Systematic review and meta-analysis of oral squamous cell carcinoma associated oral microbiome - Peter, Tabitha K.; Withanage, Miyuraj H. H.; Comnick, Carissa L.; Pendleton, Chandler; Dabdoub, Shareef; Ganesan, Sukirth; Drake, David; Banas, Jeffrey; Xie, Xian Jin; Zeng, Erliang (Frontiers in Microbiology, 2022)
The intersection between the human oral microbiome and oral health is an emerging area of study which has gained momentum over the last decade. This momentum has motivated a search for associations between the oral microbiome and oral cancer, in hopes of identifying possible biomarkers that facilitate earlier diagnosis and improved prognosis for patients with that disease. The present study examined the relationship between the microbiome in the human oral cavity and oral squamous cell carcinoma (OSCC). We searched the literature for case-control studies which focused on the relationship between the human oral microbiome and OSCC. We aggregated three types of data from these studies: microbiome data at the genus level, predicted functional pathway data, and gene abundance data. From these data, we noted several microbial genera which may be associated with oral cancer status, including Fusobacterium. We also identified functional pathways which merit further investigation, including pyrimidine metabolism and RNA degradation. In addition, our analysis of gene abundance data identified the gene K06147 (ATP-binding cassette, subfamily B, bacterial) as being over abundant in OSCC samples. Our results are generalizations which identified some currents that we believe could guide further research. Our work faced several limitations related to the heterogeneity of the available data. Wide variation in methods for sample collection, methods for controlling for known behavioral risk factors, computing platform choice, and methods for case-control design all posed confounding factors in this work. We examined the current methods of data collection, data processing, and data reporting in order to offer suggestions toward the establishment of best practices within this field. We propose that these limitations should be addressed through the implementation of standardized data analytic practices that will conform to the rigor and reproducibility standards required of publicly funded research.