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Abstract Background Researchers gradually focus on the relationship between phage and cancer. Objective To summarize the research hotspots and trends in the field of bacteriophage and cancer. Methods The downloaded articles were searched from the Web of Science Core Collection database from January 2008 to June 2023. Bibliometric analysis was carried out through CiteSpace, including the analysis of cooperative networks (country/region, institution, and author), co-citations of references, and key words.Visual analysis of three topics, including gut phage, phage and bacteria, and phage and tumor, was conducted. Results Overall, the United States and China have the most phage-related research. In terms of gut phage, the future research directions are “gut microbiome”, “database” and “microbiota”. The bursting citations explored the phage-dominated viral genome to discover its diversity and individual specificity and investigated associations among bacteriome, metabolome, and virome. In terms of phage and bacteria, “lipopolysaccharide” and “microbiota” are future research directions. Future research hotspots should mainly concentrate on the further exploration and application of phage properties. As for phages and tumors, the future research directions should be "colorectal cancer", "protein" and "phage therapy". Future directions are likely to focus on the research on phages in cancer mechanisms, cancer diagnosis, and cancer treatment combined with genetic engineering techniques. Conclusion Phage therapy would become a hot spot and research direction of tumor and phage research, and the relationship between phage and tumor, especially colorectal cancer (CRC), is expected to be further explored.

Abstract Escherichia coli (E. coli) is a ubiquitous symbiotic bacterium in the gut, and the diversity of E. coli genes determines the diversity of its functions. In this review, the two‐edged sword theory was innovatively proposed. For the question ‘how can we harness the ambivalent nature of E. coli to screen and treat CRC?’, in terms of CRC screening, the variations in the abundance and subtypes of E. coli across different populations present an opportunity to utilise it as a biomarker, while in terms of CRC treatment, the natural beneficial effect of E. coli on CRC may be limited, and engineered E. coli, particularly certain subtypes with probiotic potential, can indeed play a significant role in CRC treatment. It seems that the favourable role of E. coli as a genetic tool lies not in its direct impact on CRC but its potential as a research platform that can be integrated with various technologies such as nanoparticles, imaging methods, and synthetic biology modification. The relationship between gut microflora and CRC remains unclear due to the complex diversity and interaction of gut microflora. Therefore, the application of E. coli should be based on the ‘One Health’ view and take the interactions between E. coli and other microorganisms, host, and environmental factors, as well as its own changes into account. In this paper, the two‐edged sword role of E. coli in CRC is emphasised to realise the great potential of E. coli in CRC screening and treatment.

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.

Abstract 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.

BackgroundGut microbiome is a complex community of microbes present in the human gut and plays an important role in the occurrence and progression of colorectal cancer (CRC). However, the relationship between virus and CRC has not been fully understood.ObjectiveTo explore the hot spots and research trends in the field of CRC and virus.MethodsBy using the bibliometric analysis tool CiteSpace and based on the articles of the Web of Science Core Collection (WoSCC) database, the country, institution, highly cited literature, keywords and so on were visually analyzed.ResultsA total of 356 research articles on CRC from 2001 to 2023 were thoroughly analyzed. The USA and China have made the largest contribution in the field of virus and CRC. The Helmholtz Association published the most papers. There were relatively few cooperations among institutions from different countries. The results of keyword cluster analysis proved that the literature on the relationship between human cytomegalovirus (CMV) and CRC was the most widely studied aspect in this field. “Gut microbiota,” “inflammatory bowel disease,” “hepatitis b virus,” and “human papillomavirus infection” are the current research hotspots; “oncolytic virus,” “apoptosis,” and “gut microbiome” are the recent research frontiers and should be paid closer attention.ConclusionBy using CiteSpace bibliometric software, the visual analysis reflected the research trends and hot topics of virus and CRC. In addition, the prevalence and mechanism of specific virus on CRC were also reviewed, which provides valuable references for future CRC research.

Abstract Background Colorectal cancer (CRC) is a multifactorial disease with genetic and environmental factors. Regional differences in risk factors are an important reason for the different incidences of CRC in different regions. Objective The goal was to clarify the intestinal microbial composition and structure of CRC patients in different regions and construct CRC risk prediction models based on regional differences. Methods A metagenomic dataset of 601 samples from 6 countries in the GMrepo and NCBI databases was collected. All whole-genome sequencing (WGS) data were annotated for species by MetaPhlAn2. We obtained the relative abundance of species composition at the species level and genus level. The MicrobiotaProcess package was used to visualize species composition and PCA. LEfSe analysis was used to analyze the differences in the datasets in each region. Spearman correlation analysis was performed for CRC differential species. Finally, the CRC risk prediction model was constructed and verified in each regional dataset. Results The composition of the intestinal bacterial community varied in different regions. Differential intestinal bacteria of CRC in different regions are inconsistent. There was a common diversity of bacteria in all six countries, such as Peptostreptococcus stomatis and Fusobacterium nucleatum at the species level. Peptostreptococcus stomatis (species level) and Peptostreptococcus (genus level) are important CRC-related bacteria that are related to other bacteria in different regions. Region has little influence on the accuracy of the CRC risk prediction model. Peptostreptococcus stomatis is an important variable in CRC risk prediction models in all regions. Conclusion Peptostreptococcus stomatis is a common high-risk pathogen of CRC worldwide, and it is an important variable in CRC risk prediction models in all regions. However, regional differences in intestinal bacteria had no significant impact on the accuracy of the CRC risk prediction model.

