Introduction: Colorectal cancer (CRC) is the second leading cause of death in developed countries and the most common cancer in Spain. Despite the availability of a gold standard diagnostics biomarker such as Fecal Occult Blood Test (FOB), their accuracy for the early stages of the disease is suboptimal. In this study, we performed a combination of metabolomics and microbiome analyses in feces samples in order to identify and characterize potential early biomarkers for both advanced adenomas (AD) and CRC.

Methods: We performed UHPLC-MS and V1-V2 16S rDNA sequencing on 245 fecal samples: 77 controls (C), 69 AD and 99 CRC patients. Results obtained through each omics approach were studied per separate and later combined them by a range of methodologies in order to identify potential interactions between the microbiome and fecal metabolome. We finally generated a combined metabolomics-microbiome model for CRC diagnosis.

Results: We report differences in fecal levels of cholesteryl esters, sphingomyelins and ceramides in CRC patients when compared to C and AD samples. We also identified a trend of AD patients to have elevated triacylglycerols and diacylglycerols when compared to C samples. We identified 3 genera to be increased in CRC patients (Fusobacterium, Parvimonas and Staphylococcus) and Lachnospiraceae family to be reduced in these patients. We finally described Adlercreutzia to be more abundant in AD patients’ feces when compared to both control and CRC samples, suggesting a potential utility as biomarker for early stages of CRC disease. Microbiome composition identified alterations were associated to proinflammatory events and to a metabolism shift towards carbohydrates degradation and fermentation, leading to a reduction of short-chain fatty acids, including also several methane-related metabolic pathways, supporting the presence of anaerobic bacteria in this population. Then, we combined both datasets and identified a number of correlations between altered metabolites and altered genera in CRC patients.

Conclusions: We describe a relevant similarity between both datasets, thus confirming the important role of gut microbiota on fecal metabolome. Finally, using the combination of metabolomics and microbiome data, we propose a diagnostics logistic model that outperforms single-omics ones and FOB discriminative capabilities.