Gut microbial subtypes and clinicopathological value for colorectal cancer.

BACKGROUND: Gut bacteria are related to colorectal cancer (CRC) and its clinicopathologic characteristics. OBJECTIVE: To develop gut bacterial subtypes and explore potential microbial targets for CRC. METHODS: Stool samples from 914 volunteers (376 CRCs, 363 advanced adenomas, and 175 normal controls) were included for 16S rRNA sequencing. Unsupervised learning was used to generate gut microbial subtypes. Gut bacterial community composition and clustering effects were plotted. Differences of gut bacterial abundance were analyzed. Then, the association of CRC-associated bacteria with subtypes and the association of gut bacteria with clinical information were assessed. The CatBoost models based on gut differential bacteria were constructed to identify the diseases including CRC and advanced adenoma (AA). RESULTS: Four gut microbial subtypes (A, B, C, D) were finally obtained via unsupervised learning. The characteristic bacteria of each subtype were Escherichia-Shigella in subtype A, Streptococcus in subtype B, Blautia in subtype C, and Bacteroides in subtype D. Clinical information (e.g., free fatty acids and total cholesterol) and CRC pathological information (e.g., tumor depth) varied among gut microbial subtypes. Bacilli, Lactobacillales, etc., were positively correlated with subtype B. Positive correlation of Blautia, Lachnospiraceae, etc., with subtype C and negative correlation of Coriobacteriia, Coriobacteriales, etc., with subtype D were found. Finally, the predictive ability of CatBoost models for CRC identification was improved based on gut microbial subtypes. CONCLUSION: Gut microbial subtypes provide characteristic gut bacteria and are expected to contribute to the diagnosis of CRC.

Gut microbiota alterations in colorectal adenoma-carcinoma sequence based on 16S rRNA gene sequencing: A systematic review and meta-analysis.

BACKGROUND: Most sporadic colorectal cancers (CRC) develop through the adenoma-carcinoma sequence. While dysbiosis of the intestinal flora contributes to CRC's pathogenesis, precise microbial taxa closely associated with the colorectal adenoma-carcinoma sequence remain elusive. This meta-analysis aimed to summarize the features of intestinal flora in patients with AD and CRC. METHODS: PubMed, Embase, Cochrane Library, and Web of Science were searched for case-control studies comparing the relative abundance of gut microbiota in the feces of patients with AD, CRC, and healthy controls (HC) from inception to January 2024. The weighted mean difference (WMD) with a 95 % confidence interval (CI) was used to display the results. The Newcastle-Ottawa Scale (NOS) was used to assess the quality of the entailed literature. Publication bias was evaluated with the Egger's and Begg's tests. RESULTS: Eleven studies were included, involving 477 CRC patients, 628 AD patients, and 864 healthy controls. Compared with HC, the patients with AD had a significantly lower Chao 1 index (WMD = -30.17, 95 % CI [-41.10, -19.23], P < 0.001) and Shannon index (WMD = -0.11 95 % CI [-0.18, -0.04], P = 0.002). Compared with AD, the CRC patients had a significantly higher Chao1 index (WMD = 22.09, 95 % CI [7.59, 36.00], P = 0.003) and Shannon index (WMD = 0.08, 95 % CI [0.00, 0.15], P = 0.037). Enterobacteriaceae (WMD = 0.03 95 % CI [0.00,0.05], P = 0.047; WMD = 0.02 95 % CI [0.00,0.04], P = 0.027) significantly increased in the order of Control-AD-CRC, while that of Blautia (WMD = -0.00 95 % CI [-0.01, -0.00], P = 0.001; WMD = -0.00 95 % CI [-0.00, -0.00], P = 0.002) was reduced. Compared with HC, the relative abundance of Proteobacteria (WMD = 0.05 95 % CI [0.03,0.07], P < 0.001), Fusobacteria (WMD = 0.02 95 % CI [0.00,0.03], P = 0.042), Streptococcaceae (WMD = 0.03 95 % CI [0.01,0.05], P = 0.017), Prevotellaceae (WMD = 0.02 95 % CI [0.00,0.04], P = 0.040), and Escherichia-Shigella (WMD = 0.06 95 % CI [0.01, 0.11], P = 0.021) was enriched in the CRC group. The relative abundance of Alistipes (WMD = 0.00 95 % CI [0.00,0.01], P = 0.032) and Streptococcus (WMD = 0.00 95 % CI [0.00,0.00], P = 0.001) was increased in the AD vs HC. The relative abundance of Firmicutes (WMD = -0.07 95 % CI [-0.12, -0.03], P = 0.003), Bifidobacteria (WMD = -0.03 95 % CI [-0.05, -0.01], P = 0.016), and Klebsiella (WMD = -0.01 95 % CI [-0.01, -0.00], P = 0.001) was decreased in the CRC vs HC. Compared with AD, the relative abundance of Firmicutes (WMD = -0.04 95 % CI [-0.07, -0.02], P = 0.002), Peptostreptococcaceae (WMD = -0.03 95 % CI [-0.05, -0.00], P = 0.021), Lachnospiraceae (WMD = -0.04 95 % CI [-0.08,-0.00], P = 0.037), Ruminococcaceae (WMD = -0.06 95 % CI [-0.09,-0.03], P < 0.001), Faecalibacterium (WMD = -0.01 95 % CI [-0.02, -0.01], P = 0.001), and Lachnoclostridium (WMD = -0.02 95 % CI [-0.03, -0.00], P = 0.040) was decreased in the CRC group, while Proteobacteria (WMD = 0.04 95 % CI [0.02,0.05], P < 0.001) was increased. CONCLUSIONS: The dysbiosis characterized by reduced levels of short-chain fatty acid (SCFA)-producing bacteria, decreased anti-inflammatory bacteria, increased pro-inflammatory bacteria, and an elevation of bacteria with cytotoxic effects damaging to DNA may represent the specific microbial signature of colorectal adenoma/carcinoma. Further research is required to elucidate the mechanisms by which gut dysbiosis leads to the progression from AD to CRC and to explore the potential of specific microbiota markers in clinical treatment and non-invasive screening.

