The gut microbiome significantly influences responses to cancer therapies, including immunotherapies, primarily through its impact on the metabolome. Despite some existing studies addressing the effects of specific microbial genera on individual metabolites, there is little to no prior work focused on identifying the key microbiome components at the genus level that shape the overall metabolome profile. To bridge this gap, we introduce B-MASTER (Bayesian Multivariate regression Analysis for Selecting Targeted Essential Regressors), a fully Bayesian framework incorporating an L1 penalty to promote sparsity in the coefficient matrix and an L2 penalty to shrink coefficients for non-major covariate components simultaneously, thereby isolating essential regressors. The method is complemented with a scalable Gibbs sampling algorithm, whose computational speed increases linearly with the number of parameters and remains largely unaffected by sample size and data-specific characteristics for models of fixed dimensions. Notably, B-MASTER achieves full posterior inference for models with up to four million parameters within a practical time-frame. Using this approach, we identify key microbial genera influencing the overall metabolite profile, conduct an in-depth analysis of their effects on the most abundant metabolites, and investigate metabolites differentially abundant in colorectal cancer patients. These results provide foundational insights into the impact of the microbiome at the genus level on metabolite profiles relevant to cancer, a relationship that remains largely unexplored in the existing literature.

翻译：肠道微生物组主要通过其对代谢组的影响，显著调控包括免疫疗法在内的癌症治疗反应。尽管已有部分研究探讨了特定微生物属对单个代谢物的影响，但鲜有或几乎没有先前工作致力于在属水平上识别塑造整体代谢组谱的关键微生物组成分。为填补这一空白，我们提出了B-MASTER（用于筛选靶向必需回归因子的贝叶斯多元回归分析），这是一个完全贝叶斯框架，它结合了L1惩罚以促进系数矩阵的稀疏性，同时采用L2惩罚以收缩非主要协变量成分的系数，从而分离出必需回归因子。该方法辅以一个可扩展的吉布斯采样算法，其计算速度随参数数量线性增长，并且对于固定维度的模型，基本不受样本量和数据特定特征的影响。值得注意的是，B-MASTER能够在实际可行的时间范围内，对参数高达四百万的模型实现完整的后验推断。利用该方法，我们识别了影响整体代谢物谱的关键微生物属，深入分析了它们对最丰富代谢物的影响，并研究了在结直肠癌患者中差异丰度的代谢物。这些结果为微生物组在属水平上对癌症相关代谢物谱的影响提供了基础性见解，而这一关系在现有文献中仍很大程度上未被探索。