DeepMetabio mCRC筛选器：用于结直肠肝转移早期风险预测和生物标志物发现的多组学深度学习框架
DeepMetabio-mCRC Screener: A Multi-Omics Deep Learning Framework for Early Risk Prediction and Biomarker Discovery in Colorectal Liver Metastasis.
摘要(中文译文)
结直肠癌肝转移（CRLM）仍是结直肠癌（CRC）患者死亡的主要原因，然而目前仍缺乏有效的预测工具和可靠的生物标志物。为解决这一空白，研究者开发了DeepMetabio-mCRC Screener，这是一个整合大规模转录组谱与血清代谢组学的多组学框架。在包含1，077例CRC样本的队列中，研究使用620个代谢相关基因训练卷积神经网络，在验证队列中获得0.92的受试者工作特征曲线下面积，在独立测试队列中为0.97，优于10种已建立的机器学习模型的性能。模型衍生的转录组风险评分揭示了22个与转移进展和CRLM发生相关的核心代谢特征，尤其是视黄醇和色氨酸代谢。跨组学整合揭示氨基羧基粘康酸半醛脱羧酶（ACMSD）是一个与烟酰胺腺嘌呤二核苷酸生物合成受损相关的有前景的生物标志物。在100例CRC患者中的临床验证证实，CRLM患者的ACMSD水平升高，且与晚期分期、复发风险、免疫炎症型肿瘤微环境以及对表皮生长因子受体/血管内皮生长因子受体靶向治疗的敏感性增高相关。在体外实验中，ACMSD敲低不仅通过抑制转化生长因子-β/上皮-间质转化通路而抑制CRC细胞迁移，还与促炎和免疫应答通路的下调及免疫细胞浸润减少相关。这些发现共同验证了DeepMetabio-mCRC Screener作为一个重要的早期风险预测工具，并强调了通过该框架鉴定的ACMSD作为CRLM患者诊断、预后、分子特征描述和治疗决策的多功能生物标志物。
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.
如何引用
AMA
Hongyu Zhang, Ke Wang, Runqiu Guo, Xiaochuan Wu, Qingquan Chen, Qiaojun He, et al. DeepMetabio-mCRC Screener: A Multi-Omics Deep Learning Framework for Early Risk Prediction and Biomarker Discovery in Colorectal Liver Metastasis.. Computational and structural biotechnology journal. 2026; doi:10.34133/csbj.0074.
APA
Hongyu Zhang, Ke Wang, Runqiu Guo, Xiaochuan Wu, Qingquan Chen, Qiaojun He, et al (2026). DeepMetabio-mCRC Screener: A Multi-Omics Deep Learning Framework for Early Risk Prediction and Biomarker Discovery in Colorectal Liver Metastasis.. Computational and structural biotechnology journal. https://doi.org/10.34133/csbj.0074
GB/T 7714
Hongyu Zhang, Ke Wang, Runqiu Guo, Xiaochuan Wu, Qingquan Chen, Qiaojun He, et al. DeepMetabio-mCRC Screener: A Multi-Omics Deep Learning Framework for Early Risk Prediction and Biomarker Discovery in Colorectal Liver Metastasis.[J]. Computational and structural biotechnology journal, 2026 doi:10.34133/csbj.0074.
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