Gut flora profiling and fecal metabolite composition of colorectal cancer patients and healthy individuals.

Colorectal cancer is one of the most common types of cancer in the world and its morbidity and mortality rates are increasing due to alterations to human lifestyle and dietary habits. The relationship between human gut flora and colorectal cancer has attracted increasing attention. In the present study, a metabolic fingerprinting technique that combined pyrosequencing with gas chromatography-mass spectrometry was utilized to compare the differences in gut flora profiling and fecal metabolites between healthy individuals and patients with colorectal cancer. The results demonstrated that there were no significant differences in the abundance and diversity of gut flora between healthy individuals and patients with colorectal cancer (P>0.05) and the dominant bacterial phyla present in the gut of both groups included Firmicutes, Bacteroidetes and Verrucomicrobia. At the bacterial strain/genus level, significant differences were observed in the relative abundance of 18 species of bacteria (P<0.05). Analysis of fecal metabolites demonstrated that the metabolic profiles of healthy individuals and patients with colorectal cancer were distinct. The levels of short-chain fatty acid metabolites, including acetic acid, valeric acid, isobutyric acid and isovaleric acid, and of nine amino acids in patients with colorectal cancer were significantly higher than those in healthy individuals (P<0.05). However, the levels of butyrate, oleic acid, trans-oleic acid, linoleic acid, glycerol, monoacyl glycerol, myristic acid, ursodesoxycholic acid and pantothenic acid in patients with colorectal cancer were significantly lower than those in healthy individuals (P<0.05). Pearson rank correlation analysis demonstrated that there was a correlation between gut flora profiling and metabolite composition. These findings suggest that gut flora disorder results in the alteration of bacterial metabolism, which may be associated with the pathogenesis of colorectal cancer. The results of the present study are useful as a foundation for further studies to elucidate a potential colorectal cancer diagnostic index and therapeutic targets.

Introduction

Colorectal cancer is one of the most common gastrointestinal tumors and is the second leading cause of cancer-related mortality worldwide (1). Colorectal cancer occurs sporadically in 95% of cases, indicating that environmental factors are the predominant cause of colorectal cancer (2). The human gut contains ~1014 flora, which is 100 times that of total human body cells. Research has indicated that gut flora has an important role in the regulation of epithelial cell proliferation, host energy metabolism and immuno-inflammatory reactions (3–5). Accumulating research has reported that bacterial populations and their metabolic patterns are closely related to the initiation and progression of colorectal cancer (6,7). The gut flora of healthy individuals differs to that of patients with colorectal cancer, with differences including an increased ratio of Bacteroides/Prevotella (8), increased numbers of Desulfovibrio spp., Enterococcus faecalis and Escherichia coli, and decreased numbers of Bifidobacterium and Lactobacillus in patients with colorectal cancer compared with healthy individuals (9–11). The profiles and types of gut flora determine the production of relevant metabolic products, such as acetaldehyde, hydrogen sulfide and secondary bile acids. Significantly elevated levels of these metabolic products would increase the risk of developing colorectal cancer (12,13); therefore, colorectal cancer is considered as a gut flora imbalance-related disease and it has been suggested that research should be focused on gut flora metabolism rather than on genes that may be related to colorectal cancer development (14).

In the present study, metabolic fingerprinting technology, which combines a pyrosequencing technique with gas chromatography-mass spectrometry (GC/MS), was utilized to compare the differences in gut flora and fecal metabolites between healthy individuals and patients with colorectal cancer. The aim was to determine whether gut flora imbalances existed in patients with colorectal cancer, which may provide an insight into the potential development of novel approaches for the prevention, diagnosis and treatment of colorectal cancer.

Materials and methods

Ethics statement

The research protocols for the present investigation were approved by the Ethics Committee at Sun Yat-sen University (Guangzhou, China). Written informed consent was provided by all participants prior to the initiation of the experiment.