Distinct gut microbiomes in Thai patients with colorectal polyps.

BACKGROUND: Colorectal polyps that develop via the conventional adenoma-carcinoma sequence [e.g., tubular adenoma (TA)] often progress to malignancy and are closely associated with changes in the composition of the gut microbiome. There is limited research concerning the microbial functions and gut microbiomes associated with colorectal polyps that arise through the serrated polyp pathway, such as hyperplastic polyps (HP). Exploration of microbiome alterations associated with HP and TA would improve the understanding of mechanisms by which specific microbes and their metabolic pathways contribute to colorectal carcinogenesis. AIM: To investigate gut microbiome signatures, microbial associations, and microbial functions in HP and TA patients. METHODS: Full-length 16S rRNA sequencing was used to characterize the gut microbiome in stool samples from control participants without polyps [control group (CT), n = 40], patients with HP (n = 52), and patients with TA (n = 60). Significant differences in gut microbiome composition and functional mechanisms were identified between the CT group and patients with HP or TA. Analytical techniques in this study included differential abundance analysis, co-occurrence network analysis, and differential pathway analysis. RESULTS: Colorectal cancer (CRC)-associated bacteria, including Streptococcus gallolyticus (S. gallolyticus), Bacteroides fragilis, and Clostridium symbiosum, were identified as characteristic microbial species in TA patients. Mediterraneibacter gnavus, associated with dysbiosis and gastrointestinal diseases, was significantly differentially abundant in the HP and TA groups. Functional pathway analysis revealed that HP patients exhibited enrichment in the sulfur oxidation pathway exclusively, whereas TA patients showed dominance in pathways related to secondary metabolite biosynthesis (e.g., mevalonate); S. gallolyticus was a major contributor. Co-occurrence network and dynamic network analyses revealed co-occurrence of dysbiosis-associated bacteria in HP patients, whereas TA patients exhibited co-occurrence of CRC-associated bacteria. Furthermore, the co-occurrence of SCFA-producing bacteria was lower in TA patients than HP patients. CONCLUSION: This study revealed distinct gut microbiome signatures associated with pathways of colorectal polyp development, providing insights concerning the roles of microbial species, functional pathways, and microbial interactions in colorectal carcinogenesis.

