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DeepMetabio-mCRC Screener: A Multi-Omics Deep Learning Framework for Early Risk Prediction and Biomarker Discovery in
Hongyu Zhang1, Ke Wang2, Runqiu Guo1
1Zhejiang Province Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Science, Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Zhejiang University, Hangzhou 310058, China.
Abstract:
Colorectal liver metastasis (CRLM) remains the primary cause of mortality in patients with colorectal cancer (CRC), yet effective predictive tools and reliable biomarkers are still lacking. DeepMetabio-mCRC Screener, an integrated multi-omics framework combining large-scale transcriptomic profiles with serum metabolomics, was developed to address this gap. In a cohort of 1,077 CRC samples, 620 metabolism-related genes were used to train a convolutional neural network, yielding an area under the receiver operating characteristic curve of 0.92 in the validation cohort and 0.97 in the independent testing cohort, outperforming the performance of the 10 established machine learning models. Model-derived transcriptomic risk scores revealed 22 core metabolic features associated with metastatic progression and CRLM occurrence, particularly retinol and tryptophan metabolism. Cross-omics integration revealed aminocarboxymuconate-semialdehyde decarboxylase (ACMSD) as a promising biomarker associated with impaired nicotinamide adenine dinucleotide biosynthesis. Clinical validation in 100 CRC patients confirmed elevated ACMSD levels in patients with CRLM, which correlated with advanced stage, recurrence risk, an immune-inflamed tumor microenvironment, and heightened sensitivity to epidermal growth factor receptor/vascular endothelial growth factor receptor-targeted therapies. In vitro, ACMSD knockdown was associated not only with suppressed CRC cell migration caused by inhibition of the transforming growth factor-β/epithelial-to-mesenchymal transition pathway but also with decreased proinflammatory and immune-responsive pathways and reduced immune cell infiltration. These findings collectively validate the DeepMetabio-mCRC Screener as a substantial early risk prediction tool and underscore ACMSD, identified through this framework, as a multifunctional biomarker for diagnosis, prognosis, molecular characterization, and therapeutic decision-making in patients with CRLM.
