Dataset Information

Signatures of Mucosal Microbiome in Oral Squamous Cell Carcinoma Identified Using a Random Forest Model.

ABSTRACT: Objective:The aim of this study was to explore the signatures of oral microbiome associated with OSCC using a random forest (RF) model. Patients and Methods:A total of 24 patients with OSCC were enrolled in the study. The oral microbiome was assessed in cancerous lesions and matched paracancerous tissues from each patient using 16S rRNA gene sequencing. Signatures of mucosal microbiome in OSCC were identified using a RF model. Results:Significant differences were found between OSCC lesions and matched paracancerous tissues with respect to the microbial profile and composition. Linear discriminant analysis effect size analyses (LEfSe) identified 15 bacteria genera associated with cancerous lesions. Fusobacterium, Treponema, Streptococcus, Peptostreptococcus, Carnobacterium, Tannerella, Parvimonas and Filifactor were enriched. A classifier based on RF model identified a microbial signature comprising 12 bacteria, which was capable of distinguishing cancerous lesions and paracancerous tissues (AUC = 0.82). The network of the oral microbiome in cancerous lesions appeared to be simplified and fragmented. Functional analyses of oral microbiome showed altered functions in amino acid metabolism and increased capacity of glucose utilization in OSCC. Conclusion:The identified microbial signatures may potentially be used as a biomarker for predicting OSCC or for clinical assessment of oral cancer risk.

SUBMITTER: Zhou J

PROVIDER: S-EPMC7342497 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Signatures of Mucosal Microbiome in Oral Squamous Cell Carcinoma Identified Using a Random Forest Model.

Zhou Jianhua J Wang Lili L Yuan Rongtao R Yu Xinjuan X Chen Zhenggang Z Yang Fang F Sun Guirong G Dong Quanjiang Q

Cancer management and research 20200703

<h4>Objective</h4>The aim of this study was to explore the signatures of oral microbiome associated with OSCC using a random forest (RF) model.<h4>Patients and methods</h4>A total of 24 patients with OSCC were enrolled in the study. The oral microbiome was assessed in cancerous lesions and matched paracancerous tissues from each patient using 16S rRNA gene sequencing. Signatures of mucosal microbiome in OSCC were identified using a RF model.<h4>Results</h4>Significant differences were found betw ...[more]

PMID: 32753953

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Project description:Oral squamous cell carcinoma (OSCC) is the most common malignancy of the head and neck worldwide. Dysbiosis of the microbiome has increasingly been linked to the development of different kinds of cancer. Applying 16S rRNA gene sequence analysis and metatranscriptomic analyses, we characterized the longitudinal changes in the profiles and the function of the oral microbiome in a 4-nitroquinoline-1-oxide (4-NQO)-induced model of OSCC in gnotobiotic mice. We characterized the dynamics of the oral microbiome in this model using two different microbiome inocula: one from healthy mice and the other from mice bearing a 4-NQO-induced tumor. Mice colonized with different oral microbiomes and exposed to 4-NQO had increased tumor numbers and sizes compared to controls exposed to 4-NQO but lacking a microbiome. We observed an overall increase in diversity in the tumorigenic samples compared to that in the nontumor group not exposed to 4-NQO. Despite the variability in community dynamics, specific patterns emerged during the progression of the disease. In the two groups that were inoculated with the OSCC-associated microbiome, we observed opposite profiles of abundance in Parabacteroides and Corynebacterium While the percentage of Parabacteroides bacteria decreased in the control group, it increased in the OSCC group, and the opposite was observed for Corynebacterium The metatranscriptomic analysis revealed overexpression of the same metabolic signatures associated with OSCC regardless of the community profile. These included nitrogen transport, response to stress, interspecies interactions, Wnt pathway modulation, and amino acid and lipid biosynthesis. Thus, these results seem to suggest that certain collective physiological activities are critical for microbiome-mediated OSCC progression.IMPORTANCE There is growing evidence that changes in the microbiome are associated with carcinogenesis. To date, no consistent oral microbiome composition associated with OSCC has been identified. Longitudinal and functional studies like the study presented here should yield a better understanding of the role that the oral microbiome plays in OSCC. Our findings, obtained using a germ-free mouse model, indicate that the presence of different oral microbiomes enhances tumorigenesis and increases the final number of tumors in mice. By studying community-wide expression profiles, we found that regardless of the phylogenetic composition of the microbiome, the same metabolic activities were consistently associated with OSCC. Therefore, due to the functional redundancy of the microbiome, the critical element in explaining the contribution of the microbiota in OSCC is the collective physiological activity of the community, thus accounting for the previous inability to identify a consensus community profile or etiologic agents for OSCC.