Gut microbes on the risk of advanced adenomas.

BACKGROUND: More than 90% of colorectal cancer (CRC) arises from advanced adenomas (AA) and gut microbes are closely associated with the initiation and progression of both AA and CRC. OBJECTIVE: To analyze the characteristic microbes in AA. METHODS: Fecal samples were collected from 92 AA and 184 negative control (NC). Illumina HiSeq X sequencing platform was used for high-throughput sequencing of microbial populations. The sequencing results were annotated and compared with NCBI RefSeq database to find the microbial characteristics of AA. R-vegan package was used to analyze α diversity and ß diversity. α diversity included box diagram, and ß diversity included Principal Component Analysis (PCA), principal co-ordinates analysis (PCoA), and non-metric multidimensional scaling (NMDS). The AA risk prediction models were constructed based on six kinds of machine learning algorithms. In addition, unsupervised clustering methods were used to classify bacteria and viruses. Finally, the characteristics of bacteria and viruses in different subtypes were analyzed. RESULTS: The abundance of Prevotella sp900557255, Alistipes putredinis, and Megamonas funiformis were higher in AA, while the abundance of Lilyvirus, Felixounavirus, and Drulisvirus were also higher in AA. The Catboost based model for predicting the risk of AA has the highest accuracy (bacteria test set: 87.27%; virus test set: 83.33%). In addition, 4 subtypes (B1V1, B1V2, B2V1, and B2V2) were distinguished based on the abundance of gut bacteria and enteroviruses (EVs). Escherichia coli D, Prevotella sp900557255, CAG-180 sp000432435, Phocaeicola plebeiuA, Teseptimavirus, Svunavirus, Felixounavirus, and Jiaodavirus are the characteristic bacteria and viruses of 4 subtypes. The results of Catboost model indicated that the accuracy of prediction improved after incorporating subtypes. The accuracy of discovery sets was 100%, 96.34%, 100%, and 98.46% in 4 subtypes, respectively. CONCLUSION: Prevotella sp900557255 and Felixounavirus have high value in early warning of AA. As promising non-invasive biomarkers, gut microbes can become potential diagnostic targets for AA, and the accuracy of predicting AA can be improved by typing.

Association of Helicobacter pylori infection with colorectal polyps/adenomas: A single-center cross-sectional study.

BACKGROUND: Helicobacter pylori (H. pylori) infection may be associated with colorectal polyps/adenomas, but the current evidence remains controversial. METHODS: We retrospectively screened the medical records of 655 participants who underwent both colonoscopy and H. pylori test from June 15, 2020 to April 30, 2023. The number, size, location, and pathological type of colorectal polyps/adenomas were compared between H. pylori positive and negative groups. Adjusting for age, gender, smoking, drinking, hypertension, diabetes, fatty liver, body mass index, and inflammatory and metabolic indicators, multivariate logistic regression analyses were performed to evaluate the association of H. pylori infection with the number, size, location, and pathological type of colorectal polyps/adenomas, where no polyp/adenoma was used as reference. RESULTS: Overall, 508 participants were included, of whom 154 and 354 were divided into H. pylori positive and negative groups, respectively. H. pylori positive group had significantly higher colorectal polyps/adenomas (74.7 % vs. 65.8 %, P=0.048), low-grade adenomas (55.7 % vs. 47.6 %, P=0.026), advanced adenomas (22.6 % vs. 13.3 %, P=0.008), and colorectal polyps/adenomas with sizes of ≥6 mm (61.7 % vs. 48.5 %, P=0.002) and ≥10 mm (25.2 % vs. 14.6 %, P=0.004) than H. pylori negative group. In multivariate logistic regression analyses, H. pylori infection was independently associated with low-grade adenomas (OR=2.677, 95 %CI=1.283-5.587, P=0.009), advanced adenomas (OR=3.017, 95 %CI=1.007-9.036, P=0.049), right-side colon polyps/adenomas (OR=5.553, 95 %CI=1.679-18.360, P=0.005), and colorectal polyps/adenomas with sizes of ≥10 mm (OR=4.436, 95 %CI=1.478-13.310, P=0.008), but not number of colorectal polyps/adenomas. CONCLUSION: H. pylori infection is associated with increased risk of colorectal polyps/adenomas, especially low-grade adenomas, advanced adenomas, right-side colon polyps/adenomas, and large colorectal polyps/adenomas.

