Molecular characterization of gut microbial structure and diversity associated with colorectal cancer patients in Egypt

Abstract

Introduction: a large number of microbes colonizing the gut are highly diverse and complex in their structure, as this complex structure of gut microbiota acts as an indicator of a diseased state. Recently, there is a need for improved biomarkers for colorectal cancer (CRC) and advanced adenoma. Among the CRC associated organisms, bacteria are the most common causes of serious disease and deaths. To understand the dynamic interaction among bacteria colonizing the gut, different approaches have been implicated.

Methods: in this study, faecal microbial markers were evaluated for detecting CRC. As most of these organisms are anaerobic, different molecular tools are of great values for rapid detection of these bacteria. Samples from Tumor Hospital were screened for the presence of different pathogens by both usual polymerase chain reaction (PCR) and a realtime assay.

Results: in a total of 34 samples, by PCR method, bifidobacterium, fusobacterium and Escherichia coli (E. coli) were mainly identified in almost all samples. However, a clear variation in bacterial composition could be observed in Porphyromonas gingivalis, Prevotella intermedia and Peptostreptococcus magnus, where positive results could be detected only in diseased samples. In addition, E. faecium and E. saphenum were mainly identified in diseased samples. In contrast, providencia could be detected mainly in control samples. In realtime assay, the relative abundance was higher for fusobacterium and bifidobacterium markers in CRC patients, compared to control samples. However, such increased in abundance has never been observed in both fusobacterium and bifidobacterium in the same sample.

Conclusion: these results demonstrated increased abundance of fusobacterium or bifidobacterium can be considered as a sign for impairment or a diseased condition and the possibility of use of the faecal microbiotain CRC patients as a marker for detecting the disease.