Research subjects

A total of 15 patients with colorectal cancer (nine males and six females) and 12 healthy control individuals from the Physical Examination Center at the Department of Gastroenterology, the Third Affiliated Hospital of Nanchang University (Nanchang, China) participated in the present study between June 2013 and October 2014 at the Third Hospital Affiliated of Nanchang University. All patients with colorectal cancer were diagnosed for the first time according to the diagnostic criteria proposed by the International Union Against Cancer and the American Joint Committee on Cancer in 2003 (15). Patient exclusion criteria included those who experienced colorectal cancer recurrence post-surgery, underwent chemotherapy, had colorectal cancer complicated with metabolic diseases (such as diabetes mellitus), received antibiotics within one month, administered nonsteroidal anti-inflammatory drugs (NSAIDS), statins or probiotics within two months prior to the initiation of the experiment, suffered chronic intestinal diseases and had a history of food allergies. The average age of the patients was 52.5 years (range, 40–60 years). Among the 15 patients, three cases were of ascending colon cancer, two were transverse colon cancer, four were descending colon cancer, one was sigmoid colon cancer and five were rectal colon cancer. The clinical stages of these patients were stage II in four cases, stage III in six cases and stage IV in five cases.

The general characteristics of healthy control individuals were recorded, including age, gender and medical history. The exclusion criteria for healthy controls included those who had a medical history of cancer, diabetes, heart disease and other metabolic syndrome-related diseases, had recently received antibiotics, NSAIDS, statins or probiotics, had suffered from chronic intestinal diseases and had a history of food allergy.

Stool collection and pretreatment

Stool samples were collected prior to surgery or bowel preparation. All participants consumed a bland diet and did not smoke or consume alcohol one day prior to sample collection. A stool sample (500 mg) was collected from the center of the stool using a sterilized cotton swab and stored at −20°C. Prior to gut flora detection, a stool sample (100 mg) was emulsified with phosphate-buffered saline followed by vibration for 1 min. Samples were subsequently placed at 0°C for 5 min and the top water-soluble layer of extraction was collected and centrifuged at 3,000 x g for 10 min at 4°C. Following this, the sample was filtered and stored at −80°C. Pretreatment of stool samples prior to metabolic profiling analysis was conducted as follows: A total of 100 mg stool sample was mixed with 1 ml of isopropanol:acetonitrile:water (3:2:2), homogenized and centrifuged at 6,500 × g for 5 min at 4°C. Following this, the samples were dried in a quick vacuum gauge and re-suspended in 50 µl pyridine-methoxy amino acid salt solution (15 mg/ml). Subsequently, the solution was incubated at 60°C for 45 min, sonicated for 10 min, and centrifuged at 3,000 × g for 5 min at room temperature. Following cooling to room temperature, GC/MS analysis was performed using the sample. For the analysis of short-chain fatty acids, 0.5 g stool was mixed with 3 ml double distilled H2O, vibrated for 2 min and subsequently centrifuged at 10,000 × g for 5 min at room temperature. The supernatant was filtered using a 0.45-µm filter. A total of 1 ml filtrate was mixed with 0.1 ml sulfuric acid solution (50%) and 1 ml diethyl ether, followed by vibration for 30 min. Subsequently, the sample was centrifuged at 9,500 × g for 5 min and placed at −20°C for 30 min. Finally, the upper layer of diethyl ether was collected for GC/MS analysis.

Analysis of gut flora

Genomic DNA of flora was extracted using PowerSoil-htp 96-well DNA isolation kit (Mo Bio Laboratories, Inc.; Qiagen, Inc., Valencia, CA, USA), according to the manufacturer's instructions. According to the Jacob method, pyrosequencing was employed to specifically detect the V4 region of bacterial 16S ribosomal RNA on the isolated genomic DNA (3). The primer sequences used were: 515F (5′-GTGCCAGCMGCCGCGGTAA-3′) and 909R (5′-GACTACHVGGGTATCTAATCC-3′). The reaction cycle parameters were as follows: Preheating at 95°C for 2 min; followed by 25 cycles of denaturation at 95°C for 30 sec, annealing at 55°C for 30 sec, extension at 72°C for 30 sec; and terminal extension at 72°C for 7 min. GenElute gel extraction reagent (Sigma-Aldrich; Merck KgaA, Darmstadt, Germany) was used for initial purification of the amplified product and the secondary purification was performed using AMpure beads (Beckman Coulter, Inc., Brea, CA, USA). DNA concentration was measured using a PicoGreen DNA detection kit (Invitrogen; Thermo Fisher Scientific, Inc., Waltham, MA, USA). A total of 1 µg DNA product was used for standardized 454 pyrophosphate sequencing analysis using a high throughput sequencing machine (Illumina MiSeq; Illumina, Inc., San Diego, CA, USA).

