## 4 Discussion
### Study Limitations and Future Directions  
This study may have some limitations, such as a reliance on prior biological knowledge, generalizability, validation, experimental limitations, and dietary and environmental factors. The CRC-associated metagenomic dataset is inherently complex and high-dimensional, making it challenging to interpret the results comprehensively. The functional profiles derived from the metagenomic data may contain noise or biases, which could affect the accuracy of the findings. While incorporating enzyme categories as domain knowledge provides insights, it may introduce biases by prioritizing specific pathways or enzymes, potentially overlooking novel or less-studied biomarkers. The EC-nomenclature-based G-S-M method depends heavily on accurate and comprehensive biological databases, which may not always reflect the latest discoveries. The findings, such as the associations with specific enzymes (e.g., glycosidases and CoA-transferases) and microbial taxa, may not generalize across diverse populations, due to differences in microbiome composition influenced by geography, diet, and genetics. This study relies primarily on computational modeling and cross-validation, lacking experimental validation to confirm the causal roles of the identified enzymes or microbes in CRC progression. The precise molecular mechanisms linking the identified microbial enzymes, metabolites, and CRC progression remain unresolved, highlighting a gap in mechanistic understanding. The interactions between microbiota and host genetics, immune responses, and environmental factors (e.g., diet) are not fully explored in this study. The traditional feature selection methods might fail to capture the full biological complexity of the data, but even the EC-nomenclature-based G-S-M approach has limitations in terms of performance, as noted in the results. The dataset appears to be cross-sectional, which limits insights into the temporal changes in the microbiome and their role in CRC development or progression. This study does not account for the influence of external factors like diet, medication, or lifestyle, which are known to significantly impact the gut microbiome. While some key taxa (e.g., _Escherichia coli_ and _Salmonella enterica_) are identified, this study does not deeply explore the microbial interactions or the broader ecological dynamics within the gut microbiome. Given the complexity of the data and the number of classifiers tested, there is a risk of overfitting, which could reduce the reproducibility of the findings when applied to new datasets. By addressing these limitations in future studies, researchers can enhance the robustness and translational potential of metagenomic analyses in understanding CRC and other microbiome-associated diseases. Current metagenomic datasets may lack adequate representation of diverse populations, leading to limited generalizability. Incomplete functional annotation in microbial databases may hinder the identification of novel biomarkers. To enlighten the utility of metagenomics in CRC, our research highlights specific enzymes and biological pathways for further exploration. Future research should focus on addressing unresolved questions, including the precise molecular mechanisms linking microbial metabolites to CRC development, the role of host genetics in shaping microbiome interactions, and the long-term impact of dietary interventions on CRC risk and gut health. In such studies, it is also important to include data from diverse populations in order to capture the geographical, genetic, and dietary variations in microbiome composition. Combining metagenomic data with transcriptomic, metabolomic, and proteomic analyses can also provide new insights into CRC-associated pathways. To this end, it is important to develop multi-omics models to study the interplay between microbial metabolites and host pathways.