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

A review of machine learning methods for cancer characterization from microbiome data

Recent studies have shown that the microbiome can impact cancer development, progression, and response to therapies suggesting microbiome-based approaches for cancer characterization. As cancer-related signatures are complex and implicate many taxa, their discovery often requires Machine Learning approaches. This review discusses Machine Learning methods for cancer characterization from microbiome data. It focuses on the implications of choices undertaken during sample collection, feature selection and pre-processing. It also discusses ML model selection, guiding how to choose an ML model, and model validation. Finally, it enumerates current limitations and how these may be surpassed. Proposed methods, often based on Random Forests, show promising results, however insufficient for widespread clinical usage. Studies often report conflicting results mainly due to ML models with poor generalizability. We expect that evaluating models with expanded, hold-out datasets, removing technical artifacts, exploring representations of the microbiome other than taxonomical profiles, leveraging advances in deep learning, and developing ML models better adapted to the characteristics of microbiome data will improve the performance and generalizability of models and enable their usage in the clinic.

Obesity and early-onset colorectal cancer risk: emerging clinical evidence and biological mechanisms

Early-onset colorectal cancer (EOCRC) is defined as diagnosed at younger than 50 years of age and indicates a health burden globally. Patients with EOCRC have distinct risk factors, clinical characteristics, and molecular pathogenesis compared with older patients with CRC. Further investigations have identified different roles of obesity between EOCRC and late-onset colorectal cancer (LOCRC). Most studies have focused on the clinical characteristics of obesity in EOCRC, therefore, the mechanism involved in the association between obesity and EOCRC remains inconclusive. This review further states that obesity affects the carcinogenesis of EOCRC as well as its development and progression, which may lead to obesity-related metabolic syndrome, intestinal dysbacteriosis, and intestinal inflammation.

Fusobacteria alterations are associated with colorectal cancer liver metastasis and a poor prognosis

Liver metastasis is a major cause of mortality in patients with advanced stages of colorectal cancer (CRC). The gut microbiota has been demonstrated to influence the progression of liver diseases, potentially providing novel perspectives for diagnosis, treatment and research. However, the gut microbial characteristics in CRC with liver metastasis (LM) and with no liver metastasis (NLM) have not yet been fully established. In the present study, high-throughput 16S RNA sequencing technology was employed, in order to examine the gut microbial richness and composition in patients with CRC with LM or NLM. A discovery cohort (cohort 2; LM=18; NLM=36) and a validation cohort (cohort 3; LM=13; NLM=41) were established using fresh feces. In addition, primary carcinoma tissue samples were also analyzed (LM=8 and NLM=10) as a supplementary discovery cohort (cohort 1). The findings of the present study indicated that the intestinal microbiota richness and diversity were increased in the LM group as compared to the NLM group. A significant difference was observed in species composition between the LM and NLM group. In the two discovery cohorts with two different samples, the dominant phyla were consistent, but varied at lower taxonomic levels. Phylum Fusobacteria presented consistent and significant enrichment in LM group in both discovery cohorts. Furthermore, with the application of a random forest model and receiver operator characteristic curve analysis, Fusobacteria was identified as a potential biomarker for LM. Moreover, Fusobacteria was also a poor prognosis factor for survival. Importantly, the findings were reconfirmed in the validation cohort. On the whole, the findings of the present study demonstrated that CRC with LM and NLM exhibit distinct gut microbiota characteristics. Fusobacteria detection thus has potential for use in predicting LM and a poor prognosis of patients with CRC.

Investigating the influence of the gut microbiome on cholelithiasis: unveiling insights through sequencing and predictive modeling

BACKGROUND

Cholelithiasis is one of the most common disorders of hepatobiliary system. Gut bacteria may be involved in the process of gallstone formation and are, therefore considered as potential targets for cholelithiasis prediction.

OBJECTIVE

To reveal the correlation between cholelithiasis and gut bacteria.

