ArticleCancer research and treatment2025
Relationships between the Microbiome and Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer.
Article in Cancer research and treatment, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- The role of the microbiome in uterine cancer: insights into tumorigenesis, therapeutic implications, and clinical prospects.Journal of the Egyptian National Cancer Institute · 2026Review
- Gut microbiota profile and response to neoadjuvant chemoradiotherapy in patients with rectal cancer: a systematic review.Translational gastroenterology and hepatology · 2026Review
- Distant metastasis risk and prognosis in elderly gastric cancer patients after neoadjuvant chemotherapy and surgery.Frontiers in oncology · 2026Article
- Gut-Microbiome Signatures Predicting Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer: A Systematic Review.Metabolites · 2025Review
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
purposeThis study aimed to investigate the dynamic changes in the microbiome of patients with locally advanced rectal cancer (LARC) undergoing neoadjuvant chemoradiotherapy (nCRT), focusing on the relationship between the microbiome and response to nCRT. Materials and Methods: We conducted a longitudinal study involving 103 samples from 26 patients with LARC. Samples were collected from both the tumor and normal rectal tissues before and after nCRT. Diversity, taxonomic, and network analyses were performed to compare the microbiome profiles across different tissue types, pre- and post-nCRT time-points, and nCRT responses.
resultsBetween the tumor and normal tissue samples, no differences in microbial diversity and composition were observed. However, when pre- and post-nCRT samples were compared, there was a significant decrease in diversity, along with notable changes in composition. Non-responders exhibited more extensive changes in their microbiome composition during nCRT, characterized by an increase in pathogenic microbes. Meanwhile, responders had relatively stable microbiome communities with more enriched butyrate-producing bacteria. Network analysis revealed distinct patterns of microbial interactions between responders and non-responders, where butyrate-producing bacteria formed strong networks in responders, while opportunistic pathogens formed strong networks in non-responders. A Bayesian network model for predicting the nCRT response was established, with butyrate-producing bacteria playing a major predictive role.
conclusionOur study demonstrated a significant association between the microbiome and nCRT response in LARC patients, leading to the development of a microbiome-based response-prediction model. These findings suggest potential applications of microbiome signatures for predicting and optimizing nCRT treatment in LARC patients.
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Registered trials
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