Gastric dysplastic lesions in Helicobacter pylori-naïve stomach: Foveolar-type adenoma and intestinal-type dysplasia.

Reports of Helicobacter pylori (Hp)-naïve gastric neoplasm (HpNGN) cases have been rapidly increasing due to the recent increase in the Hp-naïve population in Japan. Most HpNGNs exhibit the gastric immunophenotype and a low malignant potential regardless of histological type. Especially, foveolar-type gastric adenoma (FGA) and intestinal-type gastric dysplasia (IGD) rarely progress to invasive carcinoma. FGA is a foveolar epithelial neoplasm that occurs in the fundic gland (oxyntic gland) mucosa and is classified as the flat type or raspberry type (FGA-RA). The flat type is a large, whitish flatly elevated lesion while FGA-RA is a small reddish polyp. Genomically, the flat type is characterized by APC and KRAS gene mutations and FGA-RA by a common single nucleotide variant in the KLF4 gene. This KLF4 single-nucleotide variant reportedly induces gastric foveolar epithelial tumorigenesis and activates both cell proliferation and apoptosis, leading to its slow-growing nature. IGD consists of an intestinalized epithelial dysplasia that develops in the pyloric gland mucosa, characterized as a superficial depressed lesion surrounded by raised mucosa showing a gastritis-like appearance. Immunohistochemically, it exhibits an intestinal or gastrointestinal phenotype and, frequently, p53 overexpression. Thus, IGD shows unique characteristics in HpNGNs and a potential multistep tumorigenic process.

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.

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.

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.

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.

Meta-analysis of the Correlation between Helicobacter Pylori Infection and the risk of Colorectal Neoplasia.

Objective: To study the association of H. pylori infection with colorectal adenomas. Methods: Web searches of PubMed, Embase, and Scopus databases for randomized controlled trials, class-experimental studies, and cohort studies on the association between H. pylori and colorectal adenomas were performed from May 2000 to May 2023. Literature was screened based on inclusion and exclusion criteria, data were extracted and evaluated for quality, and statistical analyses were performed using RevMan 5.2 software. Results: A total of 15 studies were included, and meta-analysis showed a statistically significant difference between colorectal neoplastic polyp cases in the H. pylori-positive and H. pylori-negative groups [OR=1.80, 95%CI: (1.31, 2.47), P < .01, I2 = 95%]. Analysis based on subgroups of different H. pylori detection methods showed that the correlation between H. pylori infection and colorectal polyp incidence is not affected by their detection methods, with serological detection subgroup: [OR=0.13, 95%CI: (0.05, 0.21), P < .01, I2 = 88%], and non-serological detection subgroup: [OR=0.13, 95%CI: (0.04, 0.22), P < .01, I2 = 95%]. Subgroup analysis of pathological types showed that H. pylori infection is not significantly associated with the development of non-neoplastic polyps [OR=1.47, 95%CI: 0.98-2.22, P = .06], whereas it is correlated with the development of neoplastic polyps [95%CI: 1.69-3.22, P < .01]. In the subgroup analysis of geographic differences in the population, H. pylori infection was correlated with the development of colorectal polyps in different geographic populations (P < .01). Conclusion: H. pylori infection is a risk factor for colorectal polyp neoplasia, its infection is associated with colorectal neoplasia, and the correlation is not affected by the different methods of H. pylori detection and the different geographic regions of the population.

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.