbiome profiles of OSCC patients and healthy controls. After correcting for batch effects, we observed significantly elevated alpha diversity and distinct beta-diversity patterns in the OSCC salivary microbiome compared to healthy controls. Leveraging random effects models, we identified robust microbial signatures associated with OSCC across data sets, including enrichment of taxa such as Streptococcus, Lactobacillus, Prevotella, Bulleidia moorei, and Haemophilus in OSCC samples. The machine learning models constructed from these microbial markers accurately predicted OSCC status, highlighting their potential as non-invasive diagnostic biomarkers. Intriguingly, our analyses revealed that the age- and gender-associated signatures in normal saliva microbiome were disrupted in the OSCC, suggesting perturbations in the intricate host-microbe interactions. Collectively, our findings uncovered complex alterations in the oral microbiome in OSCC, providing novel insights into disease etiology and paving the way for microbiome-based diagnostic and therapeutic strategies. Given that the salivary microbiome can reflect the overall health status of the host and that saliva sampling is a safe, non-invasive approach, it may be worthwhile to conduct broader screening of the salivary microbiome in high-risk OSCC populations as implications for early detection.Oral squamous cell carcinoma (OSCC) is a prevalent malignancy in the oral-maxillofacial region with a poor prognosis. Oral microbiomes play a potential role in the pathogenesis of this disease. However, findings from individual studies have been inconsistent, and a comprehensive understanding of OSCC-associated microbiome dysbiosis remains elusive. Here, we conducted a large-scale meta-analysis by integrating 11 publicly available data sets comprising salivary microbiome profiles of OSCC patients and healthy controls. After correcting for batch effects, we observed significantly elevated alpha diversity and distinct beta-diversity patterns in the OSCC salivary microbiome compared to healthy controls. Leveraging random effects models, we identified robust microbial signatures associated with OSCC across data sets, including enrichment of taxa such as Streptococcus, Lactobacillus, Prevotella, Bulleidia moorei, and Haemophilus in OSCC samples. The machine learning models constructed from these microbial markers accurately predicted OSCC status, highlighting their potential as non-invasive diagnostic biomarkers. Intriguingly, our analyses revealed that the age- and gender-associated signatures in normal saliva microbiome were disrupted in the OSCC, suggesting perturbations in the intricate host-microbe interactions. Collectively, our findings uncovered complex alterations in the oral microbiome in OSCC, providing novel insights into disease etiology and paving the way for microbiome-based diagnostic and therapeutic strategies. Given that the salivary microbiome can reflect the overall health status of the host and that saliva sampling is a safe, non-invasive approach, it may be worthwhile to conduct broader screening of the salivary microbiome in high-risk OSCC populations as implications for early detection.The oral cavity hosts a diverse microbial community that plays a crucial role in systemic and oral health. Accumulated research has investigated significant differences in the saliva microbiota associated with oral cancer, suggesting that microbiome dysbiosis may contribute to the pathogenesis of oral squamous cell carcinoma (OSCC). However, the specific microbial alterations linked to OSCC remain controversial. This meta-analysis reveals robust salivary microbiome alterations. Machine learning models using differential operational taxonomic units accurately predicted OSCC status, highlighting the potential of the salivary microbiome as a non-invasive diagnostic biomarker. Interestingly, age- and gender-associated signatures in the normal salivary microbiome were disrupted in OSCC, suggesting perturbations in host-microbe interactions.IMPORTANCEThe oral cavity hosts a diverse microbial community that plays a crucial role in systemic and oral health. Accumulated research has investigated significant differences in the saliva microbiota associated with oral cancer, suggesting that microbiome dysbiosis may contribute to the pathogenesis of oral squamous cell carcinoma (OSCC). However, the specific microbial alterations linked to OSCC remain controversial. This meta-analysis reveals robust salivary microbiome alterations. Machine learning models using differential operational taxonomic units accurately predicted OSCC status, highlighting the potential of the salivary microbiome as a non-invasive diagnostic biomarker. Interestingly, age- and gender-associated signatures in the normal salivary microbiome were disrupted in OSCC, suggesting perturbations in host-microbe interactions.
