Prediction model of colorectal cancer (CRC) lymph node metastasis based on intestinal bacteria

Colorectal cancer in Lynch syndrome families: consequences of gene germline mutations and the gut microbiota

BACKGROUND

Lynch syndrome (LS)-associated colorectal cancer (CRC) always ascribes to pathogenic germline mutations in mismatch repair (MMR) genes. However, the penetrance of CRC varies among those with the same MMR gene mutation. Thus, we hypothesized that the gut microbiota is also involved in CRC development in LS families.

METHODS

This prospective, observational study was performed from December 2020 to March 2023. We enrolled 72 individuals from 9 LS families across six provinces in China and employed 16S rRNA gene amplicon sequencing to analyze the fecal microbiota components among LS-related CRC patients (AS group), their spouses (BS group), mutation carriers without CRC (CS group), and non-mutation carriers (DS group) using alpha and beta diversity indices.

RESULTS

There were no apparent differences in age or gender among the four groups. Alpha and beta diversity indices exhibited no significant differences between the AS and BS groups, verifying the role of germline mutations in the occurrence of CRC in LS families. Beta diversity analysis exhibited significant differences between the AS and CS groups, revealing the importance of the gut microbiota for the occurrence of CRC in LS families. A greater difference (both alpha and beta diversity indices) was shown between the AS and DS groups, demonstrating the combined impact of the gut microbiota and genetic germline mutations on the occurrence of CRC in LS families. Compared with those in the CS and DS groups, we identified ten microbial genera enriched in the AS group, and one genus (Bacteroides) decreased in the AS group. Among the elevated genera in the AS group, Agathobacter, Coprococcus and Prevotellaceae_NK3B31_group were butyrate-producing genera.

CONCLUSION

This study found the development of CRC in the LS families can be attributed to the combined effects of gene germline mutations as well as the gut microbiota and provided novel insights into the prevention and treatment of CRC in the LS families.

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 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 β diversity. α diversity included box diagram, and β 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.

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.

Healthy diet intervention reverses the progression of NASH through gut microbiota modulation

IMPORTANCE

The link between gut microbiota and diet is crucial in the development of non-alcoholic steatohepatitis (NASH). This study underscores the essential role of a healthy diet in preventing and treating NASH by reversing obesity, lipidemia, and gut microbiota dysbiosis. Moreover, the supplementation of functional food or drug to the diet can provide additional advantages by inhibiting hepatic inflammation through the modulation of the hepatic inflammasome signaling pathway and partially mediating the gut microbiota and lipopolysaccharide signaling pathway. This study highlights the importance of adopting healthy dietary habits in treating NASH and proposes that supplementing with ginger essential oil or obeticholic acid may offer additional benefits. Nonetheless, further clinical studies are necessary to validate these findings.

Specific gut microbiome signature predicts hepatitis B virus-related hepatocellular carcinoma patients with microvascular invasion

BACKGROUND

We aimed to assess differences in intestinal microflora between patients with operable hepatitis B virus-related hepatocellular carcinoma (HBV-HCC) with microvascular invasion (MVI) and those without MVI. Additionally, we investigated the potential of the microbiome as a non-invasive biomarker for patients with MVI.

METHODS

We analyzed the preoperative gut microbiomes (GMs) of two groups, the MVI (n = 46) and non-MVI (n = 56) groups, using 16S ribosomal RNA gene sequencing data. At the operational taxonomic unit level, we employed random forest models to predict MVI risk and validated the results in independent validation cohorts [MVI group (n = 17) and non-MVI group (n = 15)].

RESULTS

β diversity analysis, utilizing weighted UniFrac distances, revealed a significant difference between the MVI and non-MVI groups, as indicated by non-metric multidimensional scaling and principal coordinate analysis. We also observed a significant correlation between the characteristic intestinal microbial communities at the genus level and their main functions. Nine optimal microbial markers were identified, with an area under the curve of 79.76% between 46 MVI and 56 non-MVI samples and 79.80% in the independent verification group.