Project description:BACKGROUND:Oral squamous cell carcinoma (OSCC) is a genetic and epigenetic disease. There is growing evidence to suggest that environmental factors due to epigenetic changes can be involved in the OSCC pathogenesis. Although tumor suppressor genes (TSGs) are commonly inactivated by promoter hypermethylation in human cancers, the epigenetic changes and the mechanism of TSGs in human OSCC remain unclear. We therefore assessed the methylation status of the TSGs, which are associated with epigenetic silencing in human cancers, OSCC cell lines, primary tumors, and normal oral mucosa. RESULTS:We used 14 TSGs that were originally identified in colon cancer to investigate the aberrant hypermethylation of these genes associated with transcriptional silencing in 10 OSCC cell lines. We found three TSGs, TFPI2, SOX17, and GATA4, that are robustly hypermethylated and are associated with transcriptional silencing in OSCC cell lines. The re-expression of the three genes was induced by 5-aza-2'-deoxycytidine (5-aza-dC) in cells in which these genes were not expressed or had a lack of expression. In 33 cases of primary OSCC tumors, promoter hypermethylation was detected for the TFPI2, SOX17, and GATA4 genes at (32/33) 97%, (22/33) 67%, and (11/33) 33%, respectively. Eleven normal oral mucosa samples showed no promoter hypermethylation for all three genes, which suggests that this promoter hypermethylation is cancer-specific. Bisulfite sequencing analysis confirmed the cancer-specific methylation of the TFPI2, SOX17, and GATA4 promoters in the OSCC cell lines and tumors but not in the normal oral mucosa samples. More importantly, the methylation status of TFPI2, GATA4, and SOX17 was significantly associated with OSCC patients' overall survival through TCGA DNA methylation database. CONCLUSIONS:We identified that TFPI2, SOX17, and GATA4 are frequently hypermethylated in human OSCC cells in a cancer-specific manner and that the transcriptional expression of these genes is regulated by promoter hypermethylation in OSCC. Our results highlight the great potential used as a synergistic biomarker set to improve the prognosis and therapeutic treatment for patients with OSCC.

Project description:Background Early-stage oral squamous cell carcinoma (OSCC) patients have a one-in-four risk of regional metastasis (LN+), which is also the most significant prognostic factor for survival. As there are no validated biomarkers for predicting LN+ in early-stage OSCC, elective neck dissection often leads to over-treatment and under-treatment. We present a machine-learning-based model using the quantitative nuclear phenotype of cancer cells from the primary tumor to predict the risk of nodal disease. Methods and findings Tumor specimens were obtained from 35 patients diagnosed with primary OSCC and received surgery with curative intent. Of the 35 patients, 29 had well (G1) or moderately (G2) differentiated tumors, and six had poorly differentiated tumors. From each, two consecutive sections were stained for hematoxylin & eosin and Feulgen-thionin staining. The slides were scanned, and images were processed to curate nuclear morphometric features for each nucleus, measuring nuclear morphology, DNA amount, and chromatin texture/organization. The nuclei (n = 384,041) from 15 G1 and 14 G2 tumors were randomly split into 80% training and 20% test set to build the predictive model by using Random Forest (RF) analysis which give each tumor cell a score, NRS. The area under ROC curve (AUC) was 99.6% and 90.7% for the training and test sets, respectively. At the cutoff score of 0.5 as the median NRS of each region of interest (n = 481), the AUC was 95.1%. We then developed a patient-level model based on the percentage of cells with an NRS ≥ 0.5. The prediction performance showed AUC of 97.7% among the 80% (n = 23 patient) training set and with the cutoff of 61% positive cells achieved 100% sensitivity and 91.7% specificity. When applying the 61% cutoff to the 20% test set patients, the model achieved 100% accuracy. Conclusions Our findings may have a clinical impact with an easy, accurate, and objective biomarker from routine pathology tissue, providing an unprecedented opportunity to improve neck management decisions in early-stage OSCC patients.

Dataset Information

Signatures of Mucosal Microbiome in Oral Squamous Cell Carcinoma Identified Using a Random Forest Model.

Publications

Signatures of Mucosal Microbiome in Oral Squamous Cell Carcinoma Identified Using a Random Forest Model.

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