Species-level identification of enterotype-specific microbial markers for colorectal cancer and adenoma.

Enterotypes have been shown to be an important factor for population stratification based on gut microbiota composition, leading to a better understanding of human health and disease states. Classifications based on compositional patterns will have implications for personalized microbiota-based solutions. There have been limited enterotype based studies on colorectal adenoma and cancer. Here, an enterotype-based meta-analysis of fecal shotgun metagenomic studies was performed, including 1579 samples of healthy controls (CTR), colorectal adenoma (ADN) and colorectal cancer (CRC) in total. Gut microbiota of healthy people were clustered into three enterotypes (Ruminococcus-, Bacteroides- and Prevotella-dominated enterotypes). Reference-based enterotype assignments were performed for CRC and ADN samples, using the supervised machine learning algorithm, K-nearest neighbors. Differential abundance analyses and random forest classification were conducted on each enterotype between healthy controls and CRC-ADN groups, revealing novel enterotype-specific microbial markers for non-invasive CRC screening strategies. Furthermore, we identified microbial species unique to each enterotype that play a role in the production of secondary bile acids and short-chain fatty acids, unveiling the correlation between cancer-associated gut microbes and dietary patterns. The enterotype-based approach in this study is promising in elucidating the mechanisms of differential gut microbiome profiles, thereby improving the efficacy of personalized microbiota-based solutions.

Distinctive Lipid Characteristics of Colorectal Cancer Revealed through Non-targeted Lipidomics Analysis of Tongue Coating.

The metabolites and microbiota in tongue coating display distinct characteristics in certain digestive disorders, yet their relationship with colorectal cancer (CRC) remains unexplored. Here, we employed liquid chromatography coupled with tandem mass spectrometry to analyze the lipid composition of tongue coating using a nontargeted approach in 30 individuals with colorectal adenomas (CRA), 32 with CRC, and 30 healthy controls (HC). We identified 21 tongue coating lipids that effectively distinguished CRC from HC (AUC = 0.89), and 9 lipids that differentiated CRC from CRA (AUC = 0.9). Furthermore, we observed significant alterations in the tongue coating lipid composition in the CRC group compared to HC/CRA groups. As the adenoma-cancer sequence progressed, there was an increase in long-chain unsaturated triglycerides (TG) levels and a decrease in phosphatidylethanolamine plasmalogen (PE-P) levels. Furthermore, we noted a positive correlation between N-acyl ornithine (NAOrn), sphingomyelin (SM), and ceramide phosphoethanolamine (PE-Cer), potentially produced by members of the Bacteroidetes phylum. The levels of inflammatory lipid metabolite 12-HETE showed a decreasing trend with colorectal tumor progression, indicating the potential involvement of tongue coating microbiota and tumor immune regulation in early CRC development. Our findings highlight the potential utility of tongue coating lipid analysis as a noninvasive tool for CRC diagnosis.

A comparative study of supervised and unsupervised machine learning algorithms applied to human microbiome.

Background: The human microbiome, consisting of diverse bacte-rial, fungal, protozoan and viral species, exerts a profound influence on various physiological processes and disease susceptibility. However, the complexity of microbiome data has presented significant challenges in the analysis and interpretation of these intricate datasets, leading to the development of specialized software that employs machine learning algorithms for these aims. Methods: In this paper, we analyze raw data taken from 16S rRNA gene sequencing from three studies, including stool samples from healthy control, patients with adenoma, and patients with colorectal cancer. Firstly, we use network-based methods to reduce dimensions of the dataset and consider only the most important features. In addition, we employ supervised machine learning algorithms to make prediction. Results: Results show that graph-based techniques reduces dimen-sion from 255 up to 78 features with modularity score 0.73 based on different centrality measures. On the other hand, projection methods (non-negative matrix factorization and principal component analysis) reduce dimensions to 7 features. Furthermore, we apply supervised machine learning algorithms on the most important features obtained from centrality measures and on the ones obtained from projection methods, founding that the evaluation metrics have approximately the same scores when applying the algorithms on the entire dataset, on 78 feature and on 7 features. Conclusions: This study demonstrates the efficacy of graph-based and projection methods in the interpretation for 16S rRNA gene sequencing data. Supervised machine learning on refined features from both approaches yields comparable predictive performance, emphasizing specific microbial features-bacteroides, prevotella, fusobacterium, lysinibacillus, blautia, sphingomonas, and faecalibacterium-as key in predicting patient conditions from raw data.

