Zhou Yuhang, Han Wenjie, Feng Yun, Wang Yue, Liu Xiaolin, Sun Tao, Xu Junnan
Department of Breast Medicine 1, Cancer Hospital of China Medical University, Liaoning Cancer Hospital, Shenyang, China.
Department of Pharmacology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital, Shenyang, China.
Front Immunol. 2025 Mar 18;16:1539653. doi: 10.3389/fimmu.2025.1539653. eCollection 2025.
The gut microbiota is associated with the response to immunotherapy in cutaneous melanoma (CM). However, gut fungal biomarkers and bacterial-fungal interactions have yet to be determined.
Metagenomic sequencing data of stool samples collected before immunotherapy from three independent groups of European ancestry CM patients were collected. After characterizing the relative abundances of bacteria and fungi, Linear Discriminant Analysis Effect Size (LEfSe) analysis, Random Forest (RF) model construction, and SHapley Additive exPlanations (SHAP) methodology were applied to identify biomarkers and key bacterial-fungal interactions associated with immunotherapy responders in CM.
Diversity analysis revealed significant differences in the bacterial and fungal composition between CM immunotherapy responders and non-responders. LEfSe analysis identified 45 bacterial and 4 fungal taxa as potential biomarkers. After constructing the RF model, the AUC of models built using bacterial and fungal data separately were 0.64 and 0.65, respectively. However, when bacterial and fungal data were combined, the AUC of the merged model increased to 0.71. In the merged model, the following taxa were identified as important biomarkers: , , , , , , , and , which were associated with responders, whereas was associated with non-responders. Moreover, the positive correlation interaction between and is considered a key bacterial-fungal interaction associated with CM immunotherapy response.
Our results provide valuable insights for the enrichment of responders to immunotherapy in CM patients. Moreover, this study highlights the critical role of bacterial-fungal interactions in CM immunotherapy.
肠道微生物群与皮肤黑色素瘤（CM）的免疫治疗反应相关。然而，肠道真菌生物标志物以及细菌与真菌的相互作用尚未确定。
收集了三组具有欧洲血统的CM患者在免疫治疗前采集的粪便样本的宏基因组测序数据。在对细菌和真菌的相对丰度进行表征后，应用线性判别分析效应大小（LEfSe）分析、随机森林（RF）模型构建和SHapley加性解释（SHAP）方法来识别与CM免疫治疗反应者相关的生物标志物和关键细菌 - 真菌相互作用。
多样性分析显示CM免疫治疗反应者与无反应者之间的细菌和真菌组成存在显著差异。LEfSe分析确定了45个细菌类群和4个真菌类群为潜在生物标志物。构建RF模型后，分别使用细菌和真菌数据构建的模型的AUC分别为0.64和0.65。然而，当将细菌和真菌数据合并时，合并模型的AUC增加到0.71。在合并模型中，以下类群被确定为重要生物标志物：[此处原文缺失具体类群名称]，它们与反应者相关，而[此处原文缺失具体类群名称]与无反应者相关。此外，[此处原文缺失具体类群名称]与[此处原文缺失具体类群名称]之间的正相关相互作用被认为是与CM免疫治疗反应相关的关键细菌 - 真菌相互作用。
我们的结果为CM患者免疫治疗反应者的富集提供了有价值的见解。此外，本研究强调了细菌 - 真菌相互作用在CM免疫治疗中的关键作用。
