Associations between the gut microbiome and colorectal cancer (CRC) have been uncovered, but larger and more diverse studies are needed to assess their potential clinical use. We expanded upon 12 metagenomic datasets of patients with CRC (n = 930), adenomas (n = 210) and healthy control individuals (n = 976; total n = 2,116) with 6 new cohorts (n = 1,625) providing granular information on cancer stage and the anatomic location of tumors. We improved CRC prediction accuracy based solely on gut metagenomics (average area under the curve = 0.85) and highlighted the contribution of 19 newly profiled species and distinct Fusobacterium nucleatum clades. Specific gut species distinguish left-sided versus right-sided CRC (area under the curve = 0.66) with an enrichment of oral-typical microbes. We identified strain-specific CRC signatures with the commensal Ruminococcus bicirculans and Faecalibacterium prausnitzii showing subclades associated with late-stage CRC. Our analysis confirms that the microbiome can be a clinical target for CRC screening and characterizes it as a biomarker for CRC progression.

• MeSH
• adenom mikrobiologie   patologie   genetika   MeSH
• Faecalibacterium prausnitzii genetika   MeSH
• feces * mikrobiologie   MeSH
• Fusobacterium nucleatum genetika   MeSH
• kohortové studie MeSH
• kolorektální nádory * mikrobiologie   patologie   genetika   diagnóza   MeSH
• lidé středního věku MeSH
• lidé MeSH
• metagenom * genetika   MeSH
• metagenomika metody   MeSH
• nádorové biomarkery * genetika   MeSH
• Ruminococcus genetika   MeSH
• senioři MeSH
• střevní mikroflóra * genetika   MeSH
• Check Tag
• lidé středního věku MeSH
• lidé MeSH
• mužské pohlaví MeSH
• senioři MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• Názvy látek
• nádorové biomarkery * MeSH

Although metagenomic sequencing is now the preferred technique to study microbiome-host interactions, analyzing and interpreting microbiome sequencing data presents challenges primarily attributed to the statistical specificities of the data (e.g., sparse, over-dispersed, compositional, inter-variable dependency). This mini review explores preprocessing and transformation methods applied in recent human microbiome studies to address microbiome data analysis challenges. Our results indicate a limited adoption of transformation methods targeting the statistical characteristics of microbiome sequencing data. Instead, there is a prevalent usage of relative and normalization-based transformations that do not specifically account for the specific attributes of microbiome data. The information on preprocessing and transformations applied to the data before analysis was incomplete or missing in many publications, leading to reproducibility concerns, comparability issues, and questionable results. We hope this mini review will provide researchers and newcomers to the field of human microbiome research with an up-to-date point of reference for various data transformation tools and assist them in choosing the most suitable transformation method based on their research questions, objectives, and data characteristics.

• Klíčová slova
• compositionality, data preprocessing, human microbiome, machine learning, metagenomics data, normalization,
• Publikační typ
• časopisecké články MeSH
• přehledy MeSH

BACKGROUND: Mechanistic data indicate the benefit of short-chain fatty acids (SCFA) produced by gut microbial fermentation of fiber on colorectal cancer, but direct epidemiologic evidence is limited. A recent study identified SNPs for two SCFA traits (fecal propionate and butyrate-producing microbiome pathway PWY-5022) in Europeans and showed metabolic benefits. METHODS: We conducted a two-sample Mendelian randomization analysis of the genetic instruments for the two SCFA traits (three SNPs for fecal propionate and nine for PWY-5022) in relation to colorectal cancer risk in three large European genetic consortia of 58,131 colorectal cancer cases and 67,347 controls. We estimated the risk of overall colorectal cancer and conducted subgroup analyses by sex, age, and anatomic subsites of colorectal cancer. RESULTS: We did not observe strong evidence for an association of the genetic predictors for fecal propionate levels and the abundance of PWY-5022 with the risk of overall colorectal cancer, colorectal cancer by sex, or early-onset colorectal cancer (diagnosed at <50 years), with no evidence of heterogeneity or pleiotropy. When assessed by tumor subsites, we found weak evidence for an association between PWY-5022 and risk of rectal cancer (OR per 1-SD, 0.95; 95% confidence intervals, 0.91-0.99; P = 0.03) but it did not surpass multiple testing of subgroup analysis. CONCLUSIONS: Genetic instruments for fecal propionate levels and the abundance of PWY-5022 were not associated with colorectal cancer risk. IMPACT: Fecal propionate and PWY-5022 may not have a substantial influence on colorectal cancer risk. Future research is warranted to comprehensively investigate the effects of SCFA-producing bacteria and SCFAs on colorectal cancer risk.