Polyketide synthase positive Escherichia coli one-time measurement in stool is not informative of colorectal cancer risk in a screening setting.

Environmental factors like the pathogenicity island polyketide synthase positive (pks+) Escherichia coli (E. coli) could have potential for risk stratification in colorectal cancer (CRC) screening. The association between pks+ E. coli measured in fecal immunochemical test (FIT) samples and the detection of advanced neoplasia (AN) at colonoscopy was investigated. Biobanked FIT samples were analyzed for both presence of E. coli and pks+ E. coli and correlated with colonoscopy findings; 5020 CRC screening participants were included. Controls were participants in which no relevant lesion was detected because of FIT-negative results (cut-off ≥15 µg Hb/g feces), a negative colonoscopy, or a colonoscopy during which only a nonadvanced polyp was detected. Cases were participants with AN [CRC, advanced adenoma (AA), or advanced serrated polyp (ASP)]. Existing DNA isolation and quantitative polymerase chain reaction (qPCR) procedures were used for the detection of E. coli and pks+ E. coli in stool. A total of 4542 (90.2%) individuals were E. coli positive, and 1322 (26.2%) were pks+ E. coli positive. The prevalence of E. coli in FIT samples from individuals with AN was 92.9% compared to 89.7% in FIT samples of controls (p = 0.010). The prevalence of pks+ E. coli in FIT samples from individuals with AN (28.6%) and controls (25.9%) was not significantly different (p = 0.13). The prevalences of pks+ E. coli in FIT samples from individuals with CRC, AA, or ASP were 29.6%, 28.3%, and 32.1%, respectively. In conclusion, the prevalence of pks+ E. coli in a screening population was 26.2% and did not differ significantly between individuals with AN and controls. These findings disqualify the straightforward option of using a snapshot measurement of pks+ E. coli in FIT samples as a stratification biomarker for CRC risk. © 2024 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

A distinct Fusobacterium nucleatum clade dominates the colorectal cancer niche.

Fusobacterium nucleatum (Fn), a bacterium present in the human oral cavity and rarely found in the lower gastrointestinal tract of healthy individuals1, is enriched in human colorectal cancer (CRC) tumours2-5. High intratumoural Fn loads are associated with recurrence, metastases and poorer patient prognosis5-8. Here, to delineate Fn genetic factors facilitating tumour colonization, we generated closed genomes for 135 Fn strains; 80 oral strains from individuals without cancer and 55 unique cancer strains cultured from tumours from 51 patients with CRC. Pangenomic analyses identified 483 CRC-enriched genetic factors. Tumour-isolated strains predominantly belong to Fn subspecies animalis (Fna). However, genomic analyses reveal that Fna, considered a single subspecies, is instead composed of two distinct clades (Fna C1 and Fna C2). Of these, only Fna C2 dominates the CRC tumour niche. Inter-Fna analyses identified 195 Fna C2-associated genetic factors consistent with increased metabolic potential and colonization of the gastrointestinal tract. In support of this, Fna C2-treated mice had an increased number of intestinal adenomas and altered metabolites. Microbiome analysis of human tumour tissue from 116 patients with CRC demonstrated Fna C2 enrichment. Comparison of 62 paired specimens showed that only Fna C2 is tumour enriched compared to normal adjacent tissue. This was further supported by metagenomic analysis of stool samples from 627 patients with CRC and 619 healthy individuals. Collectively, our results identify the Fna clade bifurcation, show that specifically Fna C2 drives the reported Fn enrichment in human CRC and reveal the genetic underpinnings of pathoadaptation of Fna C2 to the CRC niche.

Multimodal metagenomic analysis reveals microbial single nucleotide variants as superior biomarkers for early detection of colorectal cancer.

