Investigating the oral microbiome in retrospective and prospective cases of prostate, colon, and breast cancer.
Jacob T NearingVanessa DeClercqMorgan G I LangillePublished in: NPJ biofilms and microbiomes (2023)
The human microbiome has been proposed as a potentially useful biomarker for several cancers. To examine this, we made use of salivary samples from the Atlantic Partnership for Tomorrow's Health (PATH) project and Alberta's Tomorrow Project (ATP). Sample selection was divided into both a retrospective and prospective case control design examining prostate, breast, and colon cancer. In total 89 retrospective and 260 prospective cancer cases were matched to non-cancer controls and saliva samples were sequenced using 16S rRNA gene sequencing. We found no significant differences in alpha diversity. All beta diversity measures were insignificant except for unweighted UniFrac profiles in retrospective breast cancer cases and weighted UniFrac, Bray-Curtis and Robust Atchinson's distances in colon cancer after testing with age and sex adjusted MiRKAT models. Differential abundance (DA) analysis showed several taxa that were associated with previous cancer in all three groupings. Only one genus (Clostridia UCG-014) in breast cancer and one ASV (Fusobacterium periodonticum) in colon cancer was identified by more than one DA tool. In prospective cases three ASVs were associated with colon cancer, one ASV with breast cancer, and one ASV with prostate cancer. Random Forest classification showed low levels of signal in both study designs in breast and prostate cancer. Contrastingly, colon cancer did show signal in our retrospective analysis (AUC: 0.737) and in one of two prospective cohorts (AUC: 0.717). Our results indicate that it is unlikely that reliable microbial oral biomarkers for breast and prostate cancer exist.. However, further research into the oral microbiome and colon cancer could be fruitful.
Keyphrases
- prostate cancer
- radical prostatectomy
- papillary thyroid
- squamous cell
- childhood cancer
- healthcare
- cross sectional
- machine learning
- endothelial cells
- magnetic resonance
- computed tomography
- public health
- copy number
- squamous cell carcinoma
- deep learning
- young adults
- climate change
- microbial community
- gene expression
- antibiotic resistance genes
- wastewater treatment