ArticleHeliyon2024
Identifying important microbial biomarkers for the diagnosis of colon cancer using a random forest approach.
Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Bioinformatics reveals the prognostic potential of manganese metabolism-related genes in lung adenocarcinoma.Functional & integrative genomics · 2026Article
- The prognostic significance of epoxide hydrolases in colorectal cancer.Biochemistry and biophysics reports · 2025Article
- Mechanism of action and experimental validation of key genes common to diabetic retinopathy and coronary heart disease based on multiple bioinformatics investigations.Frontiers in genetics · 2025Article
- Bioinformatics-driven exploration of key genes and mechanisms underlying oxidative stress in traumatic brain injury.Frontiers in aging neuroscience · 2025Article
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Authors and funding
8 authors.
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Abstract
Colon cancer is one of the most common cancers, with 30-50 % of patients returning or metastasizing within 5 years of treatment. Increasingly, researchers have highlighted the influence of microbes on cancer malignant activity, while no studies have explored the relationship between colon cancer and the microbes in tumors. Here, we used tissue and blood samples from 67 colon cancer patients to identify pathogenic microorganisms associated with the diagnosis and prediction of colon cancer and evaluate the predictive performance of each pathogenic marker and its combination based on the next-generation sequencing data by using random forest algorithms. The results showed that we constructed a database of 13,187 pathogenic microorganisms associated with human disease and identified 2 pathogenic microorganisms (
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