Microbiota-host interaction in colorectal cancer: emerging computational technology, multi-omics integration, and mechanisms

Microbiome and host genetic mutation

The gut microbiota contributes to CRC initiation and clonal evolution by directly inducing genomic instability and modulating host oncogenic pathways. Whole-genome sequencing (WGS) and whole-exome sequencing (WES) of CRC tumors have identified distinct mutational signatures that are mechanistically attributed to bacterial genotoxins³⁶. The pks⁺ island from Escherichia coli encodes a series of enzymes responsible for synthesizing colibactin, a potent genotoxin capable of inducing DNA damage in epithelial cells³⁷. This damage manifests as interstrand crosslinks and double-strand breaks, ultimately leading to a characteristic mutational signature (i.e., the “colibactin damage motif”) that is characterized by preferentially damaging sequences (AAWWTT, where W represents A or T) that lead to genomic instability, which is a hallmark of cancer progression¹⁹. Colibactin-associated mutations have been identified in > 12% of CRC cases³⁸, underscoring the substantial contribution to disease burden. Similar genotoxic effects have been observed with other bacterial toxins, including Bacteroides fragilis toxin (BFT), which is produced by enterotoxigenic Bacteroides fragilis (ETBF), and promotes DNA damage through spermine oxidase-dependent generation of reactive oxygen species (ROS), further establishing direct molecular links between specific gut bacteria and somatic mutations in CRC^39,40.

In addition to inducing de novo mutations, the microbiome can reshape the function of key tumor suppressor genes. TP53, the most frequently mutated gene in human cancers, is altered in approximately 43% of CRC cases⁴¹. Mutant p53 typically promotes tumor development, enhances cancer cell survival, and is associated with treatment resistance and poor prognosis. However, when mutant p53 is exposed to gallic acid, a polyphenol metabolite produced by gut commensal microbes, mutant p53 loses the tumor-suppressive property and switches to perform oncogenic functions by over-activating the Wnt signaling pathway, significantly enhancing tumor cell proliferation and invasion²⁰. In addition, some microbes can promote p53 ubiquitination and degradation, further attenuating the tumor-suppressive function⁴².

Host genetics reciprocally influence microbial colonization, establishing a bidirectional feedback loop that reinforces tumor-promoting interactions. For example, KRAS mutations promote intratumoral colonization of ETBF in CRC by regulating the miRNA3655/SURF6/IRF7/IFNβ axis²¹, whereas F. nucleatum is preferentially enriched in KRAS p.G12D mutant CRC tumor tissues and contributes to tumorigenesis via adhesion to the RNA helicase, DHX15²². Similarly, BRAF V600E mutations render intestinal stem cells susceptible to colonization with toxigenic bacteria⁴³, highlighting how common oncogenic drivers can create a niche for specific tumor-promoting microbes.

The search for microbiome-genome interactions is increasingly powered by microbiome-wide association studies (mGWAS), which correlate host genetic variation with microbial abundance⁴⁴. The single nucleotide polymorphism (SNP), rs2355016, which is located in the intron of ATP-sensitive inward rectifier potassium channel 11 (KCNJ11), was shown to be significantly associated with the abundance of F. nucleatum in a cohort of 748 Chinese CRC patients. Decreased KCNJ11 expression enhances bacterial adhesion through galactose-N-acetyl-d-galactosamine (Gal-GalNAc) binding and promotes CRC growth⁴⁵. A two-sample Mendelian randomization (MR) study, which tests for potential causal relationships between gut microbiota and health outcomes by comparing summary statistics from a separate microbiome and outcome genome-wide association study (GWAS), has also identified a causal relationship between six bacterial taxa and CRC at a locus-wide significance level⁴⁶. Additional studies indicated that early-life microbial exposures interact with host polymorphisms (e.g., DUOX2 variants) that collectively modulate CRC susceptibility through microbiota-mediated pathways²³.

In summary, the microbiota contributes to CRC-associated genetic instability through both direct genotoxic activity and functional interference with canonical tumor suppressors, while host genetic alterations also shape microbial colonization patterns. This bidirectional interplay solidifies the role of the microbiome as an active contributor to the mutational and ecological landscape of CRC.