• MeSH
• butyráty * analýza   metabolismus   MeSH
• feces * chemie   mikrobiologie   MeSH
• kolorektální nádory * epidemiologie   genetika   metabolismus   MeSH
• kyseliny mastné těkavé analýza   genetika   metabolismus   MeSH
• lidé MeSH
• mendelovská randomizace MeSH
• propionáty * analýza   metabolismus   MeSH
• riziko MeSH
• střevní mikroflóra * genetika   fyziologie   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Research Support, N.I.H., Extramural MeSH
• Geografické názvy
• Evropa epidemiologie   MeSH
• Názvy látek
• butyráty * MeSH
• kyseliny mastné těkavé MeSH
• propionáty * MeSH

The frequent occurrence of E. coli positive for cyclomodulins such as colibactin (CLB), the cytotoxic necrotizing factor (CNF), and the cytolethal distending factor (CDT) in colorectal cancer (CRC) patients published so far provides the opportunity to use them as CRC screening markers. We examined the practicability and performance of a low-cost detection approach that relied on culture followed by simplified DNA extraction and PCR in E. coli isolates recovered from 130 CRC patients and 111 controls. Our results showed a statistically significant association between CRC and the presence of colibactin genes clbB and clbN, the cnf gene, and newly, the hemolytic phenotype of E. coli isolates. We also observed a significant increase in the mean number of morphologically distinct E. coli isolates per patient in the CRC cohort compared to controls, indicating that the cyclomodulin-producing E. coli strains may represent potentially preventable harmful newcomers in CRC patients. A colibactin gene assay showed the highest detection rate (45.4%), and males would benefit from the screening more than females. However, because of the high number of false positives, practical use of this marker must be explored. In our opinion, it may serve as an auxiliary marker to increase the specificity and/or sensitivity of the well-established fecal immunochemical test (FIT) in CRC screening.

• Klíčová slova
• colibactin, colorectal cancer, cytotoxic necrotizing factor, genotoxin, screening,
• Publikační typ
• časopisecké články MeSH

Long-term dysbiosis of the gut microbiome has a significant impact on colorectal cancer (CRC) progression and explains part of the observed heterogeneity of the disease. Even though the shifts in gut microbiome in the normal-adenoma-carcinoma sequence were described, the landscape of the microbiome within CRC and its associations with clinical variables remain under-explored. We performed 16S rRNA gene sequencing of paired tumour tissue, adjacent visually normal mucosa and stool swabs of 178 patients with stage 0-IV CRC to describe the tumour microbiome and its association with clinical variables. We identified new genera associated either with CRC tumour mucosa or CRC in general. The tumour mucosa was dominated by genera belonging to oral pathogens. Based on the tumour microbiome, we stratified CRC patients into three subtypes, significantly associated with prognostic factors such as tumour grade, sidedness and TNM staging, BRAF mutation and MSI status. We found that the CRC microbiome is strongly correlated with the grade, location and stage, but these associations are dependent on the microbial environment. Our study opens new research avenues in the microbiome CRC biomarker detection of disease progression while identifying its limitations, suggesting the need for combining several sampling sites (e.g., stool and tumour swabs).

• Klíčová slova
• 16S rRNA gene, colorectal cancer, microbial subtypes, tumour microbiome,
• Publikační typ
• časopisecké články MeSH

The number of microbiome-related studies has notably increased the availability of data on human microbiome composition and function. These studies provide the essential material to deeply explore host-microbiome associations and their relation to the development and progression of various complex diseases. Improved data-analytical tools are needed to exploit all information from these biological datasets, taking into account the peculiarities of microbiome data, i.e., compositional, heterogeneous and sparse nature of these datasets. The possibility of predicting host-phenotypes based on taxonomy-informed feature selection to establish an association between microbiome and predict disease states is beneficial for personalized medicine. In this regard, machine learning (ML) provides new insights into the development of models that can be used to predict outputs, such as classification and prediction in microbiology, infer host phenotypes to predict diseases and use microbial communities to stratify patients by their characterization of state-specific microbial signatures. Here we review the state-of-the-art ML methods and respective software applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on the application of ML in microbiome studies related to association and clinical use for diagnostics, prognostics, and therapeutics. Although the data presented here is more related to the bacterial community, many algorithms could be applied in general, regardless of the feature type. This literature and software review covering this broad topic is aligned with the scoping review methodology. The manual identification of data sources has been complemented with: (1) automated publication search through digital libraries of the three major publishers using natural language processing (NLP) Toolkit, and (2) an automated identification of relevant software repositories on GitHub and ranking of the related research papers relying on learning to rank approach.