CONCLUSION

This pioneering analysis of the GM in patients with operable HBV-HCC with and without MVI opens new avenues for treating HBV-HCC with MVI. We successfully established a diagnostic model and independently verified microbial markers for patients with MVI. As preoperative targeted biomarkers, GM holds potential as a non-invasive tool for patients with HBV-HCC with MVI.

An Analysis of the Gene Expression Associated with Lymph Node Metastasis in Colorectal Cancer

Objective

This study aimed to explore the genes regulating lymph node metastasis in colorectal cancer (CRC) and to clarify their relationship with tumor immune cell infiltration and patient prognoses.

Methods

The data sets of CRC patients were collected through the Cancer Gene Atlas database; the differentially expressed genes (DEGs) associated with CRC lymph node metastasis were screened; a protein-protein interaction (PPI) network was constructed; the top 20 hub genes were selected; the Gene Ontology functions and the Kyoto Encyclopedia of Genes and Genomes pathways were enriched and analyzed. The Least Absolute Shrinkage and Selection Operator (LASSO) regression method was employed to further screen the characteristic genes associated with CRC lymph node metastasis in 20 hub genes, exploring the correlation between the characteristic genes and immune cell infiltration, conducting a univariate COX analysis on the characteristic genes, obtaining survival-related genes, constructing a risk score formula, conducting a Kaplan-Meier analysis based on the risk score formula, and performing a multivariate COX regression analysis on the clinical factors and risk scores.

Results

A total of 62 DEGs associated with CRC lymph node metastasis were obtained. Among the 20 hub genes identified via PPI, only calcium-activated chloride channel regulator 1 (CLCA1) expression was down-regulated in lymph node metastasis, and the rest were up-regulated. A total of nine characteristic genes associated with CRC lymph node metastasis (KIF1A, TMEM59L, CLCA1, COL9A3, GDF5, TUBB2B, STMN2, FOXN1, and SCN5A) were screened using the LASSO regression method. The nine characteristic genes were significantly related to different kinds of immune cell infiltration, from which three survival-related genes (TMEM59L, CLCA1, and TUBB2B) were screened. A multi-factor COX regression showed that the risk scores obtained from TMEM59L, CLCA1, and TUBB2B were independent prognostic factors. Immunohistochemical validation was performed in tissue samples from patients with rectal and colon cancer.

Conclusion

TMEM59L, CLCA1, and TUBB2B were independent prognostic factors associated with lymphatic metastasis of CRC.

Association between Gut Microbiota and Digestive System Cancers: A Bidirectional Two-Sample Mendelian Randomization Study

Accumulating evidence indicates that gut microbiota closely correlates with the tumorigenesis of digestive system cancers (DSCs). However, whether the causality between gut microbiota and DSCs exists is unknown. Genome-wide association study (GWAS) summary statistics for gut microbiota and DSCs and the bidirectional two-sample Mendelian randomization (MR) analysis were utilized to assess the causality between gut microbiota and DSCs. Sensitivity analyses were performed to evaluate the robustness of our results. We found that the genus Eggerthella (OR = 0.464, 95%CI: 0.27 to 0.796, p = 0.005) was negatively associated with the risk of gastric cancer. The genetically predicted genus Lachnospiraceae FCS020 group (OR = 0.607, 95%CI: 0.439 to 0.84, p = 0.003) correlated with a lower risk of colorectal cancer, and genus Turicibacter (OR = 0.271, 95%CI: 0.109 to 0.676, p = 0.005) was a protective factor for liver cancer. In the reverse MR, DSCs regulated the relative abundance of specific strains of gut microbiota. We comprehensively screened the association between gut microbiota and DSCs using a bidirectional two-sample MR analysis and identified the causality between several microbial taxa and DSCs. Our discoveries are beneficial for the development of novel microbial markers and microbiota-modifying therapeutics for DSC patients.