Abstract 5896: Identification of microbial biomarkers to predict recurrence of oral squamous cell carcinoma

• Published in: Cancer research (Chicago, Ill.) Vol. 83; no. 7_Supplement; p. 5896
• Main Authors: Lyu, Wei-Ni, Lin, Mei-Chun, Lou, Pei-Jen, Lai, Liang-Chuan, Tsai, Mong-Hsun
• Format: Journal Article
• Language: English
• Published: 04.04.2023
• ISSN: 1538-7445; 1538-7445
• DOI: 10.1158/1538-7445.AM2023-5896

Abstract Introduction: Oral cancer is a fatal cancer and the sixth most common neoplasm worldwide. Over 90% of oral cancer is oral squamous cell carcinoma (OSCC). OSCC is a global health problem because of its late diagnosis and high rate of relapse and metastasis. Currently, cumulative evidence has suggested the association between oral microbiome and oral cancer. However, the correlation between the oral microbiome and OSCC recurrence remained unclear. Therefore, we investigated the OSCC patients’ oral microbiota to understand the roles of oral microbiome in the recurrence of OSCC. Materials and Methods: To evaluate the role of the oral microbiome in the recurrence of oral cancer, we compared the oral bacterial composition of tumor samples from the OSCC patients with or without recurrence based on 16S rRNA amplicon sequencing of 54 oral swab samples. Then, the structures of the oral microbiome were analyzed to establish a prediction model for the recurrence of OSCC. To identify microbial signatures capable of distinguishing recurrence from non-recurrence, the 54 tumor samples from OSCC patients were used as a training dataset. Furthermore, 46 external tumor samples from other OSCC patients were used to validate the dataset. Results: The oral bacterial compositions differed in OSCC patients between with and without recurrence. Compared to nonrecurrence OSCC, periodontitis-related bacteria were enriched in OSCC recurrence. Functional analysis of the oral microbiome showed several functions associated with OSCC recurrence including amino acid metabolism, carbohydrate metabolism, lipid metabolism, glucose utilization, and drug resistance. Subsequently, we established a random forest prediction model with high accuracy (accuracy = 0.963) to discriminate OSCC recurrence from the original OSCC based on five specific microbial signatures. Moreover, the prediction model achieved an accuracy of 0.761 in another independent cohort (46 OSCC patients). On the other hand, the accuracy predicted by the current clinical-used model was 0.519 with the training dataset. Thus, our novel model could improve the prediction accuracy of OSCC recurrence. Conclusions: In this study, we elucidated the relationship between oral bacteria and the recurrence of OSCC. Moreover, we established a novel bacteria-based prediction model for the recurrence of OSCC. Thus, the present study provided a new insight and treatment strategy for the clinical prediction of OSCC recurrence. Citation Format: Wei-Ni Lyu, Mei-Chun Lin, Pei-Jen Lou, Liang-Chuan Lai, Mong-Hsun Tsai. Identification of microbial biomarkers to predict recurrence of oral squamous cell carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5896.