Dataset Information

DNA methylation signatures to predict the cervicovaginal microbiome status.

ABSTRACT:

Background

The composition of the microbiome plays an important role in human health and disease. Whether there is a direct association between the cervicovaginal microbiome and the host's epigenome is largely unexplored.

Results

Here we analyzed a total of 448 cervicovaginal smear samples and studied both the DNA methylome of the host and the microbiome using the Illumina EPIC array and next-generation sequencing, respectively. We found that those CpGs that are hypo-methylated in samples with non-lactobacilli (O-type) dominating communities are strongly associated with gastrointestinal differentiation and that a signature consisting of 819 CpGs was able to discriminate lactobacilli-dominating (L-type) from O-type samples with an area under the receiver operator characteristic curve (AUC) of 0.84 (95% CI = 0.77-0.90) in an independent validation set. The performance found in samples with more than 50% epithelial cells was further improved (AUC 0.87) and in women younger than 50 years of age was even higher (AUC 0.91). In a subset of 96 women, the buccal but not the blood cell DNA showed the same trend as the cervicovaginal samples in discriminating women with L- from O-type cervicovaginal communities.

Conclusions

These findings strongly support the view that the epithelial epigenome plays an essential role in hosting specific microbial communities.

SUBMITTER: Nene NR

PROVIDER: S-EPMC7686703 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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Publications

DNA methylation signatures to predict the cervicovaginal microbiome status.

Nené Nuno R NR Barrett James J Jones Allison A Evans Iona I Reisel Daniel D Timms John F JF Paprotka Tobias T Leimbach Andreas A Franchi Dorella D Colombo Nicoletta N Bjørge Line L Zikan Michal M Cibula David D Widschwendter Martin M

Clinical epigenetics 20201123 1

<h4>Background</h4>The composition of the microbiome plays an important role in human health and disease. Whether there is a direct association between the cervicovaginal microbiome and the host's epigenome is largely unexplored.<h4>Results</h4>Here we analyzed a total of 448 cervicovaginal smear samples and studied both the DNA methylome of the host and the microbiome using the Illumina EPIC array and next-generation sequencing, respectively. We found that those CpGs that are hypo-methylated in ...[more]

PMID: 33228781

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Project description:Prostate cancer (PCa) remains a significant global health challenge, with approximately 1.6 million new cases and 366,000 deaths annually. Despite high survival rates for localized prostate cancer, recurrence poses a substantial risk due to inherent biological factors and residual disease. Early detection and intervention are essential for enhancing patient outcomes and reducing mortality. However, traditional diagnostics such as PSA tests, digital rectal examinations, and biopsies often lack specificity resulting in overdiagnosis. There is a pressing need for novel biomarkers to enhance precision medicine approaches for PCa. This study employs a machine learning approach to identify DNA methylation and RNA expression biomarkers predictive of PCa recurrence using datasets from The Cancer Genome Atlas (TCGA). We analyzed 49,133 genes, identifying 684 differentially methylated genes (DMGs) and 691 differentially expressed genes (DEGs) between recurrence and non-recurrence groups. Ten genes (TNNI2, SPIN2, COL5A3, RNF169, CCND1, FGFR1, SLC17A2, FAMM71F2, RREB1, AOX1) were found to have significant correlations between methylation and expression, forming the basis for our predictive model. A support vector machine (SVM) model was developed using these ten genes, achieving an area under the curve (AUC) of 0.773, demonstrating robust predictive capability. Multivariate regression analysis confirmed the SVM score as an independent predictor of recurrence (HR = 0.45; 95% CI 0.28-0.69, P < 0.001). The analysis of recurrence-free survival suggested that patients with low-risk scores experienced significantly better outcomes compared to those with high-risk scores. Functional enrichment analyses of DMGs revealed significant involvement in biological processes such as transcription regulation, signal transduction, and immune response, highlighting the potential mechanistic pathways of these biomarkers. Validation using real-time PCR confirmed differential expression and methylation patterns of the identified genes in prostate cancer (PC3) and non-cancerous cell lines (PNT2). In conclusion, our study hihglights the DNA methylation biomarkers linked to PCa recurrence and introduces a promising SVM model for early prediction, potentially improving treatment outcomes. Further research is needed to explore the biological roles of these genes in PCa aiming to refine therapeutic approaches.

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Dataset Information

DNA methylation signatures to predict the cervicovaginal microbiome status.

Background

Results

Conclusions

Publications

DNA methylation signatures to predict the cervicovaginal microbiome status.

Similar Datasets

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