Metabolite profiling analysis of stool samples

Samples were isolated using a 30-cm TG-5MS column (Thermo Fisher Scientific, Inc.), with parameters set as follows: Split ratio, 1:10; starting temperature, 80°C maintained for 30 sec. The temperature was subsequently raised to 330°C at a speed of 15°C/min and maintained for 8 min using ultra-pure helium as a carrier gas at a flow rate of 1.2 ml/min. The remaining parameters were: Injection port temperature, 260°C; splitless sampling, 1 µl; interface temperature, 280°C, solvent delay, 3 min; and scanning range, 50–650 M/z 5 times/sec. The parameters were adjusted for short-chain fatty acid analysis. The starting temperature of 22°C was maintained for 30 sec, followed by a temperature increase to 180°C at a speed of 8°C/min, which was maintained at 180°C for 1 min and subsequently raised to 200°C at a speed of 20°C/min. This temperature was maintained for 5 min with ultra-pure helium as a carrier gas at a flow rate of 1.2 ml/min. The remaining parameters were: Injection port temperature, 230°C; splitless sampling, 1 µl; interface temperature, 280°C; solvent delay, 3 min; and scanning range, 50–300 M/z 5 times/sec.

Data analysis

Gene sequences were read, edited and analyzed using Mothur 1.25 software (the University of Michigan, Ann Arbor, MI, USA). The obtained sequence reads were pre-clustered, allowing two mismatches. Operational taxonomic units (OTUs) were calculated via clustering by average neighbor principle at 97% genetic similarity. The species composition of samples was obtained through species annotation and species analysis. Subsequently, the numbers of sequence reads in each classification unit, and thus the relative abundance of each species, in each sample were calculated. Shared files and R language were utilized to create a profiling bar chart, and principal component analysis and species heatmap analysis were performed. For stool metabolic profile analysis, the spectral peak was distinguished and extracted according to Matlab script 7.0 (MathWorks, Natick, MA, USA), and the three-dimensional matrix table, consisting of retention time, mass-to-charge ratio and peak intensity, was obtained. The table was imported into SIMCA 14.0 software, (Umetrics, Umea, Sweden) and multi-dimensional statistical analysis was conducted.

Statistical analysis

Statistical analysis was performed using SPSS 6.0. (SPSS, Inc., Chicago, IL, USA). Analysis of molecular variance and Student's t-test were used to analyze the difference between bacterial species and metabolic profiling. Relationships between metabolites and bacteria were represented by the Pearson coefficient (r≥0.50 and 0.70 indicated moderate and intense correlation, respectively). P<0.05 was considered to indicate a statistically significant difference.

Results

Gut flora profiling

A total of 261,144 high quality reads were obtained from 27 samples using high-throughput sequencing of gut flora, with an average of 9,672 reads per sample. Based on the principle of 97% genetic similarity, 1,409 OTUs, with an average of 103 OTUs, were obtained per sample. No significant differences in Chaol abundance indices and Shannon diversity indices were observed between patients with colorectal cancer and healthy controls (P>0.05).

A total of 13 bacterial phyla were found in the stool samples of the colorectal cancer and healthy control groups, including Verrucomicrobia, Tenericutes, Synergistetes, Proteobacteria, Fusobacteria, Firmicutes, Cyanobacterium, Chlorobacteria, Bacteroidetes, Actinobacteria, Acidobacteria, Crenarchaeota and Euryarchaeota (Fig. 1). The composition of gut flora exhibited evident individual differences between the two groups; however, the relative abundance of bacterial phyla between the two groups was similar. The dominant bacterial phyla in samples from both groups were Firmicutes (44.0% in the colorectal cancer group vs. 40.9% in the healthy control group; P>0.05), Bacteroidetes (35.6% in the colorectal cancer group vs. 47.1% in the healthy control group; P>0.05) and Verrucomicrobia (7.9% in the colorectal cancer group vs. 3.7% in the healthy control group; P>0.05). Several stool samples from the colorectal cancer group contained Synergistetes; however, there was no significant difference observed between the two groups (P>0.05).