Microbial signatures show remarkable potentials in predicting colorectal cancer (CRC). This study aimed to evaluate the diagnostic powers of multimodal microbial signatures, multi-kingdom species, genes, and single-nucleotide variants (SNVs) for detecting precancerous adenomas. We performed cross-cohort analyses on whole metagenome sequencing data of 750 samples via xMarkerFinder to identify adenoma-associated microbial multimodal signatures. Our data revealed that fungal species outperformed species from other kingdoms with an area under the ROC curve (AUC) of 0.71 in distinguishing adenomas from controls. The microbial SNVs, including dark SNVs with synonymous mutations, displayed the strongest diagnostic capability with an AUC value of 0.89, sensitivity of 0.79, specificity of 0.85, and Matthews correlation coefficient (MCC) of 0.74. SNV biomarkers also exhibited outstanding performances in three independent validation cohorts (AUCs = 0.83, 0.82, 0.76; sensitivity = 1.0, 0.72, 0.93; specificity = 0.67, 0.81, 0.67, MCCs = 0.69, 0.83, 0.72) with high disease specificity for adenoma. In further support of the above results, functional analyses revealed more frequent inter-kingdom associations between bacteria and fungi, and abnormalities in quorum sensing, purine and butanoate metabolism in adenoma, which were validated in a newly recruited cohort via qRT-PCR. Therefore, these data extend our understanding of adenoma-associated multimodal alterations in the gut microbiome and provide a rationale of microbial SNVs for the early detection of CRC.

Colonic Microbial Abundances Predict Adenoma Formers.

OBJECTIVE: We aimed to examine associations between the oral, fecal, and mucosal microbiome communities and adenoma formation. SUMMARY BACKGROUND DATA: Data are limited regarding the relationships between microbiota and preneoplastic colorectal lesions. METHODS: Individuals undergoing screening colonoscopy were prospectively enrolled and divided into adenoma and nonadenoma formers. Oral, fecal, nonadenoma and adenoma-adjacent mucosa were collected along with clinical and dietary information. 16S rRNA gene libraries were generated using V4 primers. DADA2 processed sequence reads and custom R-scripts quantified microbial diversity. Linear regression identified differential taxonomy and diversity in microbial communities and machine learning identified adenoma former microbial signatures. RESULTS: One hundred four subjects were included, 46% with adenomas. Mucosal and fecal samples were dominated by Firmicutes and Bacteroidetes whereas Firmicutes and Proteobacteria were most abundant in oral communities. Mucosal communities harbored significant microbial diversity that was not observed in fecal or oral communities. Random forest classifiers predicted adenoma formation using fecal, oral, and mucosal amplicon sequence variant (ASV) abundances. The mucosal classifier reliably diagnosed adenoma formation with an area under the curve (AUC) = 0.993 and an out-of-bag (OOB) error of 3.2%. Mucosal classifier accuracy was strongly influenced by five taxa associated with the family Lachnospiraceae, genera Bacteroides and Marvinbryantia, and Blautia obeum. In contrast, classifiers built using fecal and oral samples manifested high OOB error rates (47.3% and 51.1%, respectively) and poor diagnostic abilities (fecal and oral AUC = 0.53). CONCLUSION: Normal mucosa microbial abundances of adenoma formers manifest unique patterns of microbial diversity that may be predictive of adenoma formation.

Different Characteristics in Gut Microbiome between Advanced Adenoma Patients and Colorectal Cancer Patients by Metagenomic Analysis.

The occurrence and development of colorectal cancer (CRC) and advanced adenoma (AA) are closely related to the gut microbiome, and AA has a high cancerization progression rate to CRC. Current studies have revealed that bacteriological analysis cannot identify CRC from AA. The objective was to explore microbial targets that could identify CRC and AA from a microecological perspective and to figure out the best way to identify CRC based on fecal microbes. The metagenomic sequencing data were used to describe the gut microbiome profile and analyze the differences between microbial abundance and microbial single nucleotide polymorphism (SNP) characteristics in AA and CRC patients. It was found that there were no significant differences in the diversity between the two groups. The abundance of bacteria (e.g., Firmicutes, Clostridia, and Blautia), fungi (Hypocreales), archaea (Methanosarcina, Methanoculleus, and Methanolacinia), and viruses (Alphacoronavirus, Sinsheimervirus, and Gammaretrovirus) differed between AA and CRC patients. Multiple machine-learning algorithms were used to establish prediction models, aiming to identify CRC and AA. The accuracy of the random forest (RF) model based on the gut microbiome was 86.54%. Nevertheless, the accuracy of SNP was 92.31% in identifying CRC from AA. In conclusion, using microbial SNP was the best method to identify CRC, it was superior to using the gut microbiome, and it could provide new targets for CRC screening. IMPORTANCE There are differences in characteristic microorganisms between AA and CRC. However, current studies have indicated that bacteriological analysis cannot identify CC from AA, and thus, we wondered if there were some other targets that could be used to identify CRC from AA in the gut microbiome. The differences of SNPs in the gut microbiota of intraindividuals were significantly smaller than those of interindividuals. In addition, compared with intestinal microbes, SNP was less affected by time with certain stability. It was discovered that microbial SNP was better than the gut microbiome for identifying CRC from AA. Therefore, screening characteristic microbial SNP could provide a new research direction for identifying CRC from AA.

