Background

Colorectal cancer (CRC) is a common malignant tumour of the digestive tract, with the third highest incidence and second highest mortality among tumours worldwide [1, 2]. Colorectal adenomatous polyps (CAPs) are regarded as a critical precursor to CRC [3], and adenoma is an early neoplastic tissue that has not gained the properties of a cancer.

There are 100 trillion bacteria in the human intestine, and the collective genome of these bacteria is called the gut microbiome, which is 150 times the size of the human genome [4]. The gut microbiota is essential for the growth and physical health of the human body. The gut microbiota participates in digestion and absorption of food in the intestines and actively participates in cell-mediated immune responses, which maintain intestinal barrier function and intestinal environment stability. The imbalance of this symbiotic relationship might have an adverse effect on the host, and gut microbiota imbalance has been observed in cases of inflammatory bowel disease (IBD) [5], obesity [6], ageing [7] and cancer [8].

Moore et al. [9] applied culture methods to analyse the faeces of CRC patients and colorectal polyp patients and found that the abundances of Bacteroides and Bifidobacteria were positively correlated with the risk of colon polyps, while those of Lactobacilli and Eubacteria were related to the intestinal tract and had a protective effect. However, the types of bacteria that could be cultured in faeces were limited, and most of the bacteria could not be cultured in an in vitro environment. Therefore, the emergence of high-throughput sequencing technology and metagenomic analysis provided a better solution for analysing complex microbiome data. Fusobacterium has been identified as a risk factor for both colorectal adenomas and cancer [10], and the mechanism of the F. nucleatum association with CRC has been clarified in mice [11].

Intestinal metabolites, such as short-chain fatty acids (SCFAs) and bile acids, are strongly linked with cancerous conditions in the gut [12]. Conjugated linoleic acid (CLA) is considered a health-promoting fatty acid, and the anti-cancer properties of CAL in vivo and in vitro have been widely recognized [13]. Some Firmicutes use the butyryl-CoA:acetate CoA-transferase route to produce butyrate, and the proportion of propionate presented in faeces correlated with the relative abundance of Bacteroidetes [12]. Owning to their generation of reactive oxygen species (ROS) and reactive nitrogen species (RNS), which cause DNA damage, bile acids have been implicated in carcinogenesis.

To investigate the gut microbiota profile in CAP patients, we collected faeces from CAP patients attending the First Affiliated Hospital of Kunming Medical University. High-throughput sequencing technology was used to analyse the gut microbiota in the intestinal tract, which extended the understanding of the microbial community. Analysis of the faecal metabolites of CAP patients was performed to identify the metabolites that changed and to explore the changes in the gut microbiota and its metabolites during the progression of CAP to CRC. This study will provide comprehensive information about the gut microbiota and metabolite changes in CAP patients, which will help to characterize the role of gut microbiota and metabolites in adenoma occurrence and progression, and the differences in the gut microbiota and metabolites might be considered biomarkers of CAP in the future.

Methods

Sampling

Thirty patients with colorectal adenomatous polyps were selected from the First Affiliated Hospital of Kunming Medical University from November 2017 to April 2018. Exclusion criteria included obesity, special eating habits, colorectal cancer, history of colorectal surgery, colitis (ulcerative, Crohn’s), metabolic disease (diabetes, hyperlipidaemia), and infectious disease. Thirty healthy volunteers were selected from the First Affiliated Hospital of Kunming Medical University as controls. No subjects were taking antibiotics, immunosuppressive agents, corticosteroids or probiotics within 3 months prior to sample collection. There were no significant differences in age, gender, or BMI between the two groups (P > 0.05) (Table 1). Stool samples from volunteers were preserved after admission, and samples were collected in accordance with the relevant guidelines and regulations.

Table 1 Demographic information
Full size table

DNA extraction and 16S rRNA gene amplification

Genomic DNA was extracted from stool samples and biopsy samples with the QIAamp DNA Stool Mini Kit (Qiagen, Hilden, Germany). The primers for amplification of the V3-V4 region of the bacterial 16S rRNA gene, after amplification high throughput sequencing was performed with the Illumina MiSeq platform (Illumina, CA, USA).

Bioinformatics analysis

The high-quality paired-end reads were combined to tags based on overlaps, and the consensus sequence was generated by Fast Length Adjustment of Short reads (FLASH, v1.2.11). The tags were clustered into operational taxonomic units (OTUs) by scripts in USEARCH (v7.0.1090) software, OTUs were clustered with a 97% similarity cut-off using UPARSE, and OTU representative sequences were taxonomically classified using the Ribosomal Database Project (RDP) Classifier v.2.2 trained on the Greengene_2013_5_99 database with a 0.6 confidence value as the cut-off. OTUs were filtered by removing unassigned OTUs and removing OTUs not assigned to target species.

