Exploring Connections between Oral Microbiota, Short-Chain Fatty Acids, and Specific Cancer Types: A Study of Oral Cancer, Head and Neck Cancer, Pancreatic Cancer, and Gastric Cancer

Abstract

The association between oral microbiota and cancer development has been a topic of intense research in recent years, with compelling evidence suggesting that the oral microbiome may play a significant role in cancer initiation and progression. However, the causal connections between the two remain a subject of debate, and the underlying mechanisms are not fully understood. In this case-control study, we aimed to identify common oral microbiota associated with several cancer types and investigate the potential mechanisms that may trigger immune responses and initiate cancer upon cytokine secretion. Saliva and blood samples were collected from 309 adult cancer patients and 745 healthy controls to analyze the oral microbiome and the mechanisms involved in cancer initiation. Machine learning techniques revealed that six bacterial genera were associated with cancer. The abundance of Leuconostoc, Streptococcus, Abiotrophia, and Prevotella was reduced in the cancer group, while abundance of Haemophilus and Neisseria enhanced. G protein-coupled receptor kinase, H+-transporting ATPase, and futalosine hydrolase were found significantly enriched in the cancer group. Total short-chain fatty acid (SCFAs) concentrations and free fatty acid receptor 2 (FFAR2) expression levels were greater in the control group when compared with the cancer group, while serum tumor necrosis factor alpha induced protein 8 (TNFAIP8), interleukin-6 (IL6), and signal transducer and activator of transcription 3 (STAT3) levels were higher in the cancer group when compared with the control group. These results suggested that the alterations in the composition of oral microbiota can contribute to a reduction in SCFAs and FFAR2 expression that may initiate an inflammatory response through the upregulation of TNFAIP8 and the IL-6/STAT3 pathway, which could ultimately increase the risk of cancer onset.

Keywords: FFAR2; IL-6/STAT3; SCFAs; TNFAIP8; cancer onset; machine learning; oral microbiota.

Figure 1

The study utilized advanced techniques, such as PICRUST and machine learning, to analyze oral microbiota, identifying correlations between certain microbiota and cancer, and suggesting potential early markers of cancer.

Figure 2

The six most important microbiota in the Gradient Boosting Machine (GBM) model, along with relevant classification information. Confusion matrix evaluated machine learning model performance with actual and predicted labels, and error rate calculated by incorrect predictions divided by total predictions (a); The Probability of Detection Index (POD) for Gradient Boosting Machine (GBM) displayed value (0–1) for classifying samples into control and cancer, with values closer to 1 indicating higher likelihood of being classified as cancer (b); The stands for Area Under the Receiver Operating Characteristic curve (AUC-ROC) represented the relationship between the true positive rate (TPR) against the false positive rate (FPR) at various classification thresholds (c); Precision–Recall Curve depicted binary classification model performance, plotting precision against recall at different classification thresholds, useful for imbalanced datasets (d); Feature importance indicated how much each feature contributes to model prediction, indicating the relative importance of features in distinguishing between classes (e); SHAP value revealed an explanation of machine learning model output, providing local interpretations for individual predictions by attributing the feature contribution to the final prediction (f).

Figure 3

Analysis of oral microbiota diversity, SCFAs, FFAR2, and cytokine levels in the control and cancer groups. Alpha diversity was estimated via the phylogenetic diversity whole tree (OTUs, p < 0.001) and by observing operational taxonomic units (p < 0.001) (a); Beta diversity was calculated using principal coordinate analyses based on weighted (p < 0.001) and unweighted (p < 0.001) UniFrac distances in oral microbiota communities (b); Venn diagrams showing overlaps between groups at the genus level (c); Oral saliva total short-chain fatty acid concentrations (d); Free fatty acid receptor 2 concentrations in oral saliva (e); Concentrations of human plasma TNF-α induced protein 8 (f); Human plasma interleukin-6 levels (g); Human plasma signal transducer and activator of transcription 3 levels (h).

Figure 4

Graphical abstract. Oral microbiota-related SCFAs induce cancer and immune responses. Alcohol consumption and smoking modified the oral microbiota and short-chain fatty acids (SCFAs), free fatty acid receptor 2 (FFAR2), and relevant cytokine concentrations, which contributed to cancer initiation and systemic immune responses. ↑: increased, ↓: decreased.