| Purpose:Colorectal cancer (CRC), a commonly diagnosed cancer in the elderly, is among the top three most frequently diagnosed cancer worldwide. The incidence is higher in more developed countries, but is rapidly increasing in historically low risk areas such as Eastern Asia and Eastern Europe. CRC often develops slowly from benign polyps called adenoma. CRC has long been considered as malignant cell proliferation caused by accumulated genetic and epigenetic mutations, but increasing evidences suggests that the composition of the gut microbiome may offer novel insight s into the aetiology of CRC.2012 Tjalsma et al. proposed a bacterial driver-passenger model to explain the involvement of microbial agents in the origin and proliferation of CRC. Under this model, driver and passenger bacteria each play distinct roles in eliciting epithelial phenotype transformation of tissue from normal states, to hyperplasia, and adenoma to carcinoma. We attempt to explore structure and composition of the gut microbiota which were associated with CRC, at the same time, to identify the core gut microbiota and potential driver and passenger bacteria that may be associated with CRC via 454-pyrosequencing analysis of bacterial 16S rRNA genes.Methods:We analyzed a total of 28 location-matched biopsy samples, including normal intestinal tissues (n=10), adenoma tissues (n=10), and tumor tissues (n= 8), with each sample being taken from one individual subject. The bacterial 16S rRNA gene was amplified by PCR methods using bacterial DNAs extracted from all 28 samples and was used to perform 454-barcoded pyrosequencing. Bacterial 16S rRNA sequences were filtered and used to identify bacterial OTUs (equal to bacterial species) according to 97% sequence identity. Final OTUs were used to clarify the community structure and composition of gut microbiota. Based on these results, further exploration using statistical analysis, network analysis et al. was expanded about the gut microbiota associated with CRC.Results:After filtering raw data with our set of criteria, we obtained a dataset consisting of a total of 100,276 high quality 16S rRNA gene sequences, with an average of 3,581±408 (S.E.) (n= 28) sequences per sample. Within the dataset we identified a total of 8130 OTUs, based on 97% sequence similarity (equal to bacterial species level), with an average of 290±16 (n=28) OTUs per sample. Using the estimation of Good’s Coverage showed that 95.20±0.70% of the total found species were represented in any given sample. PLS-DA analysis illustrated a distinct structural segregation for all 28 samples that appears to be primarily related to health/disease conditions rather than other factors. Based on the model, we identified 7 bacterial genera as potential driver bacteria and 12 bacterial genera as potential passenger bacteria. Correlation analysis of the 19 bacterial taxa showed that bacterial taxa with the same defined role were clustered into groups with positive correlation of each other. We also found that the driver bacterial cluster was significantly and positively correlated to the pro-inflammatory passenger bacterial cluster, conversely, the anti-inflammatory passenger bacterial cluster was significantly and negatively correlated with the driver bacterial cluster.Conclusions:In this study, using the second generation of high-throughput sequencing methods (454 pyrosequencing), we revealed the basic structure and components of the gut microbiota in different stages of development of CRC,by analysising the bacterial 16S rRNA gene. Meanwhile our study further identified potential Driver and Passenger bacteria. The identified Driver bacteria play a important role on the early intervention and prevention for CRC, the identified passenger bacteria has important reference value in CRC’s diagnosis and personalized treatment. |