METHODS

Stool samples were collected from 100 cholelithiasis and 250 healthy individuals from Huzhou Central Hospital; The 16S rRNA of gut bacteria in the stool samples was sequenced using the third-generation Pacbio sequencing platform; Mothur v.1.21.1 was used to analyze the diversity of gut bacteria; Wilcoxon rank-sum test and linear discriminant analysis of effect sizes (LEfSe) were used to analyze differences in gut bacteria between patients suffering from cholelithiasis and healthy individuals; Chord diagram and Plot-related heat maps were used to analyze the correlation between cholelithiasis and gut bacteria; six machine algorithms were used to construct models to predict cholelithiasis.

RESULTS

There were differences in the abundance of gut bacteria between cholelithiasis and healthy individuals, but there were no differences in their community diversity. Increased abundance of Costridia, Escherichia flexneri, and Klebsiella pneumonae were found in cholelithiasis, while Bacteroidia, Phocaeicola, and Phocaeicola vulgatus were more abundant in healthy individuals. The top four bacteria that were most closely associated with cholelithiasis were Escherichia flexneri, Escherichia dysenteriae, Streptococcus salivarius, and Phocaeicola vulgatus. The cholelithiasis model based on CatBoost algorithm had the best prediction effect (sensitivity: 90.48%, specificity: 88.32%, and AUC: 0.962).

CONCLUSION

The identification of characteristic gut bacteria may provide new predictive targets for gallstone screening. As being screened by the predictive model, people at high risk of cholelithiasis can determine the need for further testing, thus enabling early warning of cholelithiasis.

Lung cancer progression alters lung and gut microbiomes and lipid metabolism

Despite advances in medical technology, lung cancer still has one of the highest mortality rates among all malignancies. Therefore, efforts must be made to understand the precise mechanisms underlying lung cancer development. In this study, we conducted lung and gut microbiome analyses and a comprehensive lipid metabolome analysis of host tissues to assess their correlation. Alternations in the lung microbiome due to lung cancer, such as a significantly decreased abundance of Firmicutes and Deferribacterota, were observed compared to a mock group. However, mice with lung cancer had significantly lower relative abundances of Actinobacteria and Proteobacteria and higher relative abundances of Cyanobacteria and Patescibacteria in the gut microbiome. The activations of retinol, fatty acid metabolism, and linoleic acid metabolism metabolic pathways in the lung and gut microbiomes was inversely correlated. Additionally, changes occurred in lipid metabolites not only in the lungs but also in the blood, small intestine, and colon. Compared to the mock group, mice with lung cancer showed that the levels of adrenic, palmitic, stearic, and oleic (a ω-9 polyunsaturated fatty acid) acids increased in the lungs. Conversely, these metabolites consistently decreased in the blood (serum) and colon. Leukotriene B4 and prostaglandin E2 exacerbate lung cancer, and were upregulated in the lungs of the mice with lung cancer. However, isohumulone, a peroxisome proliferator-activated receptor gamma activator, and resolvin (an ω-3 polyunsaturated fatty acid) both have anti-cancer effects, and were upregulated in the small intestine and colon. Our multi-omics data revealed that shifts in the microbiome and metabolome occur during the development of lung cancer and are of possible clinical importance. These results reveal one of the gut-lung axis mechanisms related to lung cancer and provide insights into potential new targets for lung cancer treatment and prophylaxis.

The impact of the gut microbiome on tumor immunotherapy: from mechanism to application strategies

Immunotherapy is one of the fastest developing areas in the field of oncology. Many immunological treatment strategies for refractory tumors have been approved and marketed. Nevertheless, much clinical and preclinical experimental evidence has shown that the efficacy of immunotherapy in tumor treatment varies markedly among individuals. The commensal microbiome mainly colonizes the intestinal lumen in humans, is affected by a variety of factors and exhibits individual variation. Moreover, the gut is considered the largest immune organ of the body due to its influence on the immune system. In the last few decades, with the development of next-generation sequencing (NGS) techniques and in-depth research, the view that the gut microbiota intervenes in antitumor immunotherapy through the immune system has been gradually confirmed. Here, we review important studies published in recent years focusing on the influences of microbiota on immune system and the progression of malignancy. Furthermore, we discuss the mechanism by which microbiota affect tumor immunotherapy, including immune checkpoint blockade (ICB) and adoptive T-cell therapy (ACT), and strategies for modulating the microbial composition to facilitate the antitumor immune response. Finally, opportunity and some challenges are mentioned to enable a more systematic understanding of tumor treatment in the future and promote basic research and clinical application in related fields.

