FNatPred: A Data-Driven Approach for Distinguishing Between NAT and Tumor on the Fungal Microbiome

The role of fungal microbiota in human carcinogenesis remains largely uncharacterized. Recent evidence suggests normal adjacent tissue (NAT) represents an intermediate state between healthy and malignant tissues, highlighting its potential for early cancer detection. Discriminating fungal compositional profiles between tumor and NAT is thus critical for elucidating fungal involvement in oncogenesis. However, the high similarity between tumor and NAT mycobiota poses significant analytical challenges.

To overcome this limitation, we developed a two-level ensemble discriminative model. Base-level classifiers, trained using rigorously filtered fungal microbiota data (based on prevalence, abundance, and quality metrics) via Random Forest, generate initial predictions. A meta-level classifier then integrates these base predictions, transforming high-dimensional, sparse fungal feature data into a low-dimensional, dense representation optimized for discrimination.

Our approach achieved clear separation between tumor and NAT mycobiomes across multiple cancer types, with particularly pronounced discrimination in colorectal cancer (CRC). The proposed model significantly outperformed existing methods in tumor-NAT classification, demonstrating an average AUC improvement of approximately 10%.