Abstract
Introduction: Burkitt lymphoma belongs to the group of non-Hodgkin lymphomas. Although curable in 80% of less advanced stages, it presents in advanced stages in about 75% of cases in Brazil’s Northeast region, requiring urgent and intensive care in the early stages of treatment. Objectives: therefore, this study aimed to verify the participation of MBL-2 gene polymorphisms in the development of Burkitt lymphoma. Methods: In this article, computational approaches based on the Machine Learning technique were used, where we implemented the Random Forest and KMeans algorithms to classify patterns of individuals diagnosed with the disease and, therefore, differentiate them from healthy individuals. A group of 56 patients aged 0 to 18 years, with Burkitt lymphoma, from a reference hospital in the treatment of childhood cancer, was evaluated, together with a control group consisting of 150 samples, all of which were tested for exon 1 polymorphisms and the MBL2 gene -221 and -550 regions. Results: At first, an unsupervised classification was performed, which identified as two the number of groups that best represent the data present in our database, reaching 72.81% accuracy in the separation of patients and controls. Then, the supervised classification was performed, where the classifier obtained a 70.97% success rate, being possible to reach 75% accuracy in the best GridSearch configuration when performing a cross validation. Conclusion: It was not yet possible to conclude about the participation of the evaluated polymorphisms in the development of the BL, however the computational techniques used proved to be very promising for carrying out studies of this nature.
Highlights
Burkitt lymphoma belongs to the group of non-Hodgkin lymphomas
Machine Learning techniques have brought new expectations to the medicine field, mainly for the diagnosis and prognosis of diseases. These are extremely promising techniques to assist in the analysis of biomedical data, as they make it possible to extract new insights from data sets that are often previously analyzed or too large to be analyzed by methods that are more conventional
We used a Machine Learning model to verify the participation of Mannose-Binding Lectin (MBL)-2 polymorphisms in the development of Burkitt lymphoma (BL), as well as verifying whether with only the aforementioned parameters, it would be possible to, satisfactorily, classify patients and controls
Summary
Burkitt lymphoma belongs to the group of non-Hodgkin lymphomas. curable in 80% of less advanced stages, it presents in advanced stages in about 75% of cases in Brazil’s Northeast region, requiring urgent and intensive care in the early stages of treatment. Burkitt lymphoma (BL) is a type of non-Hodgkin's lymphoma of mature B cells, malignant and extremely aggressive, presenting the highest cell proliferation rates among all neoplasms, with a doubling time between 24 to 48 hours. It represents almost half of all childhood lymphoma cases, with a higher incidence rate in caucasian and male children (Aydin et al, 2019; Derinkuyu et al, 2016; Swerdlow et al, 2016). Regarding the factors involved in the genesis of BL, genetic-based mechanisms (such as reciprocal translocation involving the MYC proto-oncogene) and the participation of infectious agents (Hsu & Glaser, 2000), mainly by the Epstein-Barr
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