In the oral cavity, complex microbial community is shaped by various host and environmental factors. Extensive literature describing the oral microbiome in the context of oral health and disease is available. Advances in DNA sequencing technologies and data analysis have drastically improved the analysis of the oral microbiome. For microbiome study, bacterial 16S ribosomal RNA gene amplification and sequencing is often employed owing to the cost-effective and fast nature of the method. In this review, practical considerations for performing a microbiome study, including experimental design, molecular analysis technology, and general data analysis, will be discussed.

Accurate identification of microbes facilitates the prediction, prevention, and treatment of human diseases. To increase the accuracy of microbiome data analysis, a long region of the 16S rRNA is commonly sequenced via paired-end sequencing. In paired-end sequencing, a sufficient length of overlapping region is required for effective joining of the reads, and high-quality sequencing reads are needed at the overlapping region. Trimming sequences at the reads distal to a point where sequencing quality drops below a specific threshold enhance the joining process. In this study, we examined the effect of trimming conditions on the number of reads that remained after quality control and chimera removal in the Illumina paired-end reads of the V3–V4 hypervariable region. We also examined the alpha diversity and taxa assigned by each trimming condition. Optimum quality trimming increased the number of good reads and assigned more number of operational taxonomy units. The pre-analysis trimming step has a great influence on further microbiome analysis, and optimized trimming conditions should be applied for Divisive Amplicon Denoising Algorithm 2 analysis in QIIME2 platform.

Accurate identification of microbes facilitates the prediction, prevention, and treatment of human diseases. To increase the accuracy of microbiome data analysis, a long region of the 16S rRNA is commonly sequenced via paired-end sequencing. In paired-end sequencing, a sufficient length of overlapping region is required for effective joining of the reads, and high-quality sequencing reads are needed at the overlapping region. Trimming sequences at the reads distal to a point where sequencing quality drops below a specific threshold enhance the joining process. In this study, we examined the effect of trimming conditions on the number of reads that remained after quality control and chimera removal in the Illumina paired-end reads of the V3–V4 hypervariable region. We also examined the alpha diversity and taxa assigned by each trimming condition. Optimum quality trimming increased the number of good reads and assigned more number of operational taxonomy units. The pre-analysis trimming step has a great influence on further microbiome analysis, and optimized trimming conditions should be applied for Divisive Amplicon Denoising Algorithm 2 analysis in QIIME2 platform.

Purpose: Colorectal cancer (CRC) is a common malignancy worldwide and the second leading cause of cancer-related deaths. In addition, lymph node metastasis in CRC is considered an important prognostic factor for predicting disease recurrence and patient survival. Recent studies demonstrated that the microbiome makes substantial contributions to tumor progression, however, there is still unknown about the microbiome associated with lymph node metastasis of CRC. Here, we first reported the microbial and tumor-infiltrating immune cell differences in CRC according to the lymph node metastasis status. Materials and Methods: Using Next Generation Sequencing data acquired from 368 individuals diagnosed with CRC (N0, 266; N1, 102), we applied the LEfSe to elucidate microbial differences. Subsequent utilization of the Kaplan-Meier survival analysis enabled the identification of particular genera exerting significant influence on patient survival outcomes. Results We found 18 genera in the N1 group and 3 genera in the N0 group according to CRC lymph node metastasis stages. In addition, we found that the genera Crenobacter (P=0.046), Maricaulis (P=0.093), and Arsenicicoccus (P=0.035) in the N0 group and Cecembia (P=0.08) and Asanoa (P=0.088) in the N1 group were significantly associated with patient survival according to CRC lymph node metastasis stages. Further, Cecembia is highly correlated to tumor-infiltrating immune cells in lymph node metastasized CRC.Concolusion: Our study highlights that tumor-infiltrating immune cells and intratumoral microbe diversity are associated with CRC. Also, this potential microbiome-based oncology diagnostic tool warrants further exploration.

Purpose: Triple-negative breast cancer (TNBC) is a breast cancer subtype that has poor prognosis and exhibits a unique tumor microenvironment. Analysis of the tumor microbiome has indicated a relationship between the tumor microenvironment and treatment response. Therefore, we attempted to reveal the role of the tumor microbiome in patients with TNBC receiving neoadjuvant chemotherapy. Materials and Methods: We collected TNBC patient RNA-sequencing samples from the Gene Expression Omnibus and extracted microbiome count data. Differential and relative abundance were estimated with linear discriminant analysis effect size. We calculated the immune cell fraction with CIBERSORTx and conducted survival analysis using the Cancer Genome Atlas patient data. Correlations between the microbiome and immune cell compositions were analyzed and a prediction model was constructed to estimate drug response. Results: Among the pathological complete response group (pCR), the beta diversity varied considerably; consequently, 20 genera and 24 species were observed to express a significant differential and relative abundance. Pandoraea pulmonicola and Brucella melitensis were found to be important features in determining drug response. In correlation analysis, Geosporobacter ferrireducens, Streptococcus sanguinis, and resting natural killer cells were the most correlated factors in the pCR, whereas Nitrosospira briensis, Plantactinospora sp. BC1, and regulatory T cells were key features in the residual disease group. Conclusion Our study demonstrated that the microbiome analysis of tumor tissue can predict chemotherapy response of patients with TNBC. Further, the immunological tumor microenvironment may be impacted by the tumor microbiome, thereby affecting the corresponding survival and treatment response.