Action of m6A-related gene signatures on the prognosis and immune microenvironment of colonic adenocarcinoma.

N6-methyladenosine (m6A) modification in human tumor cells exerts considerable influence on crucial processes like tumorigenesis, invasion, metastasis, and immune response. This study aims to comprehensively analyze the impact of m6A-related genes on the prognosis and immune microenvironment (IME) of colonic adenocarcinoma (COAD). Public data sources, predictive algorithms identified m6A-related genes and differential gene expression in COAD. Subtype analysis and assessment of immune cell infiltration patterns were performed using consensus clustering and the CIBERSORT algorithm. The Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis determined gene signatures. Independent prognostic factors were identified using univariate and multivariate Cox proportional hazards models. The findings indicate that 206 prognostic m6A-related DEGs contribute to the m6A regulatory network along with 8 m6A enzymes. Based on the expression levels of these genes, 438 COAD samples from The Cancer Genome Atlas (TCGA) were classified into 3 distinct subtypes, showing marked differences in survival prognosis, clinical characteristics, and immune cell infiltration profiles. Subtype 3 and 2 displayed reduced levels of infiltrating regulatory T cells and M0 macrophages, respectively. A six-gene signature, encompassing KLC3, SLC6A15, AQP7 JMJD7, HOXC6, and CLDN9, was identified and incorporated into a prognostic model. Validation across TCGA and GSE39582 datasets exhibited robust predictive specificity and sensitivity in determining the survival status of COAD patients. Additionally, independent prognostic factors were recognized, and a nomogram model was developed as a prognostic predictor for COAD. In conclusion, the six target genes governed by m6A mechanisms offer substantial potential in predicting COAD outcomes and provide insights into the unique IME profiles associated with various COAD subtypes.

Age and aging process alter the gut microbes.

BACKGROUND: Gut microbes and age are both factors that influence the development of disease. The community structure of gut microbes is affected by age. OBJECTIVE: To plot time-dependent gut microbe profiles in individuals over 45 years old and explore the correlation between age and gut microbes. METHODS: Fecal samples were collected from 510 healthy individuals over 45 years old. Shannon index, Simpson index, Ace index, etc. were used to analyze the diversity of gut microbes. The beta diversity analysis, including non-metric multidimensional scaling (NMDS), was used to analyze community distribution. Linear discriminant analysis (LDA) and random forest (RF) algorithm were used to analyze the differences of gut microbes. Trend analysis was used to plot the abundances of characteristic gut microbes in different ages. RESULTS: The individuals aged 45-49 had the highest richness of gut bacteria. Fifteen characteristic gut microbes, including Siphoviridae and Bifidobacterium breve, were screened by RF algorithm. The abundance of Ligiactobacillus and Microviridae were higher in individuals older than 65 years. Moreover, the abundance of Blautia_A massiliensis, Lubbockvirus and Enterocloster clostridioformis decreased with age and the abundance of Klebsiella variicola and Prevotella increased with age. The functional genes, such as human diseases and aging, were significantly different among different aged individuals. CONCLUSIONS: The individuals in different ages have characteristic gut microbes. The changes in community structure of gut microbes may be related to age-induced diseases.

Prediction model of poorly differentiated colorectal cancer (CRC) based on gut bacteria.

BACKGROUND: The mortality of colorectal cancer is high, the malignant degree of poorly differentiated colorectal cancer is high, and the prognosis is poor. OBJECTIVE: To screen the characteristic intestinal microbiota of poorly differentiated intestinal cancer. METHODS: Fecal samples were collected from 124 patients with moderately differentiated CRC and 123 patients with poorly differentiated CRC, and the bacterial 16S rRNA V1-V4 region of the fecal samples was sequenced. Alpha diversity analysis was performed on fecal samples to assess the diversity and abundance of flora. The RDP classifier Bayesian algorithm was used to analyze the community structure. Linear discriminant analysis and Student's t test were used to screen the differences in flora. The PICRUSt1 method was used to predict the bacterial function, and six machine learning models, including logistic regression, random forest, neural network, support vector machine, CatBoost and gradient boosting decision tree, were used to construct a prediction model for the poor differentiation of colorectal cancer. RESULTS: There was no significant difference in fecal flora alpha diversity between moderately and poorly differentiated colorectal cancer (P > 0.05). The bacteria that accounted for a large proportion of patients with poorly differentiated and moderately differentiated colorectal cancer were Blautia, Escherichia-Shigella, Streptococcus, Lactobacillus, and Bacteroides. At the genus level, there were nine bacteria with high abundance in the poorly differentiated group, including Bifidobacterium, norank_f__Oscillospiraceae, Eisenbergiella, etc. There were six bacteria with high abundance in the moderately differentiated group, including Megamonas, Erysipelotrichaceae_UCG-003, Actinomyces, etc. The RF model had the highest prediction accuracy (100.00% correct). The bacteria that had the greatest variable importance in the model were Pseudoramibacter, Megamonas and Bifidobacterium. CONCLUSION: The degree of pathological differentiation of colorectal cancer was related to gut flora, and poorly differentiated colorectal cancer had some different bacterial flora, and intestinal bacteria can be used as biomarkers for predicting poorly differentiated CRC.