Enrichment of Prevotella intermedia in human colorectal cancer and its additive effects with Fusobacterium nucleatum on the malignant transformation of colorectal adenomas.

BACKGROUND: Owing to the heterogeneity of microbiota among individuals and populations, only Fusobacterium nucleatum and Bacteroides fragilis have been reported to be enriched in colorectal cancer (CRC) in multiple studies. Thus, the discovery of additional bacteria contributing to CRC development in various populations can be expected. We aimed to identify bacteria associated with the progression of colorectal adenoma to carcinoma and determine the contribution of these bacteria to malignant transformation in patients of Han Chinese origin. METHODS: Microbiota composition was determined through 16S rRNA V3-V4 amplicon sequencing of autologous adenocarcinomas, adenomatous polyps, and non-neoplastic colon tissue samples (referred to as "tri-part samples") in patients with CRC. Enriched taxa in adenocarcinoma tissues were identified through pairwise comparison. The abundance of candidate bacteria was quantified through genomic quantitative polymerase chain reaction (qPCR) in tissue samples from 116 patients. Associations of candidate bacteria with clinicopathological features and genomic and genetic alterations were evaluated through odds ratio tests. Additionally, the effects of candidate bacteria on CRC cell proliferation, migration, and invasion were evaluated through the co-culture of CRC cells with bacterial cells or with conditioned media from bacteria. RESULTS: Prevotella intermedia was overrepresented in adenocarcinomas compared with paired adenomatous polyps. Furthermore, co-abundance of P. intermedia and F. nucleatum was observed in tumor tissues. More notably, the coexistence of these two bacteria in adenocarcinomas was associated with lymph node involvement and distant metastasis. These two bacteria also exerted additive effects on the enhancement of the migration and invasion abilities of CRC cells. Finally, conditioned media from P. intermedia promoted the migration and invasion of CRC cells. CONCLUSION: This report is the first to demonstrate that P. intermedia is enriched in colorectal adenocarcinoma tissues and enhances the migration and invasion abilities of CRC cells. Moreover, P. intermedia and F. nucleatum exert additive effects on the malignant transformation of colorectal adenomas into carcinomas. These findings can be used to identify patients at a high risk of malignant transformation of colorectal adenomas or metastasis of CRC, and they can accordingly be provided optimal clinical management.

Crosstalk between mucosal microbiota, host gene expression, and sociomedical factors in the progression of colorectal cancer.

Various omics-based biomarkers related to the occurrence, progression, and prognosis of colorectal cancer (CRC) have been identified. In this study, we attempted to identify gut microbiome-based biomarkers and detect their association with host gene expression in the initiation and progression of CRC by integrating analysis of the gut mucosal metagenome, RNA sequencing, and sociomedical factors. We performed metagenome and RNA sequencing on colonic mucosa samples from 13 patients with advanced CRC (ACRC), 10 patients with high-risk adenoma (HRA), and 7 normal control (NC) individuals. All participants completed a questionnaire on sociomedical factors. The interaction and correlation between changes in the microbiome and gene expression were assessed using bioinformatic analysis. When comparing HRA and NC samples, which can be considered to represent the process of tumor initiation, 28 genes and five microbiome species were analyzed with correlation plots. When comparing ACRC and HRA samples, which can be considered to represent the progression of CRC, seven bacterial species and 21 genes were analyzed. When comparing ACRC and NC samples, 16 genes and five bacterial species were analyzed, and four correlation plots were generated. A network visualizing the relationship between bacterial and host gene expression in the initiation and progression of CRC indicated that Clostridium spiroforme and Tyzzerella nexilis were hub bacteria in the development and progression of CRC. Our study revealed the interactions of and correlation between the colonic mucosal microbiome and host gene expression to identify potential roles of the microbiome in the initiation and progression of CRC. Our results provide gut microbiome-based biomarkers that may be potential diagnostic markers and therapeutic targets in patients with CRC.

