Metagenomic analysis of the interaction between the gut microbiota and colorectal cancer: a paired-sample study based on the GMrepo database

Using New Technologies to Analyze Gut Microbiota and Predict Cancer Risk

The gut microbiota significantly impacts human health, influencing metabolism, immunological responses, and disease prevention. Dysbiosis, or microbial imbalance, is linked to various diseases, including cancer. It is crucial to preserve a healthy microbiome since pathogenic bacteria, such as Escherichia coli and Fusobacterium nucleatum, can cause inflammation and cancer. These pathways can lead to the formation of tumors. Recent advancements in high-throughput sequencing, metagenomics, and machine learning have revolutionized our understanding of the role of gut microbiota in cancer risk prediction. Early detection is made easier by machine learning algorithms that improve the categorization of cancer kinds based on microbiological data. Additionally, the investigation of the microbiome has been transformed by next-generation sequencing (NGS), which has made it possible to fully profile both cultivable and non-cultivable bacteria and to understand their roles in connection with cancer. Among the uses of NGS are the detection of microbial fingerprints connected to treatment results and the investigation of metabolic pathways implicated in the development of cancer. The combination of NGS with machine learning opens up new possibilities for creating customized medicine by enabling the development of diagnostic tools and treatments that are specific to each patient's microbiome profile, even in the face of obstacles like data complexity. Multi-omics studies reveal microbial interactions, biomarkers for cancer detection, and gut microbiota's impact on cancer progression, underscoring the need for further research on microbiome-based cancer prevention and therapy.

The role of Akkermansia muciniphila in colorectal cancer: A double-edged sword of treatment or disease progression?

Colorectal cancer (CRC) is the second most cancer-related death worldwide. In recent years, probiotics have been used to reduce the potential risks of CRC and tumors with various mechanisms. Different bacteria have been suggested to play different roles in the progression, prevention, or treatment of CRC. Akkermansia muciniphila is considered a next-generation probiotic for preventing and treating some diseases. Therefore, in this review article, we aimed to describe and discuss different mechanisms of A. muciniphila as an intestinal microbiota or probiotic in CRC. Some studies suggested that the abundance of A. muciniphila was higher or increased in CRC patients compared to healthy individuals. However, the decreased abundance of A. muciniphila was associated with severe symptoms of CRC, indicating that A. muciniphila did not play a role in the development of CRC. In addition, A. muciniphila administration elevates gene expression of proliferation-associated molecules such as S100A9, Dbf4, and Snrpd1, or markers for cell proliferation. Some other studies suggested that inflammation and tumorigenesis in the intestine might promoted by A. muciniphila. Overall, the role of A. muciniphila in CRC development or inhibition is still unclear and controversial. Various methods of bacterial supplementation, such as viability, bacterial number, and abundance, could all influence the colonization effect of A. muciniphila administration and CRC progression. Overall, A. mucinipila has been revealed to modulate the therapeutic potential of immune checkpoint inhibitors. Preliminary human data propose that oral consumption of A. muciniphila is safe, but its efficacy needs to be confirmed in more human clinical studies.

Explainable artificial intelligence for microbiome data analysis in colorectal cancer biomarker identification

Background

Colorectal cancer (CRC) is a type of tumor caused by the uncontrolled growth of cells in the mucosa lining the last part of the intestine. Emerging evidence underscores an association between CRC and gut microbiome dysbiosis. The high mortality rate of this cancer has made it necessary to develop new early diagnostic methods. Machine learning (ML) techniques can represent a solution to evaluate the interaction between intestinal microbiota and host physiology. Through explained artificial intelligence (XAI) it is possible to evaluate the individual contributions of microbial taxonomic markers for each subject. Our work also implements the Shapley Method Additive Explanations (SHAP) algorithm to identify for each subject which parameters are important in the context of CRC.

Results

The proposed study aimed to implement an explainable artificial intelligence framework using both gut microbiota data and demographic information from subjects to classify a cohort of control subjects from those with CRC. Our analysis revealed an association between gut microbiota and this disease. We compared three machine learning algorithms, and the Random Forest (RF) algorithm emerged as the best classifier, with a precision of 0.729 ± 0.038 and an area under the Precision-Recall curve of 0.668 ± 0.016. Additionally, SHAP analysis highlighted the most crucial variables in the model's decision-making, facilitating the identification of specific bacteria linked to CRC. Our results confirmed the role of certain bacteria, such as Fusobacterium, Peptostreptococcus, and Parvimonas, whose abundance appears notably associated with the disease, as well as bacteria whose presence is linked to a non-diseased state.

Discussion

These findings emphasizes the potential of leveraging gut microbiota data within an explainable AI framework for CRC classification. The significant association observed aligns with existing knowledge. The precision exhibited by the RF algorithm reinforces its suitability for such classification tasks. The SHAP analysis not only enhanced interpretability but identified specific bacteria crucial in CRC determination. This approach opens avenues for targeted interventions based on microbial signatures. Further exploration is warranted to deepen our understanding of the intricate interplay between microbiota and health, providing insights for refined diagnostic and therapeutic strategies.

Reexamining the role of Fusobacterium nucleatum subspecies in clinical and experimental studies

The Gram-negative anaerobic species Fusobacterium nucleatum was originally described as a commensal organism from the human oral microbiome. However, it is now widely recognized as a key inflammophilic pathobiont associated with a wide variety of oral and extraoral diseases. Historically, F. nucleatum has been classified into four subspecies that have been generally considered as functionally interchangeable in their pathogenic potential. Recent studies have challenged this notion, as clinical data reveal a highly biased distribution of F. nucleatum subspecies within disease sites of both inflammatory oral diseases and various malignancies. This review details the historical basis for the F. nucleatum subspecies designations and summarizes our current understanding of the similarities and distinctions between these organisms to provide important context for future clinical and laboratory studies of F. nucleatum.