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Gut fungal signatures in colorectal cancer and their potential for supporting diagnosis: a multi-cohort metagenomic analysis
by
Guan, Tianyu
, Guo, Hongxing
, Wang, Leyi
, Lu, Tong
, Liu, Hengming
, Liu, Jin
, Lei, Zhixin
, Yu, Xiaofei
, Li, Xin
, Zhao, Deyu
, Zhang, Zhihang
, Zhang, Yue
, Li, Linlin
, Yan, Qiulong
, Dong, Bo
, Li, Shenghui
, Kang, Jian
in
Analysis
/ Biomarkers
/ Biomedical and Life Sciences
/ Biomedicine
/ Cohort Studies
/ Colorectal cancer
/ Colorectal Neoplasms - diagnosis
/ Colorectal Neoplasms - microbiology
/ Community structure
/ Datasets
/ Diagnosis
/ Disease
/ Dysbacteriosis
/ Feces
/ Fungi
/ Fungi - genetics
/ Gastrointestinal Microbiome - genetics
/ Genetic aspects
/ Genomes
/ Gut microbiota
/ Health aspects
/ Humans
/ Machine learning
/ Medical research
/ Medicine, Experimental
/ Medicine/Public Health
/ Metagenomics
/ Microbiomes
/ Microbiota (Symbiotic organisms)
/ Pathogenesis
/ Physiological aspects
/ Prediction models
/ Taxonomy
/ Translational Metagenomics
/ Whole-metagenome shotgun sequencing
2026
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Gut fungal signatures in colorectal cancer and their potential for supporting diagnosis: a multi-cohort metagenomic analysis
by
Guan, Tianyu
, Guo, Hongxing
, Wang, Leyi
, Lu, Tong
, Liu, Hengming
, Liu, Jin
, Lei, Zhixin
, Yu, Xiaofei
, Li, Xin
, Zhao, Deyu
, Zhang, Zhihang
, Zhang, Yue
, Li, Linlin
, Yan, Qiulong
, Dong, Bo
, Li, Shenghui
, Kang, Jian
in
Analysis
/ Biomarkers
/ Biomedical and Life Sciences
/ Biomedicine
/ Cohort Studies
/ Colorectal cancer
/ Colorectal Neoplasms - diagnosis
/ Colorectal Neoplasms - microbiology
/ Community structure
/ Datasets
/ Diagnosis
/ Disease
/ Dysbacteriosis
/ Feces
/ Fungi
/ Fungi - genetics
/ Gastrointestinal Microbiome - genetics
/ Genetic aspects
/ Genomes
/ Gut microbiota
/ Health aspects
/ Humans
/ Machine learning
/ Medical research
/ Medicine, Experimental
/ Medicine/Public Health
/ Metagenomics
/ Microbiomes
/ Microbiota (Symbiotic organisms)
/ Pathogenesis
/ Physiological aspects
/ Prediction models
/ Taxonomy
/ Translational Metagenomics
/ Whole-metagenome shotgun sequencing
2026
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Gut fungal signatures in colorectal cancer and their potential for supporting diagnosis: a multi-cohort metagenomic analysis
by
Guan, Tianyu
, Guo, Hongxing
, Wang, Leyi
, Lu, Tong
, Liu, Hengming
, Liu, Jin
, Lei, Zhixin
, Yu, Xiaofei
, Li, Xin
, Zhao, Deyu
, Zhang, Zhihang
, Zhang, Yue
, Li, Linlin
, Yan, Qiulong
, Dong, Bo
, Li, Shenghui
, Kang, Jian
in
Analysis
/ Biomarkers
/ Biomedical and Life Sciences
/ Biomedicine
/ Cohort Studies
/ Colorectal cancer
/ Colorectal Neoplasms - diagnosis
/ Colorectal Neoplasms - microbiology
/ Community structure
/ Datasets
/ Diagnosis
/ Disease
/ Dysbacteriosis
/ Feces
/ Fungi
/ Fungi - genetics
/ Gastrointestinal Microbiome - genetics
/ Genetic aspects
/ Genomes
/ Gut microbiota
/ Health aspects
/ Humans
/ Machine learning
/ Medical research
/ Medicine, Experimental
/ Medicine/Public Health
/ Metagenomics
/ Microbiomes
/ Microbiota (Symbiotic organisms)
/ Pathogenesis
/ Physiological aspects
/ Prediction models
/ Taxonomy
/ Translational Metagenomics
/ Whole-metagenome shotgun sequencing
2026
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Gut fungal signatures in colorectal cancer and their potential for supporting diagnosis: a multi-cohort metagenomic analysis
Journal Article
Gut fungal signatures in colorectal cancer and their potential for supporting diagnosis: a multi-cohort metagenomic analysis
2026
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Overview
Background
Colorectal cancer (CRC) is influenced by host factors and environmental exposures that shape gut microbial ecosystems. Although bacterial and viral alterations in CRC have been widely investigated, the role of gut fungi remains underexplored, partly because of their low biomass and the limited availability of well-curated fungal reference genomes.
Methods
We conducted a large-scale metagenomic analysis across 9 publicly available cohorts comprising 1,433 fecal samples to characterize CRC-associated fungal alterations and fungal-bacterial co-abundance patterns. The predictive value of microbial signatures was assessed using LASSO and random forest models, with external validation performed in 6 independent cohorts comprising 272 samples.
Results
Multi-cohort analysis revealed CRC-associated alterations in gut fungal community structure and selected diversity measures. Differential abundance analysis identified 15 fungal species with recurrent changes across cohorts. Among them,
Saccharomyces cerevisiae
c86 and
Trichophyton rubrum
c61 showed predominant enrichment in healthy controls, whereas Barnettozyma c122 and Pseudopithomyces c302 showed predominant enrichment in CRC. Fungal-only models exhibited limited standalone predictive capacity. However, integrating fungal features with bacterial biomarkers modestly improved CRC prediction performance compared with bacterial-only models. In external validation, the random forest-based fungal-bacterial model increased the mean AUC from 0.722 to 0.762, with improved AUCs in 5 of the 6 validation cohorts.
Conclusions
This study suggests that CRC is associated with gut fungal dysbiosis and supports the exploratory value of gut fungal signatures as adjunctive features in microbiome-based CRC prediction models. These findings highlight the importance of incorporating fungal communities into CRC microbiome research while emphasizing the need for prospective and mechanistic validation.
Publisher
BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
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