Microbiome and host transcriptomic alteration

The gut microbiota exerts significant influence on host gene expression programs during CRC development, reshaping signaling pathways, immune responses, and cellular phenotypes⁴⁷. Researchers have used a wide range of techniques, including bulk RNA sequencing (RNA-seq), single-cell RNA sequencing (scRNA-seq), and spatial transcriptomics, to profile host gene expression, and jointly analyze these data with microbiome features to uncover key insights into host-microbiota interactions. Early bulk RNA-seq of CRC tumor tissues has been instrumental in revealing broad associations between specific microbial presence or dysbiosis and distinct host gene expression programs, identifying links between pathogenic mucosal bacteria and the expression of host genes involved in inflammation, proliferation, and epithelial barrier dysfunction⁴⁸. For example, colonization with F. nucleatum activates TNFSF9, a co-stimulatory molecule that modulates tumor-associated immune responses²⁴.

More recently, advances in single-cell technologies have significantly transformed our understanding of host-microbe interactions by enabling cell type-specific resolution of these complex relationships. Spatial host-microbiome sequencing, a novel technique that combines spatial transcriptomics and 16S rRNA gene amplicon sequencing, allows simultaneous profiling of host gene expression and microbial composition in tissues with spatial resolution. Distinct spatial niches were identified using this approach in the mouse gut. Bacterial genera, such as Pseudobutyrivibrio and Oscillibacter, were shown to influence the expression of host genes (e.g., Muc2 and Ceacam20), which are involved in mechanisms critical for maintaining gut barrier integrity and immune signaling⁴⁹. The development of invasion-adhesion-directed expression sequencing (INVADEseq) represents a major technological leap, enabling precise identification of bacterial invasion events at the single-cell resolution²⁵. F. nucleatum-infected epithelial cells exhibit robust induction of inflammatory genes (e.g., CXCL1) and matrix-remodeling factors (e.g., MMP9) in CRC tissues, implicating the role of bacteria in driving metastatic potential. Concurrently, distinct immune cell populations, such as tumor-associated macrophages and T cells, exhibit profoundly altered expression profiles in pathways related to DNA repair and cellular dormancy upon bacterial exposure. These findings suggest sophisticated, cell-type-specific mechanisms of bacterial modulation that are entirely obscured in bulk tissue analyses.

In addition to protein-coding mRNAs, the microbiota also exerts influence through the regulation of non-coding (nc)RNAs. For example, F. nucleatum upregulates the long non-coding (lnc)RNA, ENO1-IT1, which in turn promotes chemoresistance by activating autophagic pathways that allow cancer cells to survive therapeutic stress^26,27. In contrast, ETBF downregulates the tumor-suppressive micro (mi)RNA, miR-149-3p, enhancing Th17 cell differentiation and creating a pro-tumorigenic environment. Moreover, microbial suppression of the lncRNA, Snhg9, disrupts the role in stabilizing p53 via the SIRT1–CCAR2 complex, illustrating how microbes exploit ncRNA circuits to reinforce oncogenic signaling⁵⁰.

In summary, these findings revealed that the gut microbiota shapes CRC progression through cell-type-specific transcriptional reprogramming, direct modulation of cancer-relevant pathways, and sophisticated regulation of ncRNA networks. This comprehensive rewiring of the host transcriptome represents a fundamental mechanism through which microbial communities influence tumor behavior and therapeutic responses.

Microbiome and host epigenomic modulation

Epigenetics refers to the study of heritable changes in gene expression that occur without alterations to the underlying DNA sequence. The primary epigenetic mechanisms include DNA methylation, various histone modifications (e.g., acetylation and methylation), and regulatory ncRNAs, which collectively mediate the interplay between genetic predisposition and environmental factors. The gut microbiota, as a dynamic and abundant environmental factor resident within the host, is a potent modulator of this epigenetic landscape^51,52. Advances in technologies, such as chromatin immunoprecipitation sequencing (ChIP-seq) for histone modifications, whole-genome bisulfite sequencing for DNA methylation, and assay for transposase-accessible chromatin with high-throughput sequencing (ATAC-seq) for chromatin accessibility, have begun to illuminate how microbial signals directly reshape the host epigenome in CRC.