Comparison of two primer-probe sets of Fusobacterium nucleatum using droplet digital polymerase chain reaction for the detection of colorectal neoplasia from faecal samples.

BACKGROUND: Although faecal DNA testing of Fusobacterium nucleatum (Fn) is expected to be useful for colorectal neoplasia detection, there is no standardized quantification method of Fn. We performed this study to establish a possible standardized method. METHODS: In this study, 322 participants including 71 subjects without colorectal neoplasia (control group), 31 patients with non-advanced colorectal adenoma, 93 patients with advanced colorectal adenoma, and 127 patients with colorectal cancer were enrolled. Faecal Fn were quantified by droplet digital PCR (ddPCR) using two PCR primer-probe sets reported previously that are tentatively named Fn1 and Fn2. Fn1 has been used in ddPCR by us and Fn2 has been widely used in quantitative real-time PCR. RESULTS: The Fn copy number using Fn1 was five times higher than that using Fn2, with a linear relationship shown between them. Receiver operating characteristic curve analysis showed the area under the curve (AUC) to be almost the same between Fn1 and Fn2 in discriminating between the control group and the colorectal cancer group (AUC = 0.81 and 0.81, respectively), and between the control/non-advanced colorectal adenoma group and the advanced colorectal adenoma/colorectal cancer group (AUC = 0.74 and 0.74, respectively). CONCLUSIONS: As the diagnostic performance was quite similar between Fn1 and Fn2, ddPCR-based Fn testing using Fn1 and Fn2 could be a possible standardized method for a colorectal neoplasia screening test, considering that Fn levels quantified by Fn1 are about five times higher than those by Fn2.

Altered Mycobiota Signatures and Enriched Pathogenic Aspergillus rambellii Are Associated With Colorectal Cancer Based on Multicohort Fecal Metagenomic Analyses.

BACKGROUND & AIMS: The enteric mycobiota is a major component of the human gut microbiota, but its role in colorectal cancer (CRC) remains largely elusive. We conducted a meta-analysis to uncover the contribution of the fungal mycobiota to CRC. METHODS: We retrieved fecal metagenomic data sets from 7 previous publications and established an additional in-house cohort, totaling 1329 metagenomes (454 with CRC, 350 with adenoma, and 525 healthy individuals). Mycobiota composition and microbial interactions were analyzed. Candidate CRC-enriched fungal species (Aspergillus rambellii) was functionally validated in vitro and in vivo. RESULTS: Multicohort analysis revealed that the enteric mycobiota was altered in CRC. We identified fungi that were associated with patients with CRC or adenoma from multiple cohorts. Signature CRC-associated fungi included 6 enriched (A rambellii, Cordyceps sp. RAO-2017, Erysiphe pulchra, Moniliophthora perniciosa, Sphaerulina musiva, and Phytophthora capsici) and 1 depleted species (A kawachii). Co-occurrent interactions among CRC-enriched fungi became stronger in CRC compared with adenoma and healthy individuals. Moreover, we reported the transkingdom interactions between enteric fungi and bacteria in CRC progression, of which A rambellii was closely associated with CRC-enriched bacteria Fusobacterium nucleatum. A rambellii promoted CRC cell growth in vitro and tumor growth in xenograft mice. We further identified that combined fungal and bacterial biomarkers were more accurate than panels with pure bacterial species to discriminate patients with CRC from healthy individuals (the area under the curve relative change increased by 1.44%-10.60%). CONCLUSIONS: This study reveals enteric mycobiota signatures and pathogenic fungi in stages of colorectal tumorigenesis. Fecal fungi can be used, in addition to bacteria, for noninvasive diagnosis of patients with CRC.

