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

Third-generation PacBio sequencing to explore gut bacteria and gender in colorectal cancer

BACKGROUND

Gut bacteria have an important influence on colorectal cancer (CRC). The differences of gut bacteria between genders have been the hot spots.

OBJECTIVE

To analyze the relationship between gut bacteria and gender differences in patients with CRC.

METHODS

A total of 212 patients with CRC and 212 healthy volunteers were recruited. The subjects' fecal samples were obtained, and the fecal microorganisms were analyzed by the third-generation sequencing PacBio. The composition of gut bacteria was analyzed. Linear discriminant analysis Effect Size (LEfSe) was used to analyze the differences in gut bacteria. Pearson coefficient was used to calculate the correlation between differential bacteria. CRC risk prediction models were used to rank the importance of effective differential bacteria.

RESULTS

Escherichia flexneri and Phocaeicola vulgatus were the most frequent bacteria in both male and female CRC patients. Bacteroides, Verrucomicrobia and Akkermansiaceae were highly enriched in male CRC group, while Bacteroidetes, Phocaeicola and Tissierellales were highly enriched in female CRC group. Peptostreptococcus anaerobius and Phocaeicola vulgatus were important CRC related bacteria in males and females, respectively. Peptostreptococcus anaerobius was the most important characteristic bacterium of males (AUC = 0.951), and the sensitivity and specificity of the discovery set were 78.74 % and 93.98 %, respectively. Blautia stercoris was the most important characteristic bacterium of females (AUC = 0.966), and the sensitivity and specificity of the discovery set were 90.63 % and 90.63 %, respectively.

CONCLUSION

Gut bacteria varied in different genders. Therefore, gender should be considered when gut bacteria are applied in the diagnose and prevention of CRC.

Application of Fusobacterium nucleatum as a biomarker in gastrointestinal malignancies

The morbidity and mortality of gastrointestinal (GI) malignancies are among the highest in the world, posing a serious threat to human health. Because of the insidious onset of the cancer, it is difficult for patients to be diagnosed at an early stage, and it rapidly progresses to an advanced stage, resulting in poor treatment and prognosis. Fusobacterium nucleatum (F. nucleatum) is a gram-negative, spore-free anaerobic bacterium that primarily colonizes the oral cavity and is implicated in the development of colorectal, esophageal, gastric, and pancreatic cancers via various intricate mechanisms. Recent development in novel research suggests that F. nucleatum may function as a biomarker in GI malignancies. Detecting the abundance of F. nucleatum in stool, saliva, and serum samples of patients may aid in the diagnosis, risk assessment, and prognosis monitoring of GI malignancies. This editorial systematically describes the biological roles and mechanisms of F. nucleatum in GI malignancies focusing on the application of F. nucleatum as a biomarker in the diagnosis and prognosis of GI malignancies to promote the clinical translation of F. nucleatum and GI tumors-related research.

Co-evolution of vaginal microbiome and cervical cancer

BACKGROUND

Exploration of adaptive evolutionary changes at the genetic level in vaginal microbial communities during different stages of cervical cancer remains limited. This study aimed to elucidate the mutational profile of the vaginal microbiota throughout the progression of cervical disease and subsequently establish diagnostic models.

METHODS

This study utilized a metagenomic dataset consisting of 151 subjects classified into four categories: invasive cervical cancer (CC) (n = 42), cervical intraepithelial neoplasia (CIN) (n = 43), HPV-infected (HPVi) patients without cervical lesions (n = 34), and healthy controls (n = 32). The analysis focused on changes in microbiome abundance and extracted information on genetic variation. Consequently, comprehensive multimodal microbial signatures associated with CC, encompassing taxonomic alterations, mutation signatures, and enriched metabolic functional pathways, were identified. Diagnostic models for predicting CC were established considering gene characteristics based on single nucleotide variants (SNVs).

RESULTS

In this study, we screened and analyzed the abundances of 18 key microbial strains during CC progression. Additionally, 71,6358 non-redundant mutations were identified, predominantly consisting of SNVs that were further annotated into 25,773 genes. Altered abundances of SNVs and mutation types were observed across the four groups. Specifically, there were 9847 SNVs in the HPV-infected group and 14,892 in the CC group. Furthermore, two distinct mutation signatures corresponding to the benign and malignant groups were identified. The enriched metabolic pathways showed limited similarity with only two overlapping pathways among the four groups. HPVi patients exhibited active nucleotide biosynthesis, whereas patients with CC demonstrated a significantly higher abundance of signaling and cellular-associated protein families. In contrast, healthy controls showed a distinct enrichment in sugar metabolism. Moreover, biomarkers based on microbial SNV abundance displayed stronger diagnostic capability (cc.AUC = 0.87) than the species-level biomarkers (cc.AUC = 0.78). Ultimately, the integration of multimodal biomarkers demonstrated optimal performance for accurately identifying different cervical statuses (cc.AUC = 0.86), with an acceptable performance (AUC = 0.79) in the external testing set.

