Integrative Analysis of Fecal Metagenomics and Metabolomics in Colorectal Cancer

Abstract

Although colorectal cancer (CRC) is the second leading cause of death in developed countries, current diagnostic tests for early disease stages are suboptimal. We have performed a combination of UHPLC-MS metabolomics and 16S microbiome analyses on 224 feces samples in order to identify early biomarkers for both advanced adenomas (AD) and CRC. We report differences in fecal levels of cholesteryl esters and sphingolipids in CRC. We identified Fusobacterium, Parvimonas and Staphylococcus to be increased in CRC patients and Lachnospiraceae family to be reduced. We finally described Adlercreutzia to be more abundant in AD patients’ feces. Integration of metabolomics and microbiome data revealed tight interactions between bacteria and host and performed better than FOB test for CRC diagnosis. This study identifies potential early biomarkers that outperform current diagnostic tools and frame them into the stablished gut microbiota role in CRC pathogenesis.

Liquid biopsies and cancer omics

“…Stool analysis from a multiomics perspective is also able to integrate information on the role of the microbiome in cancer pathogenesis, especially in the case of colorectal cancer and pancreatic cancer, as well as in other disease entities such as inflammatory intestinal diseases [41][42][43] . The microbiota creates a complex network that can influence the tumor microenvironment in a very heterogeneous way that relies on the intrinsically heterogeneity of the microbiome itself [44][45][46][47][48][49] . The microbiome study, also through liquid biopsies, would also grant some other information related to the geography of diseases, among which cancer, since the intestinal bacteria are able to differentiate individuals on the basis of the place they live in [50][51][52][53][54] .…”

Integration of the Microbiome, Metabolome and Transcriptomics Data Identified Novel Metabolic Pathway Regulation in Colorectal Cancer

“…In order to identify potential microbiome–metabolome interactions, we applied a Bayesian additive regression trees classification method (BART) to discriminate CRC cases from healthy controls in two published datasets; Kim et al [ 8 ] and Clos-Garcia et al [ 11 ]. The BART method was chosen because it is capable of both capturing nonlinear effects and estimating uncertainties associated with the estimates.…”

“…The Clos-Garcia [ 11 ] dataset analysis revealed a better performance in discriminating CRC from healthy controls, in both combined microbiome metabolome dataset (oos: 0.094) and microbiome dataset alone (oos: 0.113). Compared with Kim et al [ 8 ], the Clos-Garcia [ 11 ] microbiome samples confer considerably higher predictive power. This noticeable difference in the classification error between the two published datasets may be partly explained by the observation that no common microbes, at the genus level, were selected by BART.…”

Association Between Serum Thyroid-Stimulating Hormone Levels and Salivary Microbiome Shifts

“…A number of studies have explored the role of Porphyromonas in atherosclerosis (Giles et al, 2020), Alzheimer's disease (Miller and Scott, 2020), rheumatoid arthritis (Cheng et al, 2020), diabetes (Minty et al, 2019), and adverse pregnancy outcomes (Chopra et al, 2020) and nonalcoholic fatty liver disease (Clos-Garcia et al, 2020;Wang et al, 2020;Zhou et al, 2020). Parvimonas have been reported to be a potential carcinogenic pathogen, which is positively associated with oral squamous cell carcinoma (OSCC) (Zhou et al, 2020), colorectal cancer (Clos-Garcia et al, 2020), and gastric cancer (Gantuya et al, 2020). Streptococcus is one of the most common bacteria in the oral cavity, and is predominant in the saliva of patients with liver diseases (Li et al, 2020) and is associated with OSCC (Yang C. Y. et al, 2018).…”