Highlights 1. ORF3a was successfully transfected into SW480 cells, and its expression was localized in the cytoplasm of SW480 cells. 2. ORF3a inhibited SW480 cell proliferation, invasion and migration, induced their apoptosis, and blocked them in the G1 phase. 3. ORF3a inhibited colorectal tumor growth and accelerated cancer cell death in vivo. 4. Our study provides new insights into the use of viruses in anti-tumor regimens in 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. [ABSTRACT FROM AUTHOR]

Highlights A total of 309 CD8 T cell-related genes and 120 activated memory CD4 T cell-related genes were screened in liver cancer tumor samples. Forty-four chemical small-molecule–target interaction pairs associated with activated memory CD4 T cells and 276 pairs associated with CD8 T cells were screened. Upregulated expression of T cell-related genes including EOMES, CST7, and CD5L indicated the favorable prognosis of liver cancer.

Increasing evidence suggested that cholesterol is an important integrant of cell membranes, that plays a key role in tumor progression, immune dysregulation, and pathological changes in epigenetic mechanisms. Based on these theories, there is a growing interest on targeting cholesterol in the treatment of cancer. Here, we comprehensively reviewed the major function of cholesterol on oncogenicity, the therapeutic targets of cholesterol and its metabolites in cancer, and provide detailed insight into the essential roles of cholesterol in mediating immune and epigenetic mechanisms of the tumor microenvironment. It is also worth mentioning that the gut microbiome is an indispensable component of cancer mediation because of its role in cholesterol metabolism. Finally, we summarized recent studies on the potential targets of cholesterol and their metabolism, to provide more therapeutic interventions in oncology.

Introduction: Endoplasmic reticulum stress (ERS) is associated with the occurrence and development of colorectal cancer (CRC). Methods: One thousand nine CRC samples and 3 ERS gene sets from GEO database were used to screen and validate genes related to stage and prognosis of CRC. Twenty thousand five hundred thirty samples from the TCGA database validated the ERS genes related to prognosis. PPI network construction and coexpression analysis were used to investigate the correlation of genes. ConsensusClusterPlus analysis was used to classify CRC subtypes. Cox regression and the LASSO algorithm were used to screen ERS genes related to prognosis. HE staining, immunohistochemical staining, and RT-qPCR of 50 owner-central samples were used to verify the genes. The ERscore model was constructed based on the ERS genes related to prognosis. The nomogram model was used to verify that different subtypes of CRC patients have different prognosis. Results: Fifty ERS differentially expressed genes related to CRC stage and 8 ERS model genes related to prognosis were screened. Three subtypes of CRC were classified based on the former 50 genes. The clinical characteristics were significantly different among the subtypes. The ERscore model was constructed based on the latter 8 genes, and its accuracy was verified by clinical samples. Finally, the nomogram was constructed based on ERscore, age, and CRC stage, and the accuracy of the nomogram prediction was verified. Conclusion: ERS-related genes can be used as classification criteria for CRC, and the related clinical characteristics of different CRC subtypes are different. [ABSTRACT FROM AUTHOR]

The present study aimed to develop two nomograms in order to predict cancer-specific survival (CSS) and overall survival (OS) of patients with anal carcinoma receiving definitive chemoradiotherapy. Data from studies including patients with anal carcinoma, who were determined to be positive histologically and diagnosed between 2004 and 2010, were obtained from the Surveillance, Epidemiology, and End Results database. Significant prognostic factors for CSS and OS of patients were screened to develop nomograms through univariate and multivariate analyses. Nomograms were validated using internal and external data. The predictive abilities of the generated models were evaluated by concordance index (C-index) and calibration curves. Risk stratification was performed for patients with the same TNM stage. A total of 1,473 patients and six independent prognostic factors for CSS and OS, namely age, sex, ethnicity, marital status at diagnosis, T stage and N stage, were included in the nomogram calculations. Calibration curves demonstrated that nomogram prediction was in high accordance with actual observation. The C-indices of nomograms were greater than those of models based on the sixth edition of the American Joint Committee on Cancer TNM staging system for CSS prediction (training cohort, 0.72 vs. 0.70; validation cohort, 0.68 vs. 0.62) and OS (training cohort, 0.70 vs. 0.66; validation cohort, 0.68 vs. 0.62). Survival curves demonstrated significant survival differences among the different risk groups. Nomograms were more accurate than the conventional TNM staging system in prognosis prediction. In addition, survival performances of patients with the same TNM stage could be further distinguished by risk stratification, which provided individualized prediction for patients. These survival prediction methods may aid clinicians in patient counseling and in selecting more individualized therapeutic strategies. [ABSTRACT FROM AUTHOR]