Towards a metagenomics machine learning interpretable model for understanding the transition from adenoma to colorectal cancer.

Gut microbiome is gaining interest because of its links with several diseases, including colorectal cancer (CRC), as well as the possibility of being used to obtain non-intrusive predictive disease biomarkers. Here we performed a meta-analysis of 1042 fecal metagenomic samples from seven publicly available studies. We used an interpretable machine learning approach based on functional profiles, instead of the conventional taxonomic profiles, to produce a highly accurate predictor of CRC with better precision than those of previous proposals. Moreover, this approach is also able to discriminate samples with adenoma, which makes this approach very promising for CRC prevention by detecting early stages in which intervention is easier and more effective. In addition, interpretable machine learning methods allow extracting features relevant for the classification, which reveals basic molecular mechanisms accounting for the changes undergone by the microbiome functional landscape in the transition from healthy gut to adenoma and CRC conditions. Functional profiles have demonstrated superior accuracy in predicting CRC and adenoma conditions than taxonomic profiles and additionally, in a context of explainable machine learning, provide useful hints on the molecular mechanisms operating in the microbiota behind these conditions.

Association of Escherichia coli containing polyketide synthase in the gut microbiota with colorectal neoplasia in Japan.

Escherichia coli containing polyketide synthase in the gut microbiota (pks+ E coli) produce a polyketide-peptide genotoxin, colibactin, and are suspected to play a role in the development of colorectal neoplasia. To clarify the role of pks+ E coli in the early stage of tumorigenesis, we investigated whether the pks status of E coli was associated with the prevalence of colorectal neoplasia. This cross-sectional analysis of data from a prospective cohort in Izu Oshima, Japan included asymptomatic residents aged 40-79 years who underwent screening colonoscopy and provided a stool sample. We identified 543 participants with colorectal neoplasia (22 colorectal cancer and 521 adenoma) as cases and 425 participants with normal colon as controls. The pks status of E coli was assayed using stool DNA and specific primers that detected pks+ E coli. The proportion of pks+ E coli was 32.6% among cases and 30.8% among controls. Compared with those with pks- E coli, the odds ratio (OR) (95% confidence interval) for participants with pks+ E coli was 1.04 (0.77-1.41) after adjusting for potential confounders. No statistically significant associations were observed regardless of tumor site or number of colorectal adenoma lesions. However, stratified analyses revealed increased ORs among participants who consumed cereals over the median intake or vegetables under the median intake. Overall, we found no statistically significant association between pks+ E coli and the prevalence of colorectal adenoma lesions among this Japanese cohort. However, positive associations were suggested under certain intake levels of cereals or vegetables.

Helicobacter pylori infection and increased diabetes prevalence were the risks of colorectal adenoma for adults: A systematic review and meta-analysis (PRISMA-compliant article).

BACKGROUND: Helicobacter pylori infection and hyperglycemia are associated with an increased risk of colorectal neoplasm, and may have a synergistic effect in combination. However, these 2 factors that affect colorectal neoplasm remain controversial. We aimed to carry out a meta-analysis to evaluate the study population diabetes prevalence rate and H pylori infection rate with colorectal adenoma risk for adults. METHODS: We conducted systemic research through English databases for medical reports. We also recorded the diabetes prevalence and H pylori infection prevalence in each study. We classified these studies into 4 subgroups as their background population diabetes prevalence <6% (Group 1); between 6% and 8% (Group 2); between 8% and 10% (Group 3), and more than 10% (Group 4). The random-effects model had used to calculate pooled prevalence estimates with 95% confidence interval (CI). RESULTS: Twenty-seven studies were finally eligible for meta-analysis. The random-effects model of the meta-analysis was chosen, showing pooled odds ratio (OR) equal to 1.51 (95% CI 1.39-1.63). The subgroup meta-analyses showed in Group 1 the H pylori infection associated colorectal adenoma risk OR was 1.24 (95% CI 0.86-1.78). As the diabetes rate exceed 6%, the H pylori infection became the more significant increased risk of colorectal adenoma (Group 2: OR 2.16 (95% CI 1.61-2.91); Group 3: OR 1.40 (95% CI 1.24-1.57); and Group 4: OR 1.52 (95% CI 1.46-1.57)). CONCLUSIONS: The results of this meta-analysis showed elevated diabetes prevalence combined H pylori infection increasing the risks of colorectal adenoma in the adult population.

