Comprehensive Gut Microbiome Analysis Reveals Robust Colorectal Cancer Signature Across 27 Studies
核心洞察
An international consortium reanalyzed 6,779 gut microbiome sequencing profiles from 27 studies, identifying a reproducible microbial signature associated with colorectal cancer (搜索).
The machine learning-based classifier distinguished cancer from non-cancer microbiomes across diverse cohorts, geographies, and sequencing methods, including both early-onset and late-onset CRC.
Cancer-associated microbes were detected in early-stage tumor tissue, but pre-cancerous adenomas remained difficult to identify in stool samples, highlighting a key limitation for clinical translation.
An international team of researchers from the Mi-EOCRC consortium has completed one of the most comprehensive analyses to date of the gut microbiome's relationship with colorectal cancer (搜索), publishing their findings in Cell Host & Microbe. By reanalyzing data from 27 studies comprising 6,779 publicly available gut microbiome sequencing profiles, the researchers identified a robust and reproducible microbial signature associated with the disease—one that transcends geographic boundaries, sequencing methodologies, and age of diagnosis.
"The strength of this study is its comprehensiveness. We combined stool and tissue comparisons, dietary data, taxonomic analysis down to bacterial strains, and functional analysis of virulence factors," said Georg Zeller, Visiting Team Leader at EMBL Heidelberg (搜索) and Professor at Leiden University Medical Center (搜索).
A Methodological Breakthrough in Data Integration
A central challenge in microbiome research has been the difficulty of comparing datasets generated using different sequencing methods. Many prior studies reported differences between the microbiomes of colorectal cancer (搜索) patients and healthy individuals, but small sample sizes and methodological heterogeneity limited reproducibility. Meta-analyses attempted to address these consistency questions but had been based on only a fraction of available datasets.
The research team, which included the Zeller and Zimmermann groups at EMBL Heidelberg (搜索), developed computational approaches enabling large-scale integration of disparate microbiome datasets. At the core of this advance is a machine learning algorithm trained to distinguish cancer from non-cancer microbiomes.
"The key tool is a machine learning algorithm that is trained to distinguish cancer from non-cancer microbiomes," Zeller explained. "It outputs a score of how 'cancer-like' a microbiome is. We can apply this to any existing human gut microbiome dataset, including from dietary intervention studies."
This approach allowed the researchers to establish a colorectal cancer (搜索) microbiome signature that proved broadly reproducible—not limited to a single cohort, geography, sequencing method, or age of diagnosis. The signature appeared consistent across both early-onset and late-onset colorectal cancer.
Tumor Tissue and Stool Show Concordant Microbial Signals
The study also analyzed 906 intestinal tissue samples to compare stool-based microbiome signals with microbes found directly in tumor tissue. The researchers found that microbes enriched in tumor tissue mirrored the colorectal cancer (搜索) signature observed in fecal samples.
Notably, cancer-associated microbes could already be detected in early-stage tumors within tissue samples. However, in stool samples, detection accuracy was somewhat lower for early-stage cancers and for tumors located further upstream in the colon. One possible explanation is that microbes from smaller tumors or those farther from the rectum may be more difficult to detect in stool compared to advanced tumors closer to the rectum.
"These results suggest that colorectal cancer (搜索)-associated changes in the microbiome may appear early in disease development and raise the question of how the tumor shapes the microbiome and how the microbes impact the tumor microenvironment through signalling, metabolic, and other interactions," said Michael Zimmermann, Group Leader at EMBL Heidelberg (搜索).
Pre-Cancerous Adenomas: A Persistent Challenge
Despite the robustness of the colorectal cancer (搜索) microbiome signature, pre-cancerous adenomas remain difficult to detect through stool microbiome profiling. Adenoma-associated microbial changes were weaker than those seen in colorectal cancer and showed only limited overlap with the cancer-associated signature. Machine-learning classifiers trained to detect colorectal cancer, as well as those trained specifically to distinguish adenomas from controls, demonstrated variable performance across cohorts.
"This limitation is important for future clinical translation, which the Mi-EOCRC consortium is aiming at," Zimmermann noted. "It suggests that more sensitive approaches, larger datasets, or combinations with other measurements may be needed before microbiome-based tools could contribute to the reliable detection of early pre-cancerous lesions."
In comparisons with existing non-invasive screening approaches, microbiome-based classifiers did not yet match the performance of fecal immunochemical tests. Larger studies will be needed to assess whether microbiome data could complement or be combined with current clinical tests.
Dietary Fiber Linked to Reduced Cancer-Like Microbiome Scores
The researchers explored how diet relates to the colorectal cancer (搜索) microbiome signature and found that a stronger cancer-associated microbiome pattern was linked to lower dietary fiber intake. Conversely, increasing dietary fiber intake in dietary intervention studies was associated with a reduction in the colorectal cancer microbiome signature score.
This finding supports the idea that diet, and particularly fiber consumption, can influence gut microbial communities in ways that may be relevant to cancer risk, progression, or prevention. Because the machine-learning score can be applied to existing microbiome datasets, the approach may help researchers better understand how lifestyle factors influence disease-associated microbiome patterns.
Not All Fusobacteria Are the Same
The study took a closer look at Fusobacterium, a bacterial group repeatedly linked to colorectal cancer (搜索). By analyzing hundreds of bacterial genomes, the researchers identified important differences between subspecies. Some carried different disease-related genes, including virulence factors, and some were more commonly enriched in colorectal cancer samples from particular geographic regions.
Fusobacterium nucleatum (搜索) subsp. animalis showed consistent colorectal cancer (搜索) enrichment across continents, while other Fusobacterium species and subspecies displayed more geographically heterogeneous patterns, with several almost exclusively found in cancer patients from Asia. This level of resolution underscores that bacteria grouped under the same genus can differ substantially in their biology and potential effects on human health.
Open Science as a Foundation for Future Research
The work provides a comprehensive resource for understanding the microbiome's role in colorectal cancer (搜索) and lays the groundwork for future studies into microbiome-based detection, risk assessment, and prevention strategies. By defining a reproducible colorectal cancer-associated microbial signature, the study creates a reference that could be used to train and validate future models.
However, the researchers emphasize that this is not yet a diagnostic test, but rather a step toward understanding how microbiome data could eventually support clinical research and decision-making. The study particularly highlights the power of open data and large-scale evidence synthesis in microbiome research—by combining thousands of publicly available microbiome profiles, the researchers identified robust patterns that would have been difficult to detect in individual studies alone.