Ma Chen-Ying, Zhao Jing, Xu Xiao-Ting, He Xiao-Lan, Qin Song-Bing, Zhou Ju-Ying
Department of Radiation Oncology, The First Affiliated Hospital of Soochow University, No. 188 Shizi Street, Suzhou, 215006, China.
State Key Laboratory of Radiation Medicine and Protection, Soochow University, Suzhou, 215123, China.
Discov Oncol. 2025 Jul 1;16(1):1220. doi: 10.1007/s12672-025-03077-y.
INTRODUCTION: Radiation enteritis (RE), a common side effect, is a growing health concern, particularly the severe acute form of RE (SARE), among cervical cancer patients undergoing radiation therapy. Currently, there is no noninvasive diagnostic method for SARE. This study aimed to identify gut microbiomics- and metabolomics-based signatures, and assess their predictive value for SARE. METHODS: Samples from 50 cervical cancer patients receiving volumetric modulated arc therapy (VMAT) were collected for gut microbiota and metabolomic profiling. 16 S rDNA amplicon sequencing analyzed gut microbiota, and nontargeted liquid chromatography-mass spectrometry determined metabolomic profiles. Multivariate and pathway analyses identify independent metabolites associated with SARE. A predictive nomogram for SARE, combining multi-omics-based signatures and clinical characteristics, was constructed and evaluated using the area under the receiver operating characteristic curve (AUC) and calibration curve. RESULTS: Fecal microbiome analysis showed characteristic alterations in SARE, mainly including Faecalibacterium enterotype-3, Escherichia, and Shigella enterotype-2. Metabolomic analyses identified a panel of molecules significantly associated with SARE. Furthermore, an intuitive nomogram consisting of these multi-omics signatures (serum COX-2 and fecal phenylethylamine), combined with clinical characteristics with predictive value, was constructed to predict SARE. Notably, the evaluation of model performance suggested an excellent predictive discrimination for SARE [AUC, 0.975; 95% confidence interval (CI), 0.953-0.998]. Calibration curve analysis showed an adequate calibration for the model and good consistency between the predicted SARE cases with this newly developed model and the actual SARE cases. CONCLUSION: This study identified noninvasive signatures, including COX-2 and phenylethylamine, as promising predictive biomarkers for SARE and developed an intuitive nomogram with good predictive accuracy for SARE in cervical cancer patients.
引言：放射性肠炎（RE）是一种常见的副作用，在接受放射治疗的宫颈癌患者中，它对健康的影响日益受到关注，尤其是严重急性放射性肠炎（SARE）。目前，尚无针对SARE的非侵入性诊断方法。本研究旨在识别基于肠道微生物组学和代谢组学的特征，并评估其对SARE的预测价值。 方法：收集50例接受容积调强弧形放疗（VMAT）的宫颈癌患者的样本，用于肠道微生物群和代谢组学分析。通过16S rDNA扩增子测序分析肠道微生物群，采用非靶向液相色谱-质谱法测定代谢组学图谱。多变量和通路分析确定与SARE相关的独立代谢物。构建了一个结合多组学特征和临床特征的SARE预测列线图，并使用受试者操作特征曲线（AUC）下的面积和校准曲线进行评估。 结果：粪便微生物组分析显示SARE存在特征性改变，主要包括3型粪杆菌属、大肠杆菌属和2型志贺氏菌属。代谢组学分析确定了一组与SARE显著相关的分子。此外，构建了一个由这些多组学特征（血清COX-2和粪便苯乙胺）组成的直观列线图，并结合具有预测价值的临床特征来预测SARE。值得注意的是，模型性能评估表明该模型对SARE具有出色的预测判别能力【AUC，0.975；95%置信区间（CI），0.953 - 0.998】。校准曲线分析表明该模型校准良好，新开发模型预测的SARE病例与实际SARE病例之间具有良好的一致性。 结论：本研究确定了包括COX-2和苯乙胺在内的非侵入性特征，作为SARE有前景的预测生物标志物，并为宫颈癌患者开发了一个对SARE具有良好预测准确性的直观列线图。
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