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.

Advances in bacteria-based drug delivery systems for anti-tumor therapy.

In recent years, bacteria have gained considerable attention as a promising drug carrier that is critical in improving the effectiveness and reducing the side effects of anti-tumor drugs. Drug carriers can be utilised in various forms, including magnetotactic bacteria, bacterial biohybrids, minicells, bacterial ghosts and bacterial spores. Additionally, functionalised and engineered bacteria obtained through gene engineering and surface modification could provide enhanced capabilities for drug delivery. This review summarises the current studies on bacteria-based drug delivery systems for anti-tumor therapy and discusses the prospects and challenges of bacteria as drug carriers. Furthermore, our findings aim to provide new directions and guidance for the research on bacteria-based drug systems.

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.

Identification of enterotype and its predictive value for patients with colorectal cancer.

BACKGROUND: Gut microbiota dysbiosis involved in the pathogenesis of colorectal cancer (CRC). The characteristics of enterotypes in CRC development have not been determined. OBJECTIVE: To characterize the gut microbiota of healthy, adenoma, and CRC subjects based on enterotype. METHODS: The 16 S rRNA sequencing data from 315 newly sequenced individuals and three previously published datasets were collected, providing total data for 367 healthy, 320 adenomas, and 415 CRC subjects. Enterotypes were analyzed for all samples, and differences in microbiota composition across subjects with different disease states in each enterotype were determined. The predictive values of a random forest classifier based on enterotype in distinguishing healthy, adenoma, and CRC subjects were evaluated and validated. RESULTS: Subjects were classified into one of three enterotypes, namely, Bacteroide- (BA_E), Blautia- (BL_E), and Streptococcus- (S_E) dominated clusters. The taxonomic profiles of these three enterotypes differed among the healthy, adenoma, and CRC cohorts. BA_E group was enriched with Bacteroides and Blautia; BL_E group was enriched by Blautia and Coprococcus; S_E was enriched by Streptococcus and Ruminococcus. Relative abundances of these genera varying among the three human cohorts. In training and validation sets, the S_E cluster showed better performance in distinguishing among CRC patients, adenoma patients, and healthy controls, as well as between CRC and non-CRC individuals, than the other two clusters. CONCLUSION: This study provides the first evidence to indicate that changes in the microbial composition of enterotypes are associated with disease status, thereby highlighting the diagnostic potential of enterotypes in the treatment of adenoma and CRC.

Correlation of gut microbiota with leukopenia after chemotherapy in patients with colorectal cancer.

BACKGROUND: The most common toxic side effect after chemotherapy, one of the main treatments for colorectal cancer (CRC), is myelosuppression. OBJECTIVE: To analyze the correlation between gut microbiota and leukopenia after chemotherapy in CRC patients. METHODS: Stool samples were collected from 56 healthy individuals and 55 CRC patients. According to the leukocytes levels in peripheral blood, the CRC patients were divided into hypoleukocytes group (n = 13) and normal leukocytes group (n = 42). Shannon index, Simpson index, Ace index, Chao index and Coverage index were used to analyze the diversity of gut microbiota. LDA and Student's t-test(St test) were used for analysis of differences. Six machine learning algorithms, including logistic regression (LR) algorithm, random forest (RF) algorithm, neural network (NN) algorithm, support vector machine (SVM) algorithm, catboost algorithm and gradient boosting tree algorithm, were used to construct the prediction model of gut microbiota with leukopenia after chemotherapy for CRC. RESULTS: Compared with healthy group, the microbiota alpha diversity of CRC patients was significantly decreased (p < 0.05). After analyzing the gut microbiota differences of the two groups, 15 differential bacteria, such as Bacteroides, Faecalibacterium and Streptococcus, were screened. RF prediction model had the highest accuracy, and the gut microbiota with the highest predictive value were Peptostreptococcus, Faecalibacterium, and norank_f__Ruminococcaceae, respectively. Compared with normal leukocytes group, the microbiota alpha diversity of hypoleukocytes group was significantly decreased (p < 0.05). The proportion of Escherichia-Shigella was significantly decreased in the hypoleukocytes group. After analyzing the gut microbiota differences of the two groups, 9 differential bacteria, such as Escherichia-Shigella, Fusicatenibacter and Cetobacterium, were screened. RF prediction model had the highest accuracy, and the gut microbiota with the highest predictive value were Fusicatenibacte, Cetobacterium, and Paraeggerthella. CONCLUSION: Gut microbiota is related to leukopenia after chemotherapy. The gut microbiota may provide a novel method for predicting myelosuppression after chemotherapy in CRC patients.

Application and progress of highcontent imaging in molecular biology.

Humans have adopted many different methods to explore matter imaging, among which high content imaging (HCI) could conduct automated imaging analysis of cells while maintaining its structural and functional integrity. Meanwhile, as one of the most important research tools for diagnosing human diseases, HCI is widely used in the frontier of medical research, and its future application has attracted researchers' great interests. Here, the meaning of HCI was briefly explained, the history of optical imaging and the birth of HCI were described, and the experimental methods of HCI were described. Furthermore, the directions of the application of HCI were highlighted in five aspects: protein localization changes, gene identification, chemical and genetic analysis, microbiology, and drug discovery. Most importantly, some challenges and future directions of HCI were discussed, and the application and optimization of HCI were expected to be further explored.

Gut microbes involvement in gastrointestinal cancers through redox regulation.

Gastrointestinal (GI) cancers are among the most common and lethal cancers worldwide. GI microbes play an important role in the occurrence and development of GI cancers. The common mechanisms by which GI microbes may lead to the occurrence and development of cancer include the instability of the microbial internal environment, secretion of cancer-related metabolites, and destabilization of the GI mucosal barrier. In recent years, many studies have found that the relationship between GI microbes and the development of cancer is closely associated with the GI redox level. Redox instability associated with GI microbes may induce oxidative stress, DNA damage, cumulative gene mutation, protein dysfunction and abnormal lipid metabolism in GI cells. Redox-related metabolites of GI microbes, such as short-chain fatty acids, hydrogen sulfide and nitric oxide, which are involved in cancer, may also influence GI redox levels. This paper reviews the redox reactions of GI cells regulated by microorganisms and their metabolites, as well as redox reactions in the cancer-related GI microbes themselves. This study provides a new perspective for the prevention and treatment of GI cancers.

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.