Oral squamous cell carcinoma (OSCC) is a prevalent malignancy in the oral-maxillofacial region with a poor prognosis. Oral microbiomes play a potential role in the pathogenesis of this disease. However, findings from individual studies have been inconsistent, and a comprehensive understanding of OSCC-associated microbiome dysbiosis remains elusive. Here, we conducted a large-scale meta-analysis by integrating 11 publicly available data sets comprising salivary microbiome profiles of OSCC patients and healthy controls. After correcting for batch effects, we observed significantly elevated alpha diversity and distinct beta-diversity patterns in the OSCC salivary microbiome compared to healthy controls. Leveraging random effects models, we identified robust microbial signatures associated with OSCC across data sets, including enrichment of taxa such as , , , , and in OSCC samples. The machine learning models constructed from these microbial markers accurately predicted OSCC status, highlighting their potential as non-invasive diagnostic biomarkers. Intriguingly, our analyses revealed that the age- and gender-associated signatures in normal saliva microbiome were disrupted in the OSCC, suggesting perturbations in the intricate host-microbe interactions. Collectively, our findings uncovered complex alterations in the oral microbiome in OSCC, providing novel insights into disease etiology and paving the way for microbiome-based diagnostic and therapeutic strategies. Given that the salivary microbiome can reflect the overall health status of the host and that saliva sampling is a safe, non-invasive approach, it may be worthwhile to conduct broader screening of the salivary microbiome in high-risk OSCC populations as implications for early detection. The oral cavity hosts a diverse microbial community that plays a crucial role in systemic and oral health. Accumulated research has investigated significant differences in the saliva microbiota associated with oral cancer, suggesting that microbiome dysbiosis may contribute to the pathogenesis of oral squamous cell carcinoma (OSCC). However, the specific microbial alterations linked to OSCC remain controversial. This meta-analysis reveals robust salivary microbiome alterations. Machine learning models using differential operational taxonomic units accurately predicted OSCC status, highlighting the potential of the salivary microbiome as a non-invasive diagnostic biomarker. Interestingly, age- and gender-associated signatures in the normal salivary microbiome were disrupted in OSCC, suggesting perturbations in host-microbe interactions.
Author Hu, Yichen
Lin, Xin
Liu, Xuan
Wu, Buling
Han, Zewen
Dong, Biao
Cheng, Hongyu
Xu, Zhenjiang Zech
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Keywords meta-analysis
oral squamous cell carcinoma
gender
salivary microbiome
age
Language English
License This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license. https://creativecommons.org/licenses/by/4.0
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The authors declare no conflict of interest.
Zewen Han and Yichen Hu contributed equally to this article. Author order was determined by drawing straws.
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Snippet The oral cavity hosts a diverse microbial community that plays a crucial role in systemic and oral health. Accumulated research has investigated significant...
Oral squamous cell carcinoma (OSCC) is a prevalent malignancy in the oral-maxillofacial region with a poor prognosis. Oral microbiomes play a potential role in...
ABSTRACTOral squamous cell carcinoma (OSCC) is a prevalent malignancy in the oral-maxillofacial region with a poor prognosis. Oral microbiomes play a potential...
ABSTRACT Oral squamous cell carcinoma (OSCC) is a prevalent malignancy in the oral-maxillofacial region with a poor prognosis. Oral microbiomes play a...
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Open Access Repository
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StartPage e0124724
SubjectTerms age
Biomarkers
Cancer
Carcinoma, Squamous Cell - microbiology
Clinical Microbiology
Dysbacteriosis
Dysbiosis - microbiology
Female
Gender
Host Microbial Interactions
Humans
Invasiveness
Learning algorithms
Machine learning
Male
Malignancy
Maxillofacial
Meta-analysis
Microbiomes
Microbiota
Microorganisms
Mouth Neoplasms - microbiology
Oral carcinoma
Oral cavity
Oral squamous cell carcinoma
Pathogenesis
Research Article
Saliva
Saliva - microbiology
salivary microbiome
Squamous cell carcinoma
Squamous Cell Carcinoma of Head and Neck - microbiology
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Title Systematic analyses uncover robust salivary microbial signatures and host-microbiome perturbations in oral squamous cell carcinoma
URI https://www.ncbi.nlm.nih.gov/pubmed/39873508
https://journals.asm.org/doi/10.1128/msystems.01247-24
https://www.proquest.com/docview/3203849855
https://www.proquest.com/docview/3160461603
https://pubmed.ncbi.nlm.nih.gov/PMC11834404
https://doaj.org/article/5f70153840c74f94b2eb6777bfea49f2
Volume 10
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