Faecal microbial biomarkers in early diagnosis of colorectal cancer.

Colorectal cancer (CRC) is ranked as the second most common cause of cancer deaths and the third most common cancer globally. It has been described as a 'silent disease' which is often easily treatable if detected early-before progression to carcinoma. Colonoscopy, which is the gold standard for diagnosis is not only expensive but is also an invasive diagnostic procedure, thus, effective and non-invasive diagnostic methods are urgently needed. Unfortunately, the current methods are not sensitive and specific enough in detecting adenomas and early colorectal neoplasia, hampering treatment and consequently, survival rates. Studies have shown that imbalances in such a relationship which renders the gut microbiota in a dysbiotic state are implicated in the development of adenomas ultimately resulting in CRC. The differences found in the makeup and diversity of the gut microbiota of healthy individuals relative to CRC patients have in recent times gained attention as potential biomarkers in early non-invasive diagnosis of CRC, with promising sensitivity, specificity and even cost-effectiveness. This review summarizes recent studies in the application of these microbiota biomarkers in early CRC diagnosis, limitations encountered in the area of the faecal microbiota studies as biomarkers for CRC, and future research exploits that address these limitations.

Aspirin Modulation of the Colorectal Cancer-Associated Microbe Fusobacterium nucleatum.

Aspirin is a chemopreventive agent for colorectal adenoma and cancer (CRC) that, like many drugs inclusive of chemotherapeutics, has been investigated for its effects on bacterial growth and virulence gene expression. Given the evolving recognition of the roles for bacteria in CRC, in this work, we investigate the effects of aspirin with a focus on one oncomicrobe-Fusobacterium nucleatum We show that aspirin and its primary metabolite salicylic acid alter F. nucleatum strain Fn7-1 growth in culture and that aspirin can effectively kill both actively growing and stationary Fn7-1. We also demonstrate that, at levels that do not inhibit growth, aspirin influences Fn7-1 gene expression. To assess whether aspirin modulation of F. nucleatum may be relevant in vivo, we use the ApcMin/+ mouse intestinal tumor model in which Fn7-1 is orally inoculated daily to reveal that aspirin-supplemented chow is sufficient to inhibit F. nucleatum-potentiated colonic tumorigenesis. We expand our characterization of aspirin sensitivity across other F. nucleatum strains, including those isolated from human CRC tissues, as well as other CRC-associated microbes, enterotoxigenic Bacteroides fragilis, and colibactin-producing Escherichia coli Finally, we determine that individuals who use aspirin daily have lower fusobacterial abundance in colon adenoma tissues, as determined by quantitative PCR performed on adenoma DNA. Together, our data support that aspirin has direct antibiotic activity against F. nucleatum strains and suggest that consideration of the potential effects of aspirin on the microbiome holds promise in optimizing risk-benefit assessments for use of aspirin in CRC prevention and management.IMPORTANCE There is an increasing understanding of the clinical correlations and potential mechanistic roles of specific members of the gut and tumoral microbiota in colorectal cancer (CRC) initiation, progression, and survival. However, we have yet to parlay this knowledge into better CRC outcomes through microbially informed diagnostic, preventive, or therapeutic approaches. Here, we demonstrate that aspirin, an established CRC chemopreventive, exhibits specific effects on the CRC-associated Fusobacterium nucleatum in culture, an animal model of intestinal tumorigenesis, and in human colonic adenoma tissues. Our work proposes a potential role for aspirin in influencing CRC-associated bacteria to prevent colorectal adenomas and cancer, beyond aspirin's canonical anti-inflammatory role targeting host tissues. Future research, such as studies investigating the effects of aspirin on fusobacterial load in patients, will help further elucidate the prospect of using aspirin to modulate F. nucleatumin vivo for improving CRC outcomes.

Microbial Community Heterogeneity Within Colorectal Neoplasia and its Correlation With Colorectal Carcinogenesis.