OTUs were used for α diversity estimation. Comparison of the β diversity, which is the difference in species diversity between two groups, was performed based on the OTU abundance by QIIME (V1.80).

Specimen annotation analysis is a method that compares OTUs to a database of classified OTUs at the phylum, class, order, family and species levels, and the analysis is then presented by histograms. UniFrac analysis used phylogenetic information to compare species community differences between samples.

Metabolite determination

Ion chromatograph analysis

The faecal samples stored in the refrigerator at − 80 °C were removed, weighed to 300 mg, dissolved in 1 mL of dH2O, vortexed and mixed for 30 s. Then, the supernatant was incubated with a 0.22 μm microporous nylon membrane (water system). The liquid was filtered, placed in an EP tube and placed in a refrigerator at − 20 °C for use. Standard curves were generated by using standard solutions. A volume of 25 μL was performed into chromatographic columns (DIONEX IonPac AG11-HC 4 × 50 mm & IonPac AS11-HC 4 × 250 mm, USA) and eluted with KOH at a flow rate of 1.2 mL/min; ions were detected by a conductivity detector in an ion chromatograph (Thermo Dionex ICS-3000, USA), and the column temperature was 30 °C.

UPLC-MS/MS analysis

The faecal samples stored in the refrigerator at − 80 °C were removed, weighed to 200 mg, dissolved in 1 mL of methanol, vortexed and mixed for 30 s. The supernatant was then incubated with a 0.22 μm microporous nylon membrane (organic system). The liquid was filtered, placed in an EP tube and placed in a refrigerator at − 20 °C for use. Standard curves were generated by using standard solutions.

UPLC conditions

The mobile phase consisted of 0.05% ammonia (5 mM aqueous solution) in water as solution A and acetonitrile as solution B. The flow rate of the mobile phase through the column was 0.4 mL/min (Waters BEH C18 1.7 μm, 50*2.1 mm, USA) at a temperature of 40 °C. The injection volume was 1 μL. Mass spectrometry conditions included electrospray ionization, negative ion mode, multiple reaction detection, air as the desolvation gas, nitrogen as the cone gas, and argon as the collision gas.

Real-time PCR analysis

To explore bacteria that produce specific metabolites, real-time PCR was used (TIANLONG Gentier 96, ** the gut microbiome of diet-induced obese mice. BMC Biol. 2017;15(1):120." href="/article/10.1186/s13099-020-00395-0#ref-CR40" id="ref-link-section-d91300678e1641">40, 41], and the Bacteroides abundance showed a significant increase in the CAP group. The differences in secondary bile acid-producing bacteria were not statistically significant in this study. Mullish et al. [42] found that the BaiCD operon was not present in all bacteria with 7α-dehydroxylating ability, which has been considered an important process for secondary bile acid formation in faeces [13].

Metabolomics provides a qualitative and quantitative method of metabolite in analysis that can complete analysis along with microbiology. Metabolites (small molecules < 1500 Da) are cellular metabolism intermediates or end products, that can be produced directly by the host organism or can be derived from various other external sources, such as the diet, microbes, or xenobiotic sources [43]. Biological systems display complex and analytical limitations, and it is not possible to identify all the metabolites present in a specimen. Studies on metabolites and diseases indicate changes in diabetes [44], cardiovascular disease and heart failure [45, 46], autism [47] and anxiety [48]. As research progresses, metagenomic markers can be utilized for early disease diagnosis or cancer screening, and gut microbiota biology can indicate the effectiveness of cancer therapies and has predictive potential [49].

Conclusion

In this study, gut microbiota analysis in the faeces showed that the abundances of Weissella and Lactobacillus were decreased, and those of Bacteroides and Citrobacter were increased in the CAP group. The increased abundances of Bacteroides and Citrobacter were positively correlated with CAPs, which indicated that changes in specific genera might be detrimental to intestinal health. In addition, metabolite detection showed changes in butyrate content, indicating that additional experiments are needed to investigate the function of butyrate in the intestinal environment. The increased concentration of t10,c12-CLA plays an important role in protecting the intestine. Analysis of the gut microbiota demonstrated carriage of operons producing metabolites, which indicated that additional functional operons might exist in the gut microbiota or intracellularly. Therefore, further studies focusing on lifestyle, diet and other factors in different populations are required to confirm the effect on intestinal health.