Oral Microbiota as Novel Biomarkers for Colorectal Cancer Screening

Abstract

Alterations of the gut microbiome in cases of colorectal cancer (CRC) hint at the involvement of host-microbe interactions in the onset and progression of CRC and also, possibly, provide novel ways to detect and prevent CRC early. The aim of the present study was to evaluate whether the oral and fecal microbiomes of an individual can be suitable for CRC screening. Oral and fecal samples (n = 80) were gathered in Taleghani hospital, affiliated with Shahid Beheshti University of Medical Sciences, Tehran-Iran, from CRC stage 0 and I patients and healthy controls (HCs), who were screened for the first time. Microbial metagenomics assays were performed for studying microbiota profiles in all oral and fecal samples gathered. An abundance of top bacterial genera from both types of specimens (fecal and saliva samples) revealed a distinction between CRC patients and HCs. In saliva samples, the α diversity index was different between the microbiome of HCs and CRC patients, while β diversity showed a densely clustered microbiome in the HCs but a more dispersed pattern in CRC cases. The α and β diversity of fecal microbiota between HCs and CRC patients showed no statistically significant differences. Bifidobacterium was identified as a potential bacterial biomarker in CRC saliva samples, while Fusobacterium, Dialister, Catonella, Tennerella, Eubacterium-brachy-group, and Fretibacterium were ideal to distinguish HCs from CRC patients. One of the reasons for the heterogeneity of CRC may be the gastrointestinal (GI) tract microbiota, which can also cause systematic resistance to CRC. Moreover, an evaluation of saliva microbiota might offer a suitable screening test for the early detection of this malignancy, providing more accurate results than its fecal counterpart.

Keywords: biomarkers; colorectal cancer; metagenomics; non-invasive diagnosis; oral microbiota.

Figure 1

Composition of bacteria and the relative differential abundance of the bacterial genera in fecal and saliva samples of colorectal cancer (CRC) patients and healthy controls (HCs).

Figure 2

Microbial community diversity across our study groups using the Chao1 approach for α-diversity and NMDS metric for β-diversity: (A) α-diversity of saliva samples from CRC patients and healthy controls; (B) α-diversity of stool samples from CRC patients and healthy controls; (C) α-diversity of saliva and stool samples from CRC patients; (D) α-diversity using CHAO1 of saliva and stool samples from healthy controls; (E) β-diversity of saliva samples from CRC patients and healthy controls; (F) β-diversity of stool samples from CRC patients and healthy controls; (G) β-diversity of saliva and stool samples from CRC patients; (H) β-diversity of saliva and stool samples from healthy controls.

Figure 3

Clustered heatmap of bacterial genera–sample group associations in (A) saliva samples of colorectal cancer (CRC) patients and healthy controls (HCs) and (B) stool samples of colorectal cancer (CRC) patients and healthy controls (HCs).

Figure 4

Clustered heatmap of bacterial genera–sample group associations regardless of explanatory variable. The heatmap shows a clear distinction between saliva and stool samples while the disease creates different association patterns versus healthy controls.

Figure 5

Top features (bacterial genera) in Random Forest (RF) models using (A) saliva samples of CRC (Colorectal cancer) patients and healthy controls (HC); (B) stool samples of CRC patients and healthy controls to predict sample classification into the patient and control groups. Figures also include Out-Of-Bag error and classification matrices for each model. In addition, Linear Discriminant Analysis (LDA) (C,D) between the same sample groups reveals bacterial genera which can serve as biomarkers for possible sample classification in CRC. Both approaches highlight, through different statistical methodologies, specific bacterial genera. ●.