DeepCRC: Precision Colorectal Cancer Diagnosis Using Deep Learning and Metagenomic Biomarkers

Colorectal cancer (CRC) is one of the leading causes of cancer-related deaths globally, underscoring the critical need for early detection. Microbial biomarker detection offers significant advantages in revealing the physiological and pathological mechanisms of CRC, as well as in early screening. It provides not only deep insights into disease progression but also enables the possibility of non-invasive early diagnosis. The utilization of metagenomic shotgun sequencing (MGS) technology and the bioBakery workflow to analyze gut microbiome samples from six independent cohorts (n = 1291), including 645 CRC patients and 646 healthy controls (HC). Metagenomic analysis revealed a significant reduction in microbial diversity and identified 117 microbial species with altered abundance in CRC patients (p < 0.05). The DeepCRC model, incorporating key biomarkers and clinical metadata, achieved 97.70% accuracy and an AUC of 0.982 on an independent test set. SHAP analysis indicated that microbial species such as Ruminococcaceae unclassified SGB15260 and Clostridium leptum impact CRC significantly. These bacteria are associated with CRC through mechanisms involving inflammation and gut barrier dysfunction. The superior performance of the DeepCRC diagnostic model is expected to advance the development of non-invasive CRC screening methods. Source code is available at https://github.com/NENUBioCompute/DeepCRC.