Microbiota and metabolomic profiling coupled with machine learning to identify biomarkers and drug targets in nasopharyngeal carcinoma

作者：Junsong Liu, Chongwen Xu, Rui Wang, Jianhua Huang, Ruimin Zhao, Rui Wang · 发表于：Frontiers in Pharmacology · 年份：2025 · DOI：10.3389/fphar.2025.1551411 · 被引用次数：6 · 研究领域：Head and Neck Cancer Studies、Gut microbiota and health、Metabolomics and Mass Spectrometry Studies

Background Nasopharyngeal carcinoma (NPC) is a prevalent malignancy in certain regions, with radiotherapy as the standard treatment. However, resistance to radiotherapy remains a critical challenge, necessitating the identification of novel biomarkers and therapeutic targets. The tumor-associated microbiota and metabolites have emerged as potential modulators of radiotherapy outcomes. Methods This study included 22 NPC patients stratified into radiotherapy-responsive (R, n = 12) and radiotherapy-non-responsive (NR, n = 10) groups. Tumor tissue and fecal samples were subjected to 16S rRNA sequencing to profile microbiota composition and targeted metabolomics to quantify short-chain fatty acids (SCFAs). The XGBoost algorithm was applied to identify microbial taxa associated with radiotherapy response, and quantitative PCR (qPCR) was used to validate key findings. Statistical analyses were conducted to assess differences in microbial diversity, relative abundance, and metabolite levels between the groups. Results Significant differences in alpha diversity at the species level were observed between the R and NR groups. Bacteroides acidifaciens was enriched in the NR group, while Propionibacterium acnes and Clostridium magna were more abundant in the R group. Machine learning identified Acidosoma , Propionibacterium acnes , and Clostridium magna as key predictors of radiotherapy response. Metabolomic profiling revealed elevated acetate levels in the NR group, implicating its role ...