BackgroundMicrobial dysbiosis and microbiome-induced inflammation have emerged as important factors in oral squamous cell carcinoma (OSCC) tumorigenesis during the last two decades. However, the “rare biosphere” of the oral microbiome, including fungi, has been sparsely investigated. This study aimed to characterize the salivary mycobiome in a prospective Sudanese cohort of OSCC patients and to explore patterns of diversities associated with overall survival (OS).Materials and MethodsUnstimulated saliva samples (n = 72) were collected from patients diagnosed with OSCC (n = 59) and from non-OSCC control volunteers (n = 13). DNA was extracted using a combined enzymatic–mechanical extraction protocol. The salivary mycobiome was assessed using a next-generation sequencing (NGS)-based methodology by amplifying the ITS2 region. The impact of the abundance of different fungal genera on the survival of OSCC patients was analyzed using Kaplan–Meier and Cox regression survival analyses (SPPS).ResultsSixteen genera were identified exclusively in the saliva of OSCC patients. Candida, Malassezia, Saccharomyces, Aspergillus, and Cyberlindnera were the most relatively abundant fungal genera in both groups and showed higher abundance in OSCC patients. Kaplan–Meier survival analysis showed higher salivary carriage of the Candida genus significantly associated with poor OS of OSCC patients (Breslow test: p = 0.043). In contrast, the higher salivary carriage of Malassezia showed a significant association with favorable OS in OSCC patients (Breslow test: p = 0.039). The Cox proportional hazards multiple regression model was applied to adjust the salivary carriage of both Candida and Malassezia according to age (p = 0.029) and identified the genus Malassezia as an independent predictor of OS (hazard ratio = 0.383, 95% CI = 0.16–0.93, p = 0.03).ConclusionThe fungal compositional patterns in saliva from OSCC patients were different from those of individuals without OSCC. The fungal genus Malassezia was identified as a putative prognostic biomarker and therapeutic target for OSCC.

Background The majority of oral cavity cancers arise in the oral tongue. The aim of this study was to evaluate the prognostic value of tumor budding in oral tongue squamous cell carcinoma, both as a separate variable and in combination with depth of invasion. We also assessed the prognostic impact of the 8th edition of the American Joint Committee on Cancer's TNM classification (TNM8), where depth of invasion (DOI) supplements diameter in the tumor size (T) categorization. Methods Patients diagnosed with primary oral tongue squamous cell carcinoma were evaluated retrospectively. Spearman bivariate correlation analyses with bootstrapping were used to identify correlation between variables. Prognostic value of clinical and histopathological variables was assessed by Log rank and Cox regression analyses with bootstrapping using 5-year disease specific survival as outcome. The significance level for the hypothesis test was 0.05. Results One-hundred and fifty patients had available material for microscopic evaluation on Hematoxylin and Eosin-stained slides and were included in the analyses. Reclassification of

Background: Oral squamous cell carcinoma (OSCC) is increasing at an alarming rate particularly in low-income countries. This urges for research into noninvasive, user-friendly diagnostic tools that can be used in limited-resource settings. This study aims to test and validate the feasibility of e-nose technology for detecting OSCC in the limited-resource settings of the Sudanese population. Methods: Two e-nose devices (Aeonose™, eNose Company, Zutphen, The Netherlands) were used to collect breath samples from OSCC (n = 49) and control (n = 35) patients. Patients were divided into a training group for building an artificial neural network (ANN) model and a blinded control group for model validation. The Statistical Package for the Social Sciences (SPSS) software was used for the analysis of baseline characteristics and regression. Aethena proprietary software was used for data analysis using artificial neural networks based on patterns of volatile organic compounds. Results: A diagnostic accuracy of 81% was observed, with 88% sensitivity and 71% specificity. Conclusions: This study demonstrates that e-nose is an efficient tool for OSCC detection in limited-resource settings, where it offers a valuable cost-effective strategy to tackle the burden posed by OSCC.