<i>Streptococcus gallolyticus</i> Group Bacteremia and Colonic Adenocarcinoma

McAuliffe, Agnes; Bhat, Varun; Cox, Jaclyn

doi:10.7556/jaoa.2019.011

Cited by 2 publications

(2 citation statements)

References 2 publications

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“…Fusobacterium nucleatum has been reported to promote glycolysis and oncogenesis in CRC by upregulating the lncRNA ENO1-IT1 (Hong et al, 2021 ) and promoting CRC cell migration by modulating the long non-coding RNAs keratin7-antisense (KRT7-AS) and keratin7 (KRT7) (Chen et al, 2020 ). Streptococcus is also a common pathogenic genus that often causes inflammation and bacteremia, possibly promoting CRC (Kwong et al, 2018 ; McAuliffe et al, 2019 ). Peptostreptococcus is an anaerobic, gram-positive bacterium, and Peptostreptococcus anaerobius has been reported to promote CRC and modulate tumor immunity (Long et al, 2019 ).…”

Section: Discussionmentioning

confidence: 99%

An independent evaluation in a CRC patient cohort of microbiome 16S rRNA sequence analysis methods: OTU clustering, DADA2, and Deblur

Liu

Zhu

et al. 2023

Front. Microbiol.

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16S rRNA is the universal gene of microbes, and it is often used as a target gene to obtain profiles of microbial communities via next-generation sequencing (NGS) technology. Traditionally, sequences are clustered into operational taxonomic units (OTUs) at a 97% threshold based on the taxonomic standard using 16S rRNA, and methods for the reduction of sequencing errors are bypassed, which may lead to false classification units. Several denoising algorithms have been published to solve this problem, such as DADA2 and Deblur, which can correct sequencing errors at single-nucleotide resolution by generating amplicon sequence variants (ASVs). As high-resolution ASVs are becoming more popular than OTUs and only one analysis method is usually selected in a particular study, there is a need for a thorough comparison of OTU clustering and denoising pipelines. In this study, three of the most widely used 16S rRNA methods (two denoising algorithms, DADA2 and Deblur, along with de novo OTU clustering) were thoroughly compared using 16S rRNA amplification sequencing data generated from 358 clinical stool samples from the Colorectal Cancer (CRC) Screening Cohort. Our findings indicated that all approaches led to similar taxonomic profiles (with P > 0.05 in PERMNAOVA and P <0.001 in the Mantel test), although the number of ASVs/OTUs and the alpha-diversity indices varied considerably. Despite considerable differences in disease-related markers identified, disease-related analysis showed that all methods could result in similar conclusions. Fusobacterium, Streptococcus, Peptostreptococcus, Parvimonas, Gemella, and Haemophilus were identified by all three methods as enriched in the CRC group, while Roseburia, Faecalibacterium, Butyricicoccus, and Blautia were identified by all three methods as enriched in the healthy group. In addition, disease-diagnostic models generated using machine learning algorithms based on the data from these different methods all achieved good diagnostic efficiency (AUC: 0.87–0.89), with the model based on DADA2 producing the highest AUC (0.8944 and 0.8907 in the training set and test set, respectively). However, there was no significant difference in performance between the models (P >0.05). In conclusion, this study demonstrates that DADA2, Deblur, and de novo OTU clustering display similar power levels in taxa assignment and can produce similar conclusions in the case of the CRC cohort.

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Section: Discussionmentioning

confidence: 99%

An independent evaluation in a CRC patient cohort of microbiome 16S rRNA sequence analysis methods: OTU clustering, DADA2, and Deblur

Liu

Zhu

et al. 2023

Front. Microbiol.

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“…73 It is suspected that Streptococcus gallolyticus causes colorectal cancer. 74,75 Interestingly, Streptococcus mitis were suggested as diagnostic marker which were able to predict 80% of oral cancer cases. 76 Recently, there has been an increased interest in the possible role of oral microbiota in the development of OSCC.…”

Section: Bacteria and Oral Cancermentioning

confidence: 99%

Oral microbiota and oral cancer: Review

et al. 2020

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In this review, we draw attention and discuss the risk factors and causes of the development of oral squamous cell carcinoma (OSCC) focusing on oral microbiota. Recently, a breakthrough in the study of cancer has been the discovery of the relationship between the presence of certain types of bacteria and the development of cancer in the human body. Studies have shown that, Porphyromonas gingivalis (P. gingivalis) bacteria that is responsible for the destructive processes in the oral cavity, could play an important role in the development of OSCC. In our continuing search for bacteria that causes oral squamous cell carcinoma, we came across the Pseudomona aeruginosa, which due to its metabolite properties, may play important role in carcinogenesis of oral cancer. One possible mechanism is the ability of Pseudomonas to synthesize nitric oxide (NO) that modulates different cancer-related appearances such as apoptosis, cell cycle, angiogenesis, invasion, and metastasis. We think that P. aeruginosa increases the concentration of NO by converting salivary nitrite to nitric oxide, and this is how it contributes to NO-related carcinogenesis. Early diagnosis and treatment of periodontitis are very important not only for patients’ oral health, but also for the prevention of OSCC development. Screening test for OSCC based on determination of salivary NO levels could be appealing and may prove to be useful assay for diagnosis and early detection of disease progression in oral cancer.

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Streptococcus gallolyticus Group Bacteremia and Colonic Adenocarcinoma

Cited by 2 publications

References 2 publications

An independent evaluation in a CRC patient cohort of microbiome 16S rRNA sequence analysis methods: OTU clustering, DADA2, and Deblur

An independent evaluation in a CRC patient cohort of microbiome 16S rRNA sequence analysis methods: OTU clustering, DADA2, and Deblur

Oral microbiota and oral cancer: Review

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