A growing body of evidence delineates a direct causal link between CRC-associated dysbiosis and aberrant epigenetic patterning. Commensal bacteria have been shown to induce TET2/3-dependent DNA demethylation in intestinal epithelial cells, thereby reprogramming transcriptional responses associated with colitis and CRC²⁸. FMT from CRC patients-to-mice induces hypermethylation of the promoter regions of key tumor suppressor genes, such as SFRP1, PENK, and WIF1⁵³. Several pathogens, such as F. nucleatum, Hungatella hathewayi, and Streptococcus species, which are consistently enriched in CRC tumors, have been reported to promote hypermethylation of tumor suppressor gene promoters by upregulating the expression of DNA methyltransferases (DNMTs)²⁹. Moreover, a population-based study revealed that CRC tissues infected with a high abundance of F. nucleatum are significantly associated with the CpG island methylator phenotype (CIMP)-high subtype and correlate with a poorer patient prognosis⁵⁴. Supporting these findings, integrated multi-omics analyses combining 16S/metagenomic sequencing with whole-genome bisulfite sequencing have consistently identified significant methylome differences between CRC tumors and adjacent normal tissues, pinpointing specific microbial-associated CpG methylation patterns in gene promoter regions³⁰.

The microbial influence extends beyond DNA methylation to the rapidly growing field of RNA epigenetics, in which bacteria and bacteria-derived metabolites can modulate mRNA N⁶-methyladenosine (m6A) modification. For example, F. nucleatum has been shown to suppress the m6A “writer” enzyme, METTL3, via the YAP signaling pathway, leading to reduced m6A modification and subsequent stabilization of pro-metastatic genes, like Kif26b³¹. In contrast, butyrate, a metabolite derived from commensal bacteria, such as Clostridium, functions not only as a potent endogenous histone deacetylase inhibitor (HDACi) but also as a key regulator of epi-transcriptomic modifications. Butyrate inhibits CRC development by downregulating METTL3 expression and downstream targets, such as cyclin E1, thereby attenuating processes, like epithelial-mesenchymal transition (EMT)^55,56.

Therefore, the gut microbiota could serve as a master regulator of the host epi-transcriptome, influencing CRC progression through both classical epigenetic mechanisms (DNA methylation) and emerging epi-transcriptomic pathways (m6A modification). While these insights reveal promising therapeutic targets for epigenetic therapy, this field remains nascent and warrants further investigation to fully exploit the potential of targeting the microbiota-epigenome axis for CRC intervention.

Microbiome and host metabolomic crosstalk

Metabolomic crosstalk represents one of the most direct and dynamic interfaces of the microbiota-host interaction in CRC. The gut microbiota functions as a vast, diverse, and highly adaptable bioreactor, transforming dietary and host-derived compounds into a wide array of bioactive metabolites that directly influence host cellular processes. Mass spectrometry-based metabolomics has enabled systematic profiling of these interactions, enabling comprehensive profiling of thousands of small molecules in biological samples, such as serum and stool. Integrated multi-omics approaches have linked specific metabolite profiles to CRC-associated microbiome signatures, identifying both enriched metabolites, such as leucylalanine, serotonin, and imidazole propionate, and depleted species, including perfluorooctane sulfonate and sphingadienine, in CRC patients³².

Maladaptation in host-microbiota metabolic crosstalk has a critical role in colorectal carcinogenesis. An arginine succinyltransferase (AST)-deficient strain of E. coli, Nissle 1917 (ΔacEcN), inhibits intestinal arginine catabolism, leading to arginine accumulation that promotes M2 macrophage polarization and activates Wnt/β-catenin signaling, ultimately accelerating tumor growth³³. Similarly, multi-omics analyses involving single-cell transcriptomics, microbiome profiling, metabolomics, and clinical data have revealed significant activation of the host urea cycle as a hallmark of colorectal tumorigenesis. This metabolic shift is accompanied by a loss of ureolytic beneficial bacteria, such as Bifidobacterium, and an expansion of non-ureolytic pathobionts, collectively disrupting intestinal nitrogen homeostasis and local immune responses³⁴.

