Introduction
Colorectal cancer (CRC) is currently on of the most common cancer types worldwide and is the second leading cause of cancer-related mortality (Bray et al., 2018). Regardless of increasing access to CRC screening programs in many EU countries, more precise methods for early non-invasive disease discovery are highly needed (Kalager et al., 200; Benard et al., 2018). Currently CRC screening programs are based on fecal occult blood tests and/or colonoscopy, but this approach lacks of specificity and a large number or premalignant and malignant alterations in the colon are being overlooked (Click et al., 2019). Furthermore, current CRC programs result in high rate of expensive and unnecessary colonoscopy procedures, that pose significant risk for absolute healthy individuals (Ladabaum et al., 2019).
It is known that microbiome takes part in cancer genesis, its development, progression and even to its response to different treatment methods. Recent publication proves that gut microbiota (GM) can be used as novel biomarker tool in some disease (Russo et al., 2022, Roda et al., 2020). However, in order to, know whether microbiota is altered, studies identifying its profile in CRC patients are highly needed.
Aims & Methods
The aim of this study was to identify microbiome profile of CRC patients using gut mucosa biopsy and blood plasma samples.
In total, 313 individuals were included in this study, 104 of them were patients with CRC, 101 patients with adenomatous polyps (AP), and 108 were control individuals. Biopsies were taken from damaged mucosa during colonoscopy. Extracted DNA from all the samples was used to V1–V2 region of bacterial 16S rRNA gene amplification and sequencing on an Illumina MiSeq platform. To construct sample-similarity matrices abundances of taxonomic ranks in each sample type were used by applying Bray-Curtis algorithm. For significant differences between groups Permutational multivariate analysis of variance (PERMANOVA) and Mann-Whitney test followed by false-discovery rate test were used.
Results
Comparison between the global tissues bacterial assemblages revealed that the global structures at all taxonomic ranks were significantly different between control, CRC, and AP groups. Analysis revealed list of bacteria at the different taxonomic levels, the number of which was statistically significantly different between the groups. Moreover, analysis revealed bacteria that were associated with such clinical data as TNM classification, stage, and grade.
Conclusion
The global circulating and tissues microbiome profile of CRC and AP patients differs from that of the control group. Based on tissue and blood microbial profile it is possible to distinguish healthy state from CRC or AP. Mucosa tissue microbiome turns to be 20 times richer than blood microbiome. In CRC case bacterial richness and diversity decreases. Also there is distinct list of bacteria differed between CRC and control groups that could presumably be used as clinical biomarkers for CRC.
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Disclosure
The authors declare no conflict of interest.