ArticleBMC microbiology2025
Exploring the gut microbiota associated with peripheral nerve invasion in colorectal cancer patients and constructing predictive models.
Article in BMC microbiology, 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
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Integrative transcriptomic profiling of tumor patients in surgical ICU: identifying prognostic immune signatures.Frontiers in molecular biosciences · 2026Article
- The gut microbiota in colorectal cancer: role in cytokine regulation, intestinal immune barrier dysfunction.Frontiers in oncology · 2026Review
- Immunological, Inflammatory, and Microbiota Determinants of Carpal Tunnel Syndrome: Evidence from Mendelian Randomization.Endocrine, metabolic & immune disorders drug targets · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
18 authors.
Funding
Abstract
objectiveThis study aims to explore the differences in composition, abundance, and biological functions of the gut microbiota between colorectal cancer (CRC) patients with peripheral nerve invasion (PNI) and those without peripheral nerve invasion (NPNI). Additionally, we tried to construct a machine-learning predictive model incorporating the identified microbiota characteristics to explore the impact of gut microbiota on CRC-PNI progression and to search for new non-invasive microbiological indicators for CRC-PNI. Finally, we successfully developed a predictive model to predict PNI in CRC patients through leveraging microbial biomarkers. This innovative approach is expected to offer a novel strategy for the early detection of CRC metastasis, thereby facilitating more informed decisions regarding treatment options.
methodThis study included 132 colorectal cancer (CRC) patients, who were divided into two separate groups according to whether they exhibited PNI. The gut microbiota of these participants were subjected to 16S rRNA gene sequencing, followed by a thorough analysis to identify any significant differences between the groups. We applied a cell sorting algorithm to convert the transcriptome sequencing data obtained from 8 colorectal cancer patients into a matrix representing immune cell abundance. Following this, the matrix was utilized to investigate the associations among the PNI-related distinct gut microbiota, immune cells, and immune-related genes, and PNI-related differentially expressed genes (or molecular markers, pathways), as well as their associations with KEGG pathways. Based on the differential gut microbiota, we constructed Random Forest (RF) and Multilayer Perceptron (MLP) models to predict PNI in CRC patients.
resultComparative analysis of α-diversity and β-diversity in the gut microbiota of CRC patients with and without PNI revealed no statistically significant differences (P > 0.05). However, Linear Discriminant Analysis effect size (LEfSe) identified 35 distinct gut microbiota, with 28 species enriched in the PNI group and 7 species significantly enriched in the NPNI group. By analyzing the gut microbiota significantly associated with PNI, we successfully constructed predictive models using RF and MLP that can predict the occurrence of PNI in CRC patients. Both models have demonstrated robust performance.
conclusionsIn the PNI and NPNI groups, 35 gut microbiota species exhibited significant variations in abundance. The differential intestinal microbiota associated with PNI in colorectal cancer may modulate the neuroinvasion process via a variety of potential biological mechanisms. The RF and MLP predictive models show considerable accuracy in predicting CRC-PNI status and are of reference value.
Indexed as
Identifiers
What OpenQuestion holds
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.