The gut microbiome also profoundly modulates lipid metabolism in CRC. For example, F. nucleatum infection reduces lipid accumulation in CRC stem-like cells (CCSCs) by enhancing fatty acid oxidation, thus promoting CCSC self-renewal. F. nucleatum infection induces fatty acid formation and promotes lipid accumulation in non-CCSCs, which in turn drives cancer stemness via activation of the Notch/Numb signaling pathway³⁵. Similarly, Peptostreptococcus anaerobius interacts with TLR2 and TLR4 on colon epithelial cells to increase intracellular levels of reactive oxidative species, which promote cholesterol synthesis and cell proliferation via SREBP2 and PI3K-Akt-mTOR signaling⁵⁷. Bile acid metabolism constitutes another major node of microbiota-driven metabolic reprogramming. Genes involved in secondary bile acid synthesis are strongly associated with CRC progression⁵⁸. A landmark shotgun metagenomics-metabolomics study demonstrated that the levels of the carcinogenic secondary bile acid, DCA, are markedly elevated in patients with advanced adenomas and Bilophila wadsworthia is the only species consistently correlated with DCA abundance⁵⁹.

In summary, the metabolome provides a functional readout of the microbiome biochemical activity within the TME. Microbial metabolites act as signaling molecules, immune modulators, and metabolic substrates that collectively shape tumorigenesis. Deciphering this complex crosstalk is essential for developing metabolism-focused interventions and biomarker strategies in CRC.

Dimensionality reduction methods

Microbiome and transcriptome datasets are typically high-dimensional, characterized by numerous features [e.g., operational taxonomic units (OTUs) and genes] relative to sample size. Dimensionality reduction techniques provide an essential first step for exploratory data analysis, providing a high-level overview of sample similarities and underlying patterns⁶⁰. Principal component analysis (PCA) and an extension for distance matrices, principal coordinates analysis (PCoA), are fundamental linear methods that transform the original high-dimensional data into a set of orthogonal axes capturing major sources of variation⁶¹. These techniques are widely used to visualize the overall structure of microbiome data (e.g., based on beta-diversity metrics, like Bray-Curtis or UniFrac distances) and host omics data (e.g., gene expression profiles from RNA-seq), enabling researchers to assess sample relationships, detect potential batch effects, and identify the main sources of biological variation. For more complex, non-linear structures that linear methods fail to capture, non-linear dimensionality reduction techniques, such as t-distributed stochastic neighbor embedding (t-SNE) and uniform manifold approximation and projection (UMAP), have gained prominence^62,63. These methods are particularly powerful for revealing subtle sample clusters that may correspond to biologically or clinically distinct patient subtypes, including those defined by specific microbiome-host interaction states.

Clustering methods

Clustering methods are essential for deciphering complex microbiome data structures by grouping samples or microbial features based on similarity measures⁶⁴. Hierarchical clustering, one of the most established approaches, organizes microbiota samples into dendrograms through pairwise distance metrics, such as Bray-Curtis or Jensen-Shannon divergence. This method enables multi-resolution visualization of sample relationships and is particularly valuable for identifying broad ecological patterns in microbial communities⁶⁵. K-means clustering offers an alternative partitioning approach that groups samples into a predefined number of clusters by minimizing within-cluster variance, providing efficient processing of large datasets despite requiring prior specification of cluster numbers⁶⁶. Partitioning around medoids (PAM) clustering with Jensen-Shannon distance is widely adopted for enterotype analysis due to robustness to outliers, effectively identifying stable states of the gut ecosystem across populations⁶⁷. Recent advances have incorporated machine learning to enhance clustering performance.

