口腔微生物群的变化与口腔鳞状细胞癌（OSCC）相关。口腔微生物源性标志物已被用作OSCC的标记。然而，OSCC复发期间口腔微生物群的结构以及预测OSCC复发的生物标志物仍未知。为了识别与OSCC复发相关的微生物标志物以预测OSCC复发，我们对54个来自OSCC患者口腔拭子样本进行了16S rRNA扩增子测序。观察到了复发与非复发患者之间的细菌组成差异。我们发现Granulicatella、 Peptostreptococcus、Campylobacter、 Porphyromonas、 Oribacterium、Actinomyces、 Corynebacterium、Capnocytophaga和Dialister在OSCC复发中富集。口腔微生物组的功能分析显示与非复发相比，与OSCC复发相关的功能发生了改变。我们构建了一个随机森林预测模型，包括Leptotrichia trevisanii、Capnocytophaga sputigena、Capnocytophaga、Cardiobacterium和Olsenella这五个微生物标志物，以区分OSCC复发与原发OSCC（准确度=0.963）。此外，我们在另一个独立队列（46名OSCC患者）中验证了预测模型，准确度达到了0.761。我们比较了五个微生物标志物和两个临床病理指标（切缘和淋巴结计数）对于预测OSCC复发的准确度。基于五个微生物标志物的模型预测结果显示比基于两个临床病理指标的临床结果有着较高的准确度。本研究证实了使用与复发相关的微生物标志物进行OSCC复发预测的可行性，这是一种无创且有效的方法。我们的发现可能有助于OSCC复发的预后和治疗。

Changes in the oral microbiome are associated with oral squamous cell carcinoma (OSCC). Oral microbe-derived signatures have been utilized as markers of OSCC. However, the structure of the oral microbiome during OSCC recurrence and biomarkers for the prediction of OSCC recurrence remains unknown. To identify OSCC recurrence-associated microbial biomarkers for the prediction of OSCC recurrence, we performed 16S rRNA amplicon sequencing on 54 oral swab samples from OSCC patients. Differences in bacterial compositions were observed in patients with vs without recurrence. We found that Granulicatella, Peptostreptococcus, Campylobacter, Porphyromonas, Oribacterium, Actinomyces, Corynebacterium, Capnocytophaga, and Dialister were enriched in OSCC recurrence. Functional analysis of the oral microbiome showed altered functions associated with OSCC recurrence compared with nonrecurrence. A random forest prediction model was constructed with five microbial signatures including Leptotrichia trevisanii, Capnocytophaga sputigena, Capnocytophaga, Cardiobacterium, and Olsenella to discriminate OSCC recurrence from original OSCC (accuracy = 0.963). Moreover, we validated the prediction model in another independent cohort (46 OSCC patients), achieving an accuracy of 0.761. We compared the accuracy of the prediction of OSCC recurrence between the five microbial signatures and two clinicopathological parameters, including resection margin and lymph node counts. The results predicted by the model with five microbial signatures showed a higher accuracy than those based on the clinical outcomes from the two clinicopathological parameters. This study demonstrated the validity of using recurrence-related microbial biomarkers, a noninvasive and effective method for the prediction of OSCC recurrence. Our findings may contribute to the prognosis and treatment of OSCC recurrence.