ArticlemSystems2025
Identification and predictive machine learning models construction of gut microbiota associated with lymph node metastasis in colorectal cancer.
Article in mSystems, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed.
- Gut microbiota signatures and machine learning-based candidate feature prioritization in advanced colorectal cancer.Archives of microbiology · 2026Article
- Microbiome-Shaped Metastatic Niches in Colorectal Cancer: Organ-Specific Patterns, Immune-Metabolic Mechanisms, and Therapeutic Translation.Microorganisms · 2026Review
- Preoperative gut microbiota combined with machine learning for predicting post-hepatectomy liver failure in HBV-related hepatocellular carcinoma: a pilot study.Frontiers in cellular and infection microbiology · 2026Article
Corrections and comments
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Authors and funding
15 authors.
Funding
Abstract
This study focuses on the significant role between gut microbiota and lymph node metastasis (LNM) in colorectal cancer (CRC). By conducting 16S rRNA sequencing on fecal samples from 147 CRC patients and combining it with the linear discriminant analysis effect size algorithm, we successfully identified significant differences in the gut microbiota between patients with LNM and those with no lymph node metastasis (NLNM). Furthermore, using transcriptome data from 23 CRC patients, we constructed an immune cell infiltration matrix to deeply explore the biological functions associated with LNM. Eventually, using the characteristics of the gut microbiota associated with LNM, we developed random forest (RF) and multilayer perceptron (MLP) machine learning models to predict the LNM status of CRC patients. We identified 21 differentially abundant gut microbes between the two groups, among which
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.