The integration of microbial data with host transcriptomic classifications has yielded significant insights into CRC heterogeneity. A landmark study by Guinney et al. established the consensus molecular subtypes (CMS) classification, which categorizes CRC tumors into four distinct subgroups based on gene expression patterns: CMS1 (immune invasive, 14%); CMS2 (canonical, 37%); CMS3 (metabolic dysregulation, 13%); and CMS4 (mesenchymal, 23%)⁶⁸. Subsequent research has revealed critical interactions between these molecular subtypes and specific microbial signatures. For example, F. nucleatum enrichment has been identified as a prognostic factor for patients within the CMS4 subtype with studies showing that mesenchymal tumors (CMS4) exhibiting high levels of Fusobacteriales are associated with an approximately two-fold higher risk of poor clinical outcomes⁶⁹. Building on this finding, more recent frameworks have integrated microbial community data directly into stratification models, leading to the proposal of onco-microbial community subtypes (OCSs). This classification system stratifies CRCs into three distinct subgroups based on the microbial composition, each demonstrating unique clinic-molecular features and patient outcomes, thereby highlighting the power of integrated omics approaches for refining cancer taxonomy⁷⁰. Furthermore, multi-omics profiling of fecal samples has identified five enterotypes associated with immunotherapy responses in cancer patients, including patients with CRC⁷¹. These approaches not only reveal microbial community structures but also enable the development of novel classification systems that combine microbial and molecular features, offering new perspectives for personalized cancer diagnostics and therapeutics.

Correlation-based methods

Correlation-based analyses represent a fundamental approach for identifying specific pairwise associations between microbial features (e.g., species abundance) and host molecular features (e.g., gene expression and metabolite concentration). Standard non-parametric tests, like Spearman’s rank correlation, are widely used due to the robustness of non-normal distributions, which are typical of microbial data. However, a critical limitation arises from the compositional nature of microbiome data, in which the relative abundance of each taxon is intrinsically dependent on other taxa, often leading to spurious correlations that reflect data structure rather than true biological relationships. To address these limitations, more sophisticated multivariate approaches have been developed. Multivariate statistical methods, including partial least squares regression (PLS), orthogonal partial least squares, and non-metric multidimensional scaling (NMDS), enable the identification of key features contributing to associations across multiple omics datasets. Canonical correlation analysis (CCA) and the sparse variant (sCCA) enable the identification of complex associations between groups of microbial and host features. Priya et al. demonstrated the power of this approach by applying sCCA to colonic mucosal samples from CRC patients, revealing coordinated microbial-host gene networks with both shared and disease-specific interaction patterns⁷². Other advanced methodologies, including weighted correlation network analysis (WGCNA), facilitate the construction of co-expression networks that capture complex interaction patterns, while Procrustes analysis coupled with Mantel testing provides a framework for assessing overall concordance between microbial and host data matrices. This method rotates, scales, and translates one dataset (e.g., microbiome PCoA coordinates) to maximize the similarity with another dataset (e.g., transcriptome PCoA coordinates) and the statistical significance of the association is typically assessed using a Mantel test⁷³. Tools, such as MaAsLin2, are specifically designed to identify multivariate associations, while controlling for confounding factors, like age, BMI, and technical batch effects, providing robust statistical frameworks for high-dimensional data⁷⁴. Despite the challenges posed by high-dimensional multi-omics data, these correlation-based methods remain essential tools for generating initial hypotheses in microbiota-host interaction studies. The correlation-based methods provide a crucial foundation for subsequent experimental validation of identified relationships, bridging the gap between observational associations and mechanistic understanding.

Regression and classification methods

Regression and classification methods represent a critical advancement beyond correlation-based analyses, moving from identifying associations-to-building microbiome-based predictive models in which microbial features forecast host-related outcomes⁷⁵. These techniques often involve feature selection, probabilistic inference, model optimization, and performance assessment. Regularized regression techniques, particularly least absolute shrinkage and selection operator (LASSO) and Elastic Net, are well-suited to high-dimensional microbiome data. Regularized regression techniques perform variable selection by penalizing regression coefficients, effectively identifying a small subset of microbial taxa that are most predictive of a continuous host outcome (regression), such as immune cell infiltration score, or a categorical outcome (classification), such as response to immunotherapy. For more complex, non-linear relationships, machine learning algorithms, like random forests, are widely used. These models can handle high-dimensional data and provide measures of feature importance, ranking microbes by predictive power. CRC research has demonstrated particularly successful applications of these methods. For example, a random forest-based multiclass model combining gut bacterial abundances with metabolite markers significantly improved diagnostic performance in distinguishing CRC from other gastrointestinal diseases, showcasing the clinical potential of integrated microbiome-host feature analysis⁷⁶. The MetaNN framework, a neural network-based deep learning approach, demonstrates superior classification accuracy for host phenotype prediction by leveraging both synthetic and real metagenomic data⁷⁷. These frameworks illustrate the growing potential of predictive modeling to translate microbiome-host interactions into actionable biomarkers.

