肠道微生物平衡失调与许多人类疾病存在关联，包括结直肠癌（CRC）、炎症性肠病、2型糖尿病、肥胖症、自闭症和阿尔茨海默病。与其他人类疾病相比，CRC是一种具有高死亡率和高转移概率的肠道恶性肿瘤。然而，目前的研究主要关注结直肠癌的预测，却忽视了更为严重的转移性结直肠癌（mCRC）。此外，高维度和小样本导致肠道微生物数据的复杂性增加，增加了传统机器学习模型的难度。为了解决这些挑战，我们收集和处理了非转移性结直肠癌（non-mCRC）和mCRC患者的16S rRNA数据，并计算了其丰度数据。与传统的健康-疾病分类策略不同，我们采用了一种新颖的疾病-疾病分类策略，并提出了一种基于微生物组的多视图卷积变分信息瓶颈（MV-CVIB）模型。实验结果表明，MV-CVIB可以有效地预测mCRC。与其他最先进的模型相比，该模型的AUC值可以达到0.9以上。不仅如此，MV-CVIB还在多个已发表的CRC肠道微生物组数据集上取得了令人满意的预测性能。最后，通过多个肠道微生物组分析，阐明了mCRC与non-mCRC之间的差异，利用患者年龄和微生物组表达评估了CRC的转移性。版权所有© 2023 Cui、Wu、Zhang、Wang、He和Huang。

Imbalances in gut microbes have been implied in many human diseases, including colorectal cancer (CRC), inflammatory bowel disease, type 2 diabetes, obesity, autism, and Alzheimer's disease. Compared with other human diseases, CRC is a gastrointestinal malignancy with high mortality and a high probability of metastasis. However, current studies mainly focus on the prediction of colorectal cancer while neglecting the more serious malignancy of metastatic colorectal cancer (mCRC). In addition, high dimensionality and small samples lead to the complexity of gut microbial data, which increases the difficulty of traditional machine learning models.To address these challenges, we collected and processed 16S rRNA data and calculated abundance data from patients with non-metastatic colorectal cancer (non-mCRC) and mCRC. Different from the traditional health-disease classification strategy, we adopted a novel disease-disease classification strategy and proposed a microbiome-based multi-view convolutional variational information bottleneck (MV-CVIB).The experimental results show that MV-CVIB can effectively predict mCRC. This model can achieve AUC values above 0.9 compared to other state-of-the-art models. Not only that, MV-CVIB also achieved satisfactory predictive performance on multiple published CRC gut microbiome datasets.Finally, multiple gut microbiota analyses were used to elucidate communities and differences between mCRC and non-mCRC, and the metastatic properties of CRC were assessed by patient age and microbiota expression.Copyright © 2023 Cui, Wu, Zhang, Wang, He and Huang.