Figure 1.

Composition of gut flora in C and H. A total of 13 bacterial phyla were found in stool samples from the two groups. Compositions of gut flora between H and C did not differ significantly (P>0.05). H, healthy controls; C, patients with colorectal cancer.

At the bacterial strain/genus level, there was a significant difference observed in 18 bacteria between the colorectal cancer group and the healthy controls. As presented in Table I, the proportions of Adlercreutzia, Anaerosporobacter, Megamonas, Hypersegal, Dialister, Faecalibacterium, Bacteroides, Prevotella, Pseudobutyrivibrio, Enterobacter, Roseburia, Ruminococcus and Dorea forsicigenerans significantly decreased (P<0.05) in the colorectal cancer group compared with the healthy control group, whereas the proportions of Citrobacter farmer, Fusobacterium, Akkermansia muciniphila, Peptostreptococcus and Streptococcus significantly increased (P<0.05).

Table I.

Differences in the presence of 18 bacteria in stool samples from patients with colorectal cancer and healthy controls.

Bacterial strain/genus	MAV of healthy controls (%)	MAV of colorectal cancer patients (%)	Fold-change	P-value
Megamonas hypermegale	0.35	0.02	17.5	0.0001
Anaerosporobacter	0.76	0.13	5.8	0.0001
Enterobacter	0.43	0.12	3.6	0.0007
Dorea formicigenerans	0.28	0.03	9.3	0.0000
Adlercreutzia	2.31	0.83	2.8	0.0001
Faecalibacterium	2.33	0.51	4.6	0.0002
Dialister	4.22	0.12	35	0.0000
Prevotella	0.96	0.02	48	0.0000
Roseburia	0.74	0.03	24.7	0.0000
Ruminococcus	1.52	0.48	3.2	0.0009
Pseudobutyrivibrio	1.39	0.62	2.2	0.0036
Bacteroides	1.23	0.22	5.6	0.0001
Citrobacter farmeri	0.18	0.97	5.4	0.0001
Clostridium	0.23	6.5	28	0.0000
Fusobacterium	1.24	7.36	2.3	0.0034
Streptococcus	2.70	11.3	4.2	0.0001
Peptostreptococcus	0.90	12.8	14.2	0.0000
Akkermansia muciniphila	2.75	10.6	3.9	0.0002

MAV, mean abundance value.

Metabonomics of stool samples

As shown in Fig. 2, the metabolic profiling of the colorectal cancer group was distinct from that of the healthy control group (R2Y=0.990; QY2=0.914). The levels of short-chain fatty acid metabolites, including acetic acid, valeric acid, butyric acid and isovaleric acid, in the patients with colorectal cancer were significantly higher than those of the healthy control patients (P<0.0001, P=0.024, P<0.0001 and P=0.002, respectively), whereas the level of isobutyric acid was significantly lower in patients with colorectal cancer than in the healthy control individuals (P<0.0001; Fig. 3). No significant difference was observed in the levels of propionic acid between the two groups (P>0.05; Fig. 3). As shown in Table II, compared with the healthy control group, levels of nine amino acid metabolites in stool samples from patients with colorectal cancer increased by 44–82%, whereas three unsaturated fatty acids (oleic acid, elaidic acid and linoleic acid), two polyhydric alcohols (glycerin and monoacyl glycerol) and one saturated fatty acid (myristic acid) significantly decreased (P<0.05). Furthermore, ursodesoxycholic acid, a metabolic product of bile acid, and pantothenic acid, a metabolic product of vitamin B, significantly decreased (P<0.05) in colorectal cancer patients compared with the healthy controls.