Network methods

Network analysis provides a systems-level framework for integrating multi-omics data by representing complex microbiota-host interactions as unified graphs⁷⁸. Nodes correspond to entities across different biological layers in these networks (e.g., bacterial species, host genes, and metabolites), while edges represent statistically significant associations between the nodes, including correlations, partial correlations, or more advanced dependency measures. This approach moves beyond pairwise analyses to reveal larger, interconnected functional modules that may drive biological processes. Methods, like graphical LASSO, enable the inference of conditional dependency networks that estimate direct interactions between nodes after accounting for all other variables in the system. Such estimates provide a more accurate representation of potential direct biological relationships than simple correlation analyses. Once constructed, these networks can be analyzed to identify highly interconnected modules, which are clusters of nodes that may represent functional units, such as a group of co-occurring bacteria interacting with host genes involved in specific signaling pathways (e.g., JAK-STAT or Wnt/β-catenin signaling)⁷⁹. Network topology analysis further revealed biologically significant patterns through centrality measures. Nodes with high-degree centrality (many connections) or high-betweenness centrality (positioned on many shortest paths) may represent keystone species or critical host factors central to network stability and function. For example, microbial networks have been shown to vary spatially within tumors and across disease states with specific modules enriched in cancer samples compared to healthy tissue⁸⁰. Functional enrichment analysis [e.g., Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG)] of host genes within these modules helps elucidate the mechanistic pathways through which microbial communities may influence tumor biology. Overall, network methods offer a powerful integrative approach for dissecting multi-omics interactions and identifying system-level drivers of cancer progression.

Compositionality

A fundamental analytical challenge in microbiome studies arises from the compositional nature of sequencing data. Samples often vary in sequencing depths (library sizes) in high-throughput sequencing results, which yields relative rather than absolute measures of microbial abundance⁸¹. As a result, an apparent increase in the relative abundance of one microbe necessarily corresponds to a proportional decrease across all other microbes, even if the actual counts remain unchanged. This phenomenon, known as compositionality, can distort common analytical tasks, such as diversity estimation, correlation analysis, and differential abundance testing⁸². Standard statistical techniques assuming absolute measurements may therefore yield misleading results. For example, a significant negative correlation between two bacteria may reflect this mathematical artifact rather than a true biological interaction. To address this issue, data normalization methods, such as rarefaction, scaling, and additive or centered log ratio transform, have been developed to transform compositional data into a form that can be readily analyzed with non-compositional analysis techniques, such as linear models⁸³.

Sparsity and zero-inflation

Microbiome datasets typically exhibit high sparsity, meaning that many microbial taxa are absent from most samples, leading to an abundance of zero values in the data matrix. These zeros can arise from the following two distinct sources: true biological absence, in which a microbe is genuinely not present; or technical limitations, in which a microbe is present but not detected due to insufficient sequencing depth. The prevalence of zeros violates the assumptions of many conventional statistical models, biases the estimate of diversity and correlation, and complicates the application of machine learning algorithms. Distinguishing between structural and sampling zeros is essential for accurate biological interpretation. Statistical approaches, such as zero-inflated negative binomial and hurdle models, are often used to account for this characteristic, although improved study design, increased sequencing depth, and technical replicates remain the most effective strategies to reduce technical zeros and improve inference.

High dimensionality and biological variability

Microbiome-host studies often face the “small n, large p” problem, in which the number of features (e.g., thousands of microbial taxa, genes, or metabolites) vastly exceeds the number of samples (e.g., dozens or hundreds of patients). In this high-dimensional space, models are highly prone to overfitting, in which the model memorizes noise and patterns specific to the training dataset but fails to generalize to new, independent data. This situation can lead to the identification of numerous false-positive associations. Utilizing regularization techniques (e.g., LASSO, ridge regression), performing rigorous cross-validation, and seeking validation in independent cohorts are necessary steps to mitigate this risk. In addition, the gut microbiota exhibits substantial interindividual variation influenced by diet, genetics, age, medication, and other factors, which can confound true associations. Including these variables as covariates in models and expanding cohort sizes are necessary to distinguish genuine microbiome-host interactions from confounding effects.