Institute of Biotechnology, National Taiwan University, Taipei 10617, Taiwan.
Department of Otolaryngology, National Taiwan University Hospital, Taipei 10002, Taiwan.
ACS Infect Dis. 2023 Sep 8;9(9):1783-1792. doi: 10.1021/acsinfecdis.3c00269. Epub 2023 Aug 11.
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 , , , , , , , , and 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 , , , , and 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.
口腔微生物组的变化与口腔鳞状细胞癌(OSCC)有关。口腔微生物衍生的特征已被用作 OSCC 的标志物。然而,OSCC 复发期间口腔微生物组的结构和用于预测 OSCC 复发的生物标志物仍然未知。为了确定与 OSCC 复发相关的口腔微生物生物标志物以预测 OSCC 复发,我们对 54 名 OSCC 患者的口腔拭子样本进行了 16S rRNA 扩增子测序。我们观察到复发患者和无复发患者的细菌组成存在差异。我们发现 、 、 、 、 、 、 和 在 OSCC 复发中富集。与非复发相比,口腔微生物组的功能分析显示与 OSCC 复发相关的功能发生改变。我们构建了一个随机森林预测模型,其中包含五个微生物特征,包括 、 、 、 和 ,以区分 OSCC 复发和原始 OSCC(准确性=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,准确率为 0.761。我们比较了五个微生物特征和两个临床病理参数(包括切缘和淋巴结计数)对 OSCC 复发的预测准确性。模型中五个微生物特征的预测结果比基于两个临床病理参数的临床结果具有更高的准确性。这项研究证明了使用与复发相关的微生物生物标志物的有效性,这是一种非侵入性且有效的预测 OSCC 复发的方法。我们的研究结果可能有助于 OSCC 复发的预后和治疗。