Chronic inflammation and gut microbiota dysbiosis are risk factors for colorectal cancer. In clinical practice, patients with inflammatory bowel disease (IBD) have a greatly increased risk of developing colitis-associated colorectal cancer (CAC). However, the underlying mechanism of the initiation of CAC remains unknown. Systematic analyses using an existing genome-wide association study (GWAS) and conditional deletion of (encoding zinc finger protein 90 homolog) in a CAC mouse model indicated that is a putative oncogene in CAC development.Strikingly, depletion of the gut microbiota eliminated the tumorigenic effect of in the CAC mouse model. Moreover, fecal microbiota transplantation demonstrated that promoted CAC dependent on the gut microbiota. Analysis of 16s rDNA sequences in fecal specimens from the CAC mouse model allowed us to speculate that a -defined microbiota might mediate the oncogenic role of in the development of CAC. Mechanistic studies revealed accelerated CAC development through the TLR4-PI3K-AKT-NF-κB pathway. Our findings revealed the crucial role of the -microbiota-NF-κB axis in creating a tumor-promoting environment and suggested therapeutic targets for CAC prevention and treatment.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8115455 | PMC |
http://dx.doi.org/10.1080/19490976.2021.1917269 | DOI Listing |
J Exp Clin Cancer Res
January 2025
Department of Cardiovascular, Endocrine-Metabolic Diseases and Aging, Istituto Superiore di Sanità, Rome, Italy.
Background: Bacterial toxins are emerging as promising hallmarks of colorectal cancer (CRC) pathogenesis. In particular, Cytotoxic Necrotizing Factor 1 (CNF1) from E. coli deserves special consideration due to the significantly higher prevalence of this toxin gene in CRC patients with respect to healthy subjects, and to the numerous tumor-promoting effects that have been ascribed to the toxin in vitro.
View Article and Find Full Text PDFWorld J Surg Oncol
January 2025
Colorectal Surgery Department, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University/ Hunan Cancer Hospital, No. 283 Tongzipo Road, Yuelu District, Changsha, Hunan, 410013, China.
Objective: The clinical benefits of neoadjuvant bevacizumab plus chemotherapy in locally advanced gastric cancer patients are controversial. This study intended to evaluate the efficacy and safety of neoadjuvant bevacizumab plus chemotherapy in these patients.
Methods: In this retrospective study, 71 locally advanced gastric cancer patients receiving neoadjuvant bevacizumab plus chemotherapy or neoadjuvant chemotherapy alone were divided into bevacizumab plus chemo group (N = 23) and chemo group (N = 48).
J Transl Med
January 2025
Department of Gastrointestinal Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, China.
Background: Tumor microenvironment (TME), particularly immune cell infiltration, programmed cell death (PCD) and stress, has increasingly become a focal point in colorectal cancer (CRC) treatment. Uncovering the intricate crosstalk between these factors can enhance our understanding of CRC, guide therapeutic strategies, and improve patient prognosis.
Methods: We constructed an immune-related cell death and stress (ICDS) prognostic model utilizing machine learning methodologies.
BMC Cancer
January 2025
Department of Laboratory Medicine, Affiliated Gaozhou People's Hospital, Guangdong Medical University, Maoming, 525200, P.R. China.
Background: DNA hypomethylation and uracil misincorporation into DNA, both of which have a very important correlation with colorectal carcinogenesis. Folate plays a crucial role in DNA synthesis, acting as a coenzyme in one-carbon metabolism, which involves the synthesis of purines, pyrimidines, and methyl groups. MTHFR, a key enzyme in folate metabolism, has been widely studied in relation to neural tube defects and hypertension, but its role in colorectal cancer remains underexplored.
View Article and Find Full Text PDFBMC Med Res Methodol
January 2025
Leeds Institute of Clinical Trials Research, University of Leeds, Clarendon Way, Leeds, LS2 9NL, UK.
Background: Early detection and diagnosis of cancer are vital to improving outcomes for patients. Artificial intelligence (AI) models have shown promise in the early detection and diagnosis of cancer, but there is limited evidence on methods that fully exploit the longitudinal data stored within electronic health records (EHRs). This review aims to summarise methods currently utilised for prediction of cancer from longitudinal data and provides recommendations on how such models should be developed.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!