BACKGROUND & AIMS: Gut microbial dysbiosis has pivotal involvement in colorectal cancer (CRC). However, the intratumoral microbiota and its association with CRC progression remain elusive. We aimed to determine the microbial community architecture within a neoplasia (CRC or adenoma) and its contribution to colorectal carcinogenesis. METHODS: We collected 436 tissue biopsies from patients with CRC (n = 36) or adenoma (n = 32) (2-6 biopsies from a neoplasia plus 2-5 biopsies from adjacent normal tissues per individual). Microbial profiling was performed using 16S ribosomal RNA gene sequencing with subsequent investigation of microbiota diversities and heterogeneity. The correlation between microbial dysbiosis and host genetic alterations (KRAS mutation and microsatellite instability) in all neoplasia biopsies was also analyzed. RESULTS: We discovered that intra-neoplasia microbial communities are heterogeneous. Abundances of some CRC-associated pathobionts (eg, Fusobacterium, Bacteroides, Parvimonas, and Prevotella) were found to be highly varied within a single neoplasia. Correlation of such heterogeneity with CRC development revealed alterations in microbial communities involving microbes with high intra-neoplasia variation in abundance. Moreover, we found that the intra-neoplasia variation in abundance of individual microbes changed along the adenoma-carcinoma sequence. We further determined that there was a significant difference in intra-neoplasia microbiota between biopsies with and without KRAS mutation (P < .001) or microsatellite instability (P < .001), and illustrated the association of intratumoral microbial heterogeneity with genetic alteration. CONCLUSIONS: We demonstrated that intra-neoplasia microbiota is heterogeneous and correlated with colorectal carcinogenesis. Our findings provide new insights on the contribution of gut microbiota heterogeneity to CRC progression.

Serrated neoplasia in the colorectum: gut microbiota and molecular pathways.

Colorectal cancer (CRC) is a heterogeneous disease with different gene expression patterns. There are two major colorectal carcinogenesis pathways: conventional adenoma-carcinoma pathway and alternative serrated neoplasia pathway. Apart from the conventional pathway that is typically initiated by characteristic APC mutation and chromosomal instability, the serrated neoplasia pathway is mainly characterized by mutations of BRAF or KRAS, microsatellite instability (MSI), and CpG island methylator phenotype (CIMP). Despite the malignant potential of serrated lesions, they can be easily overlooked during endoscopy screening and even in pathological assessment due to its anatomical location, morphology, and histological features. It has been shown that environmental factors especially the gut microbial composition play a key role in CRC pathogenesis. Thus, the preferential localization of serrated lesions in specific intestine areas suggest that niche-specific microbiota composition might intertwined with host genetic perturbations during the development of serrated lesions. Although serrated lesions and conventional adenomas are biologically different, most studies have focused on conventional adenomas, while the pathophysiology and role of microorganisms in the development of serrated lesions remain elusive. In this review, we discuss on the role of gut microbiota in the serrated neoplasia pathway of colorectal carcinogenesis and its specific clinical and molecular features, and summarize the potential mechanisms involved.

Intestinal microbiota and its association with colon cancer and red/processed meat consumption.

The human colon harbors a high number of microorganisms that were reported to play a crucial role in colorectal carcinogenesis. In the recent decade, molecular detection and metabolomic techniques have expanded our knowledge on the role of specific microbial species in promoting tumorigenesis. In this study, we reviewed the association between microbial dysbiosis and colorectal carcinoma (CRC). Various microbial species and their association with colorectal tumorigenesis and red/processed meat consumption have been reviewed. The literature demonstrated a significant abundance of Fusobacterium nucleatum, Streptococcus bovis/gallolyticus, Escherichia coli, and Bacteroides fragilis in patients with adenoma or adenocarcinoma compared to healthy individuals. The mechanisms in which each organism was postulated to promote colon carcinogenesis were collated and summarized in this review. These include the microorganisms' ability to adhere to colon cells; modulate the inhibition of tumor suppressor genes, the activations of oncogenes, and genotoxicity; and activate downstream targets responsible for angiogenesis. The role of these microorganisms in conjugation with meat components including N-nitroso compounds, heterocyclic amines, and heme was also evident in multiple studies. The outcome of this review supports the role of red meat consumption in modulating CRC progression and the possibility of gut microbiome influencing the relationship between CRC and diet. The study also demonstrates that microbiota analysis could potentially complement existing screening methods when detecting colonic lesions.