Figure 2.

Distinct metabolic profiling between the colorectal cancer group and the healthy control group.

Figure 3.

Differences in the levels of short-chain fatty acid metabolites in the stool samples of patients with colorectal cancer and healthy controls. Significant differences were observed in the levels of acetic acid, valeric acid, isobutyric acid, butyric acid and isovaleric acid between patients with colorectal cancer and the healthy control group, whereas the level of propionic acid was not significantly different between the two groups. Data are presented as the mean ± standard deviation. *P<0.05 vs. healthy controls.

Table II.

Metabonomics of stool samples from patients with colorectal cancer and healthy control individuals.

Metabolites	Increase rate of colorectal cancer group group (%)	P-value	Metabolites	Decrease rate of colorectal cancer (%)	P-value
Glutamic acid	58.4	<0.001	Oleic acid	44.1	<0.05
Glycine	69.6	<0.010	Linoleic acid	66.2	<0.01
Aspartic acid	43.7	<0.001	Elaidic acid	72.8	<0.01
Leucine	65.3	<0.010	Glycerin	44.1	<0.01
Glycerin	81.6	<0.050	Monoacyl glycerol	66.2	<0.01
Proline	49.3	<0.001	Ursodesoxycholic acid	72.8	<0.01
Serine	55.7	<0.010	Myristic acid	74.2	<0.001
Valine	72.8	<0.001	Pantothenic acid	66.2	<0.01
Phenylalanine	44.1	<0.001
Phenylacetic acid	59.5	<0.010
Propionic acid	4.3	>0.050
Cholesterol derivatives	72.8	<0.010

Levels of nine amino acid metabolites in the colorectal cancer group were significantly higher than those in the healthy control group, whereas the levels of three unsaturated fatty acids, two types of glycerin, myristic acid, ursodesoxycholic acid and pantothenic acid in the colorectal cancer group were significantly lower than those in the healthy control group.

Correlation analysis between gut flora and metabolic profiling

Pearson rank correlation analysis demonstrated that there was a close correlation between gut flora and certain metabolic products (Fig. 4). The concentration of free fatty acids in stool samples was highly positively correlated with the levels of Bacteroides, Dialister and Pseudobutyrivibrio (r=0.87) and moderately correlated with the levels of Fusobacterium and Ruminococcus. The concentration of ursodesoxycholic acid in stool samples was highly positively correlated with the level of Ruminococcus (r=0.75). The concentrations of phenylalanine and glutamic acid were highly positively correlated with the levels of Phascolarctobacterium and Acidiminobacter (r=0.74). The concentrations of serine and threonine were moderately positively correlated with the levels of Phascolarctobacterium and Acidiminobacter (r=0.63).

Figure 4.

Heatmap of Pearson correlation analysis between gut flora and metabolic profiling. CRC, colorectal cancer.

Discussion

In the present study, it was demonstrated that the relative abundance of gut flora at the bacterial phylum level was similar between patients with colorectal cancer and healthy controls; however, at the bacterial strain/genus level, there were significant differences observed between the two groups. The relative abundances of multiple bacteria, particularly gram-negative Bacillus and Prevotella species, significantly increased or decreased in colorectal cancer patients compared with healthy controls. Prevotella has a role in the digestion of dietary fiber and its reduction may result in decreased dietary fiber intake in colorectal cancer patients (16). It has previously been reported that the abundance of Fusobacterium, Lachnospiracae, Dorea forsicigenerans and Ruminococcus decreased in stool samples from patients with colorectal cancer (17,18). The present study demonstrated, for the first time, that the relative abundance of Dialister and Megamonas in patients with colorectal cancer decreased compared with healthy controls. The potential significance of Dialister and Megamonas reduction remain to be elucidated. It was observed that the increase of several bacterial genera was closely correlated with colorectal cancer, for example, Akkermansia muciniphila, a mucins degradation bacterium, was highly abundant in the stool samples of patients with colorectal cancer in the present study. Akkermansia muciniphila is a common symbiotic colonic flora. It was initially reported that the abundance of this bacterial species decreased in irritable bowel syndrome and Crohn's disease (19). It was observed that A. muciniphila often increased in ulcerative colitis-related cryptitis, suggesting that A. muciniphila increase is involved in the pathogenesis of inflammatory bowel disease-related colorectal cancer (20). Mucin (MUC)1 and MUC5AC are often overexpressed in colorectal cancer tissues, which is attributed to the increased stability of intestinal tissue matrix due to an abundance of Akkermansia spp (21,22). Citrobacter farmer utilizes citrate as a sole carbon source and is also abundant in the stools of patients with colorectal cancer. Salmonella and Shigella, members of Citrobacter farmer, have been demonstrated to activate aryl amine N-acetyl transferase to promote the progression of colorectal cancer (23).