CONCLUSIONS

The vaginal microbiome exhibits specific SNV evolution in conjunction with the progression of CC, and serves as a specific biomarker for distinguishing between different statuses of cervical disease.

Exploring Gut Microbiome Composition and Circulating Microbial DNA Fragments in Patients with Stage II/III Colorectal Cancer: A Comprehensive Analysis

BACKGROUND

Colorectal cancer (CRC) significantly contributes to cancer-related mortality, necessitating the exploration of prognostic factors beyond TNM staging. This study investigates the composition of the gut microbiome and microbial DNA fragments in stage II/III CRC.

METHODS

A cohort of 142 patients with stage II/III CRC and 91 healthy controls underwent comprehensive microbiome analysis. Fecal samples were collected for 16S rRNA sequencing, and blood samples were tested for the presence of microbial DNA fragments. De novo clustering analysis categorized individuals based on their microbial profiles. Alpha and beta diversity metrics were calculated, and taxonomic profiling was conducted.

RESULTS

Patients with CRC exhibited distinct microbial composition compared to controls. Beta diversity analysis confirmed CRC-specific microbial profiles. Taxonomic profiling revealed unique taxonomies in the patient cohort. De novo clustering separated individuals into distinct groups, with specific microbial DNA fragment detection associated with certain patient clusters.

CONCLUSIONS

The gut microbiota can differentiate patients with CRC from healthy individuals. Detecting microbial DNA fragments in the bloodstream may be linked to CRC prognosis. These findings suggest that the gut microbiome could serve as a prognostic factor in stage II/III CRC. Identifying specific microbial markers associated with CRC prognosis has potential clinical implications, including personalized treatment strategies and reduced healthcare costs. Further research is needed to validate these findings and uncover underlying mechanisms.

Identification and validation of microbial biomarkers from cross-cohort datasets using xMarkerFinder

Microbial signatures have emerged as promising biomarkers for disease diagnostics and prognostics, yet their variability across different studies calls for a standardized approach to biomarker research. Therefore, we introduce xMarkerFinder, a four-stage computational framework for microbial biomarker identification with comprehensive validations from cross-cohort datasets, including differential signature identification, model construction, model validation and biomarker interpretation. xMarkerFinder enables the identification and validation of reproducible biomarkers for cross-cohort studies, along with the establishment of classification models and potential microbiome-induced mechanisms. Originally developed for gut microbiome research, xMarkerFinder's adaptable design makes it applicable to various microbial habitats and data types. Distinct from existing biomarker research tools that typically concentrate on a singular aspect, xMarkerFinder uniquely incorporates a sophisticated feature selection process, specifically designed to address the heterogeneity between different cohorts, extensive internal and external validations, and detailed specificity assessments. Execution time varies depending on the sample size, selected algorithm and computational resource. Accessible via GitHub ( https://github.com/tjcadd2020/xMarkerFinder ), xMarkerFinder supports users with diverse expertise levels through different execution options, including step-to-step scripts with detailed tutorials and frequently asked questions, a single-command execution script, a ready-to-use Docker image and a user-friendly web server ( https://www.biosino.org/xmarkerfinder ).

Multi-kingdom microbial signatures in excess body weight colorectal cancer based on global metagenomic analysis

Excess body weight (EBW) increases the risk of colorectal cancer (CRC) and is linked to lower colonoscopy compliance. Here, we extensively analyzed 981 metagenome samples from multiple cohorts to pinpoint the specific microbial signatures and their potential capability distinguishing EBW patients with CRC. The gut microbiome displayed considerable variations between EBW and lean CRC. We identify 44 and 37 distinct multi-kingdom microbial species differentiating CRC and controls in EBW and lean populations, respectively. Unique bacterial-fungal associations are also observed between EBW-CRC and lean-CRC. Our analysis revealed specific microbial functions in EBW-CRC, including D-Arginine and D-ornithine metabolism, and lipopolysaccharide biosynthesis. The best-performing classifier for EBW-CRC, comprising 12 bacterial and three fungal species, achieved an AUROC of 0.90, which was robustly validated across three independent cohorts (AUROC = 0.96, 0.94, and 0.80). Pathogenic microbial species, Anaerobutyricum hallii, Clostridioides difficile and Fusobacterium nucleatum, are EBW-CRC specific signatures. This work unearths the specific multi-kingdom microbial signatures for EBW-CRC and lean CRC, which may contribute to precision diagnosis and treatment of CRC.