Wang Jun, Cong Yuan, Tang Bo, Liu Juan, Pu Ke
Department of Gastroenterology, Affiliated Hospital of North Sichuan Medical College, Nanchong, Sichuan, China.
School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
Sci Rep. 2025 Jul 12;15(1):25268. doi: 10.1038/s41598-025-08915-1.
Colorectal cancer (CRC) exhibits substantial heterogeneity in molecular subtypes and clinical outcomes. We performed an integrative analysis of multi-omics data from 274 CRC patients to investigate the impact of gut microbiota composition on prognosis, identify novel subtypes, and develop a machine learning-based prognostic model. Our microbiome analysis revealed significant differences between CRC and normal tissues. Multi-omics clustering identified two major CRC subtypes, CS1 and CS2, with distinct molecular characteristics and survival outcomes. We developed the Multi-Omics Integrative Clustering and Machine Learning Score (MCMLS) model, which demonstrated strong prognostic value in predicting patient survival and outperformed existing models. The MCMLS low-score group exhibited higher immune cell infiltration, increased metabolic pathway activity, and potentially better immunotherapy response. In contrast, the MCMLS high-score group showed higher mutation burden, fibroblast infiltration, and enrichment of cell adhesion and migration pathways. Bacterial analysis revealed differentially abundant bacteria associated with prognosis. Importantly, MCMLS consistently predicted immunotherapy response across six independent datasets. Our findings highlight the complex interplay between the gut microbiome, tumor microenvironment, and immune landscape in CRC, providing valuable insights for improving patient stratification and personalized treatment strategies.
结直肠癌（CRC）在分子亚型和临床结局方面表现出显著的异质性。我们对274例CRC患者的多组学数据进行了综合分析，以研究肠道微生物群组成对预后的影响，识别新的亚型，并开发基于机器学习的预后模型。我们的微生物组分析揭示了CRC组织与正常组织之间的显著差异。多组学聚类确定了两种主要的CRC亚型，即CS1和CS2，它们具有不同的分子特征和生存结局。我们开发了多组学综合聚类和机器学习评分（MCMLS）模型，该模型在预测患者生存方面显示出强大的预后价值，并且优于现有模型。MCMLS低分患者组表现出更高的免疫细胞浸润、增加的代谢途径活性以及潜在的更好的免疫治疗反应。相比之下，MCMLS高分患者组显示出更高的突变负担、成纤维细胞浸润以及细胞黏附和迁移途径的富集。细菌分析揭示了与预后相关的差异丰度细菌。重要的是，MCMLS在六个独立数据集中均一致地预测了免疫治疗反应。我们的研究结果突出了CRC中肠道微生物组、肿瘤微环境和免疫格局之间的复杂相互作用，为改善患者分层和个性化治疗策略提供了有价值的见解。