Metabolic fingerprinting using GC/MS to analyze small molecule metabolites is an ideal method for metabolomics research. It can be employed to obtain a wide range of metabolic profiling, accurately determine information on metabolites and thus discover the characteristic varying patterns of metabolites. In the present study, GC/MS analysis identified distinct metabolic profiling between patients with colorectal cancer and healthy individuals, providing information that may be useful for the diagnosis and staging of colorectal cancer, and for the discovery of abnormal metabolic pathways and potential therapeutic targets (24). It remains unclear how gut flora disorder participates in the pathogenesis of colorectal cancer; however, gut flora disorder inevitably induces changes in the relevant metabolic products in urine, stool and blood (25). The present study identified significant differences in short-chain fatty acid metabolites between patients with colorectal cancer and healthy individuals. Short-chain fatty acids as microbial metabolites, particularly butyric acid, are widely reported to have anti-cancer effects, protecting against the occurrence and progression of colorectal cancer (26). Levels of butyric acid-producing bacteria in patients with colorectal cancer, such as Ruminococcus and Pseudobutyrivibrio spp., are lower than those in healthy individuals. However, the metabolites of these flora, such as acetate, propionate, butyrate and three short-chain fatty acids, increase in stool samples from patients with colorectal cancer, indicating the exhaustion of butyric acid-producing microbiota in the colon (27).

In the present study, metabolite analysis of stool samples demonstrated that the levels of nine amino acids increased by 44–82% in patients with colorectal cancer compared with healthy controls. Possible reasons for this may be: Inflammation reduces nutrient absorption in patients with cancer; the accumulation of free amino acids in the metabolic pool of cancer cells resulting from cancer cell autophagy; dietary protein degradation by some specific bacteria in the distal colon of patients with colorectal cancer increases several amino acid metabolites in stool, such as amine, cresol and phenol; tumor cells display enhanced glutaminase activity to convert glutamine to glutamic acid, thus increasing glutamic acid in patients with colorectal cancer; or the levels of proline, serine and threonine in patients with colorectal cancer patients are consistent with the accumulation of Akkermansia muciniphila that promotes mucin degradation, leading to a corresponding metabolite increase in the stool (28).

Cancer cells possess a unique transport system for increased absorption of glycerin, which most likely attributes to the decreased level of glycerin in the stools of colorectal cancer patients compared with those of healthy individuals (29). Healthy individuals have abundant bacterial lipase in their stool to metabolize diet-source and endogenous nutrients to produce triacylglycerol, thus increasing terminal metabolic products of glycerin and free fatty acids. Ursodesoxycholic acid, a secondary bile acid produced by intestinal bacteria, in healthy individuals is ~72.8% higher than that in patients with colorectal cancer. A number of in vitro and in vivo studies have demonstrated that ursodesoxycholic acid has a preventive role in colorectal cancer (30,31).

The present study demonstrated that the abundance of gut flora and metabolite components in stool samples from healthy controls and patients with colorectal cancer differed; there were high correlations between the abundance of gut flora and relevant candidate metabolites. These findings suggest that gut flora disorders may have a role in the pathogenesis of colorectal cancer (32). The main limitation of the present study is that the sample size was small. Although moderate or high positive correlations between multiple gut bacteria and metabolites in stool samples from patients with colorectal cancer patients were identified, it is necessary for a definitive conclusion to be validated in a larger clinical series.