Finding the combination of multiple biomarkers to diagnose oral squamous cell carcinoma – A data mining approach

• Autor(a) principal: da Costa, Nattane Luíza
• Data de Publicação: 2022
• Outros Autores: de Sá Alves, Mariana [UNESP], de Sá Rodrigues, Nayara [UNESP], Bandeira, Celso Muller [UNESP], Oliveira Alves, Mônica Ghislaine, Mendes, Maria Anita, Cesar Alves, Levy Anderson, Almeida, Janete Dias [UNESP], Barbosa, Rommel
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1016/j.compbiomed.2022.105296; http://hdl.handle.net/11449/234108
• topic: Data mining; Feature selection; Machine learning; Metabolites; Oral squamous cell carcinoma; Salivary biomarkers

Data mining has proven to be a reliable method to analyze and discover useful knowledge about various diseases, including cancer research. In particular, data mining and machine learning algorithms to study oral squamous cell carcinoma (OSCC), the most common form of oral cancer, is a new area of research. This malignant neoplasm can be studied using saliva samples. Saliva is an important biofluid that must be used to verify potential biomarkers associated with oral cancer. In this study, first, we provide an overview of OSSC diagnoses based on machine learning and salivary metabolites. To our knowledge, this is the first study to apply advanced data mining techniques to diagnose OSCC. Then, we give new results of classification and feature selection algorithms used to identify potential salivary biomarkers of OSCC. To accomplish this task, we used the filter feature selection random forest importance algorithm and a wrapper methodology to evaluate the importance of metabolites obtained from gas chromatography mass-spectrometry (GC-MS) in the context of differentiation of OSCC and the control group. Salivary samples (n = 68) were collected for the control group, and the OSCC group were from patients matched for gender, age, and smoking habit. The classification process occurred based on Random Forest (RF) classification algorithm along with 10-cross validation. The results showed that glucuronic acid, maleic acid, and batyl alcohol can classify the samples with an area under the curve (AUC) of 0.91 versus an AUC of 0.76 using all 51 metabolites analyzed. The methodology used in this study can assist healthcare professionals and be adopted to discover diagnostic biomarkers for other diseases.