ArticleGut microbes
A powerful machine learning approach to identify interactions of differentially abundant gut microbial subsets in patients with metastatic and non-metastatic pancreatic cancer.
Article in Gut microbes. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Microbiome-based diagnostic biomarkers in pancreatic ductal adenocarcinoma: Current evidence and translational challenges (Review).Oncology letters · 2026Review
- Intratumoral Microorganisms in Tumors: Current Understanding and Emerging Therapeutic Strategies.MedComm · 2026Review
- Gut microbiota and pancreatic cancer: tumorigenesis, progression, and clinical applications.Cancer biology & medicine · 2026Review
- The microbiome of host saliva, gastric fluid, and gastric mucosa as accurate diagnostic tools for gastric cancer detection.Journal of translational medicine · 2026Article
- Unveiling the gut-pancreas axis: microbial influence on stemness and tumor microenvironment of PDAC.Stem cells (Dayton, Ohio) · 2026Review
- Ferroptosis in cancer toward molecular insights and clinical translation in pancreatic cancer.Molecular cancer · 2026Review
- Metagenomic next-generation sequencing: new horizons in microbiology.Frontiers in cellular and infection microbiology · 2026Review
- Distinct effects of a dairy probiotic drink and physical exercise on gut microbiota in pediatric acute lymphoblastic leukemia patients in remission: a randomized clinical trial.Frontiers in microbiology · 2026Article
- Untargeted Metabolomics Reveals Metabolic Reprogramming Linked to HCC Risk in Late Diagnosed Tyrosinemia Type 1.Metabolites · 2025Article
- Multifaceted effects of the microbiome in pancreatic cancer: from association to modulation.Nature reviews. Gastroenterology & hepatology · 2025Review
- Construction of a gene-metabolite-microbiome regulatory network reveals novel therapeutic targets in bladder cancer through multi-omics analysis.Annals of medicine · 2025Article
- Revolutionizing gastroenterology and hepatology with artificial intelligence: From precision diagnosis to equitable healthcare through interdisciplinary practice.World journal of gastroenterology · 2025Review
- A Novel Prognostic Signature of Mitophagy-Related E3 Ubiquitin Ligases in Breast Cancer.International journal of molecular sciences · 2025Article
- Emerging technologies and current challenges in intratumoral microbiota research.Frontiers in cellular and infection microbiology · 2025Review
- Integrative metagenomic and metabolomic profiling identifies gut microbial and metabolite signatures associated with lymph node metastasis in pancreatic cancer.Frontiers in microbiology · 2025Article
- 3-O-Acetyl-11-Keto-Oncology research · 2025Article
- Polyphenol-mediated microbiome modulation in STEMI patients: a pilot study.Frontiers in medicine · 2025Article
- Gut-Microbiome Interactions: Characterization, Therapeutic Implications and Machine Learning.Sage open pathologyReview
Corrections and comments
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Authors and funding
16 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Pancreatic cancer has a dismal prognosis, as it is often diagnosed at stage IV of the disease and is characterized by metastatic spread. Gut microbiota and its metabolites have been suggested to influence the metastatic spread by modulating the host immune system or by promoting angiogenesis. To date, the gut microbial profiles of metastatic and non-metastatic patients need to be explored. Taking advantage of the 16S metagenomic sequencing and the PEnalized LOgistic Regression Analysis (PELORA) we identified clusters of bacteria with differential abundances between metastatic and non-metastatic patients. An overall increase in Gram-negative bacteria in metastatic patients compared to non-metastatic ones was identified using this method. Furthermore, to gain more insight into how gut microbes can predict metastases, a machine learning approach (iterative Random Forest) was performed. Iterative Random Forest analysis revealed which microorganisms were characterized by a different level of relative abundance between metastatic and non-metastatic patients and established a functional relationship between the relative abundance and the probability of having metastases. At the species level, the following bacteria were found to have the highest discriminatory power:
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