• Klíčová slova
• biomarker identification, disease prediction, feature selection, machine learning, microbiome,
• Publikační typ
• časopisecké články MeSH
• přehledy MeSH

Dysbiotic configurations of the human gut microbiota have been linked to colorectal cancer (CRC). Human small noncoding RNAs are also implicated in CRC, and recent findings suggest that their release in the gut lumen contributes to shape the gut microbiota. Bacterial small RNAs (bsRNAs) may also play a role in carcinogenesis, but their role has been less extensively explored. Here, we performed small RNA and shotgun sequencing on 80 stool specimens from patients with CRC or with adenomas and from healthy subjects collected in a cross-sectional study to evaluate their combined use as a predictive tool for disease detection. We observed considerable overlap and a correlation between metagenomic and bsRNA quantitative taxonomic profiles obtained from the two approaches. We identified a combined predictive signature composed of 32 features from human and microbial small RNAs and DNA-based microbiome able to accurately classify CRC samples separately from healthy and adenoma samples (area under the curve [AUC] = 0.87). In the present study, we report evidence that host-microbiome dysbiosis in CRC can also be observed by examination of altered small RNA stool profiles. Integrated analyses of the microbiome and small RNAs in the human stool may provide insights for designing more-accurate tools for diagnostic purposes.IMPORTANCE The characteristics of microbial small RNA transcription are largely unknown, while it is of primary importance for a better identification of molecules with functional activities in the gut niche under both healthy and disease conditions. By performing combined analyses of metagenomic and small RNA sequencing (sRNA-Seq) data, we characterized both the human and microbial small RNA contents of stool samples from healthy individuals and from patients with colorectal carcinoma or adenoma. With the integrative analyses of metagenomic and sRNA-Seq data, we identified a human and microbial small RNA signature which can be used to improve diagnosis of the disease. Our analysis of human and gut microbiome small RNA expression is relevant to generation of the first hypotheses about the potential molecular interactions occurring in the gut of CRC patients, and it can be the basis for further mechanistic studies and clinical tests.

• Klíčová slova
• gut microbiome, human stool samples, microRNAs, small RNAs,
• Publikační typ
• časopisecké články MeSH

Association studies have linked microbiome alterations with many human diseases. However, they have not always reported consistent results, thereby necessitating cross-study comparisons. Here, a meta-analysis of eight geographically and technically diverse fecal shotgun metagenomic studies of colorectal cancer (CRC, n = 768), which was controlled for several confounders, identified a core set of 29 species significantly enriched in CRC metagenomes (false discovery rate (FDR) < 1 × 10-5). CRC signatures derived from single studies maintained their accuracy in other studies. By training on multiple studies, we improved detection accuracy and disease specificity for CRC. Functional analysis of CRC metagenomes revealed enriched protein and mucin catabolism genes and depleted carbohydrate degradation genes. Moreover, we inferred elevated production of secondary bile acids from CRC metagenomes, suggesting a metabolic link between cancer-associated gut microbes and a fat- and meat-rich diet. Through extensive validations, this meta-analysis firmly establishes globally generalizable, predictive taxonomic and functional microbiome CRC signatures as a basis for future diagnostics.

• MeSH
• adenom genetika   mikrobiologie   MeSH
• biologické modely MeSH
• databáze genetické MeSH
• druhová specificita MeSH
• feces mikrobiologie   MeSH
• kohortové studie MeSH
• kolorektální nádory genetika   mikrobiologie   MeSH
• lidé středního věku MeSH
• lidé MeSH
• metagenom * MeSH
• nádorové biomarkery metabolismus   MeSH
• reprodukovatelnost výsledků MeSH
• senioři MeSH
• střevní mikroflóra genetika   MeSH
• Check Tag
• lidé středního věku MeSH
• lidé MeSH
• mužské pohlaví MeSH
• senioři MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• metaanalýza MeSH
• Research Support, N.I.H., Extramural MeSH
• Názvy látek
• nádorové biomarkery MeSH