Philip Daryll, Hodgkiss Rebecca, Radhakrishnan Swarnima Kollampallath, Sinha Akshat, Acharjee Animesh
Cancer and Genomic Sciences, School of Medical Sciences, College of Medicine and Health, University of Birmingham Dubai, Dubai, UAE.
Cancer and Genomic Sciences, School of Medical Sciences, College of Medicine and Health, University of Birmingham, Birmingham, UK.
J Transl Med. 2025 May 16;23(1):549. doi: 10.1186/s12967-025-06552-w.
Gastrointestinal disorders (GIDs) affect nearly 40% of the global population, with gut microbiome-metabolome interactions playing a crucial role in gastric cancer (GC), colorectal cancer (CRC), and inflammatory bowel disease (IBD). This study aims to investigate how microbial and metabolic alterations contribute to disease development and assess whether biomarkers identified in one disease could potentially be used to predict another, highlighting cross-disease applicability.
Microbiome and metabolome datasets from Erawijantari et al. (GC: n = 42, Healthy: n = 54), Franzosa et al. (IBD: n = 164, Healthy: n = 56), and Yachida et al. (CRC: n = 150, Healthy: n = 127) were subjected to three machine learning algorithms, eXtreme gradient boosting (XGBoost), Random Forest, and Least Absolute Shrinkage and Selection Operator (LASSO). Feature selection identified microbial and metabolite biomarkers unique to each disease and shared across conditions. A microbial community (MICOM) model simulated gut microbial growth and metabolite fluxes, revealing metabolic differences between healthy and diseased states. Finally, network analysis uncovered metabolite clusters associated with disease traits.
Combined machine learning models demonstrated strong predictive performance, with Random Forest achieving the highest Area Under the Curve(AUC) scores for GC(0.94[0.83-1.00]), CRC (0.75[0.62-0.86]), and IBD (0.93[0.86-0.98]). These models were then employed for cross-disease analysis, revealing that models trained on GC data successfully predicted IBD biomarkers, while CRC models predicted GC biomarkers with optimal performance scores.
These findings emphasize the potential of microbial and metabolic profiling in cross-disease characterization particularly for GIDs, advancing biomarker discovery for improved diagnostics and targeted therapies.
胃肠道疾病（GIDs）影响着全球近40%的人口，肠道微生物组与代谢组的相互作用在胃癌（GC）、结直肠癌（CRC）和炎症性肠病（IBD）中起着至关重要的作用。本研究旨在调查微生物和代谢改变如何促进疾病发展，并评估在一种疾病中鉴定出的生物标志物是否有可能用于预测另一种疾病，突出跨疾病适用性。
来自Erawijantari等人（GC：n = 42，健康：n = 54）、Franzosa等人（IBD：n = 164，健康：n = 56）以及Yachida等人（CRC：n = 150，健康：n = 127）的微生物组和代谢组数据集接受了三种机器学习算法，即极端梯度提升（XGBoost）、随机森林和最小绝对收缩和选择算子（LASSO）。特征选择确定了每种疾病特有的以及跨条件共享的微生物和代谢物生物标志物。一个微生物群落（MICOM）模型模拟了肠道微生物生长和代谢物流，揭示了健康状态和疾病状态之间的代谢差异。最后，网络分析揭示了与疾病特征相关的代谢物簇。
组合机器学习模型表现出强大的预测性能，随机森林在GC（0.94[0.83 - 1.00]）、CRC（0.75[0.62 - 0.86]）和IBD（0.93[0.86 - 0.98]）方面获得了最高的曲线下面积（AUC）分数。然后将这些模型用于跨疾病分析，结果显示，在GC数据上训练的模型成功预测了IBD生物标志物，而CRC模型预测GC生物标志物时具有最佳性能分数。
这些发现强调了微生物和代谢谱分析在跨疾病特征描述中的潜力，特别是对于胃肠道疾病，推动了生物标志物的发现，以改善诊断和靶向治疗。
