Project description:Background: The differential abundance of cell-free RNAs in bodily fluids is emerging as a promising tool for the non-invasive molecular diagnosis of cancer. Human saliva is considered a promising source of non-invasive biomarkers of diagnostic value for oral cancer detection. This study aims to identify diagnostic potent salivary RNAs in oral squamous cell carcinoma (OSCC)-patients by RNA-Sequencing. Method: Unstimulated saliva was collected from 5 normal control (NC) individuals and 9 OSCC patients (PS) with prior consent and ethical committee approvals. Total RNA isolated from cell-free saliva (CFS) supernatant was used to prepare small RNA libraries and sequenced on the Ion Torrent S5 platform. The sequencing reads were aligned to the human genome (hg19) using Bowtie 2, and the differential expression analysis was performed using RUVSeq and DESeq2. Mapped reads were screened across miRBase (v22) annotations for miRNAs and Gencode (v19) annotation for other RNAs. Reads were quantified by the Featurecount (v1.4.6) module of the R-package. The microbial-RNA enrichment analysis was determined using the One Codex platform. Result: RNA-sequencing detected protein-coding transcripts (PCTs), long-intergenic RNAs (lincRNAs), microRNAs (miRNAs), small nuclear RNAs (snRNAs), transfer RNAs (tRNAs) and pseudogenes from the saliva of PS and HC samples. Transcriptome analyses revealed 89 PCTs, 18 lincRNAs and 6 miRNAs differentially expressed between PS and HC with a log2fold change ≥ 1 or ≤ -1 and p-value < 0.05. Gene ontology and pathway enrichment analyses indicated a significant correlation of the identified PCTs and miRNAs to various cancer-related pathways that may have implications in the pathogenesis of OSCC. Interestingly, unmapped non-human reads aligned to the microbial reference genomes. Further analyses of these microbial sequence reads revealed a significant microbial dysbiosis differentiating PS from HC. Metabolic pathways and functional analysis of the identified microbial phylotypes showed gene ontologies associated with inflammation, cell proliferation, ROS generation, and a range of metabolic processes. Conclusion: We report novel panels of differentially expressed PCTs, miRNAs and lincRNAs distinguishing PS from HC. Importantly, our results also provide evidence for oral microbial dysbiosis that appears to have pathological implications in OSCC. Summarily, this study provides a comprehensive landscape of salivary RNAs that can be exploited as non-invasive biomarkers for OSCC detection.

Project description:The effect of oral microbiota on the intestinal microbiota has garnered growing attention as a mechanism linking periodontal diseases to systemic diseases. However, the salivary microbiota is diverse and comprises numerous bacteria with a largely similar composition in healthy individuals and periodontitis patients. Thus, the systemic effects of small differences in the oral microbiota are unclear. In this study, we explored how health-associated and periodontitis-associated salivary microbiota differently colonized the intestine and their subsequent systemic effects by analyzing the hepatic gene expression and serum metabolomic profiles. The salivary microbiota was collected from a healthy individual and a periodontitis patient and gavaged into C57BL/6NJcl[GF] mice. Samples were collected five weeks after administration. Gut microbial communities were analyzed by 16S ribosomal RNA gene sequencing. Hepatic gene expression profiles were analyzed using a DNA microarray and quantitative polymerase chain reaction. Serum metabolites were analyzed by capillary electrophoresis time-of-flight mass spectrometry. The gut microbial composition at the genus level was significantly different between periodontitis-associated microbiota-administered (PAO) and health-associated oral microbiota-administered (HAO) mice. The hepatic gene expression profile demonstrated a distinct pattern between the two groups, with higher expression of Neat1, Mt1, Mt2, and Spindlin1, which are involved in lipid and glucose metabolism. Disease-associated metabolites such as 2-hydroxyisobutyric acid and hydroxybenzoic acid were elevated in PAO mice. These metabolites were significantly correlated with Bifidobacterium, Atomobium, Campylobacter, and Haemophilus, which are characteristic taxa in PAO mice. Conversely, health-associated oral microbiota were associated with higher levels of beneficial serum metabolites in HAO mice. The multi-omics approach used in this study revealed that periodontitis-associated oral microbiota is associated with the induction of disease phenotype when they colonized the gut of germ-free mice.

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Oral microorganisms

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