Metagenomic analysis reveals the relationship between intestinal protozoan parasites and the intestinal microecological balance in calves

BACKGROUND

A close connection between a protozoan parasite and the balance of the other gut microbes of the host has been demonstrated. The calves may be naturally co-infected with many parasites, and the co-effects of parasites on other intestinal microbes of calves remain unclear. This study aims to preliminarily reveal the relationship between intestinal parasites and other intestinal microbes in calves.

METHODS

Fecal samples were collected from four calves with bloody diarrhea, four calves with watery diarrhea, and seven normal calves, and the microbial flora of the samples were analyzed by whole-genome sequencing. Protozoal parasites were detected in the metagenome sequences and identified using polymerase chain reaction (PCR).

RESULTS

Cryptosporidium, Eimeria, Giardia, Blastocystis, and Entamoeba were detected by metagenomic analysis, and the identified species were Giardia duodenalis assemblage E, Cryptosporidium bovis, Cryptosporidium ryanae, Eimeria bovis, Eimeria subspherica, Entamoeba bovis, and Blastocystis ST2 and ST10. Metagenomic analysis showed that the intestinal microbes of calves with diarrhea were disordered, especially in calves with bloody diarrhea. Furthermore, different parasites show distinct relationships with the intestinal microecology. Cryptosporidium, Eimeria, and Giardia were negatively correlated with various intestinal bacteria but positively correlated with some fungi. However, Blastocystis and Entamoeba were positively associated with other gut microbes. Twenty-seven biomarkers not only were significantly enriched in bloody diarrhea, watery diarrhea, and normal calves but were also associated with Eimeria, Cryptosporidium, and Giardia. Only Eimeria showed a distinct relationship with seven genera of bacteria, which were significantly enriched in the healthy calves. All 18 genera of fungi were positively correlated with Cryptosporidium, Eimeria, and Giardia, which were also significantly enriched in calves with bloody diarrhea. Functional genes related to parasites and diseases were found mainly in fungi.

CONCLUSIONS

This study revealed the relationship between intestinal protozoan parasites and the other calf gut microbiome. Different intestinal protozoan parasites have diametrically opposite effects on other gut microecology, which not only affects bacteria in the gut, but also is significantly related to fungi and archaea.

Basal Diet Fed to Recipient Mice Was the Driving Factor for Colitis and Colon Tumorigenesis, despite Fecal Microbiota Transfer from Mice with Severe or Mild Disease

Consumption of the total Western diet (TWD) in mice has been shown to increase gut inflammation, promote colon tumorigenesis, and alter fecal microbiome composition when compared to mice fed a healthy diet, i.e., AIN93G (AIN). However, it is unclear whether the gut microbiome contributes directly to colitis-associated CRC in this model. The objective of this study was to determine whether dynamic fecal microbiota transfer (FMT) from donor mice fed either the AIN basal diet or the TWD would alter colitis symptoms or colitis-associated CRC in recipient mice, which were fed either the AIN diet or the TWD, using a 2 × 2 factorial experiment design. Time-matched FMT from the donor mice fed the TWD did not significantly enhance symptoms of colitis, colon epithelial inflammation, mucosal injury, or colon tumor burden in the recipient mice fed the AIN diet. Conversely, FMT from the AIN-fed donors did not impart a protective effect on the recipient mice fed the TWD. Likewise, the composition of fecal microbiomes of the recipient mice was also affected to a much greater extent by the diet they consumed than by the source of FMT. In summary, FMT from the donor mice fed either basal diet with differing colitis or tumor outcomes did not shift colitis symptoms or colon tumorigenesis in the recipient mice, regardless of the basal diet they consumed. These observations suggest that the gut microbiome may not contribute directly to the development of disease in this animal model.