Gut Microbiota Disruption in Hematologic Cancer Therapy: Molecular Insights and Implications for Treatment Efficacy

International Journal of Molecular Sciences, Journal Year: 2024, Volume and Issue: 25(19), P. 10255 - 10255

Published: Sept. 24, 2024

Hematologic malignancies (HMs), including leukemia, lymphoma, and multiple myeloma, involve the uncontrolled proliferation of abnormal blood cells, posing significant clinical challenges due to their heterogeneity varied treatment responses. Despite recent advancements in therapies that have improved survival rates, particularly chronic lymphocytic leukemia acute lymphoblastic treatments like chemotherapy stem cell transplantation often disrupt gut microbiota, which can negatively impact outcomes increase infection risks. This review explores complex, bidirectional interactions between microbiota cancer patients with HMs. Gut influence drug metabolism through mechanisms such as production enzymes bacterial β-glucuronidases, alter efficacy toxicity. Moreover, microbial metabolites short-chain fatty acids modulate host immune response, enhancing effectiveness. However, therapy reduces diversity beneficial bacteria, Bifidobacterium Faecalibacterium, while increasing pathogenic bacteria Enterococcus Escherichia coli. These findings highlight critical need preserve during treatment. Future research should focus on personalized microbiome-based therapies, probiotics, prebiotics, fecal transplantation, improve quality life for hematologic malignancies.

Language: Английский

A two-step, two-sample Mendelian randomization analysis investigating the interplay between gut microbiota, immune cells, and melanoma skin cancer

Medicine, Journal Year: 2024, Volume and Issue: 103(45), P. e40432 - e40432

Published: Nov. 8, 2024

This study aims to rigorously explore the potential causal relationships among gut microbiota (GM), immune cells, and melanoma skin cancer participants from Europe, where this disease exhibits significant prevalence profound societal impact. Using genome-wide association analysis database, a double-sample Mendelian randomization (MR) was drawn upon investigate GM, cancer. The inverse variance weighted approach applied estimate connections these variables. A two-step MR employed quantitatively gauge impact of cells mediated GM on To address sources bias, such as pleiotropy heterogeneity, multiple analytical techniques were integrated. pinpointed 6 taxa related either an augmented or declined risk late-stage In same vein, 32 cell phenotypes noticed correlates with modified Our also implies that probable between could be facilitated by 5 phenotypes. findings our underline certain influencers onset development Importantly, results spotlight agents mediating association.

Language: Английский