Artificial Intelligence Analysis and Reverse Engineering of Molecular Subtypes of Diffuse Large B-Cell Lymphoma Using Gene Expression Data

Abstract

Diffuse large B-cell lymphoma is one of the most frequent mature B-cell hematological neoplasms and non-Hodgkin lymphomas. Despite advances in diagnosis and treatment, clinical evolution is unfavorable in a subset of patients. Using molecular techniques, several pathogenic models have been proposed, including cell-of-origin molecular classification; Hans’ classification and derivates; and the Schmitz, Chapuy, Lacy, Reddy, and Sha models. This study introduced different machine learning techniques and their classification. Later, several machine learning techniques and artificial neural networks were used to predict the DLBCL subtypes with high accuracy (100–95%), including Germinal center B-cell like (GCB), Activated B-cell like (ABC), Molecular high-grade (MHG), and Unclassified (UNC), in the context of the data released by the REMoDL-B trial. In order of accuracy (MHG vs. others), the techniques were XGBoost tree (100%); random trees (99.9%); random forest (99.5%); and C5, Bayesian network, SVM, logistic regression, KNN algorithm, neural networks, LSVM, discriminant analysis, CHAID, C&R tree, tree-AS, Quest, and XGBoost linear (99.4–91.1%). The inputs (predictors) were all the genes of the array and a set of 28 genes related to DLBCL-Burkitt differential expression. In summary, artificial intelligence (AI) is a useful tool for predictive analytics using gene expression data.

Artificial Intelligence, Lymphoid Neoplasms, and Prediction of MYC, BCL2, and BCL6 Gene Expression Using a Pan-Cancer Panel in Diffuse Large B-Cell Lymphoma

“…AI combines computer science and robust datasets to make predictions and classifications based on input data [77]. Our group has worked in predictive analytics and AI in recent years in the field of lymphoma [78] and other diseases, such as celiac disease [79] and ulcerative colitis [80]. In the lymphoma field, we identified several markers of relevance, such as ENO3 [28], TNFAIP8 [81], PD-L1 [81], CASP8 [82], CSF1R [61], immune response [83], RGS1 [26], FOXP3, PD-1, IL10, and CD163 [29,30,84], as well as BCL6 in DLBCL [85,86] and FL [87].…”

Anomaly Detection and Artificial Intelligence Identified the Pathogenic Role of Apoptosis and RELB Proto-Oncogene, NF-kB Subunit in Diffuse Large B-Cell Lymphoma

“…Finally, a conventional Cox regression for overall survival, backward conditional, was performed using the same set of genes to easily understand the prognostic value of these markers. Table 1 describes the basics of the machine learning and neural network analyses used in this study [58].…”

“…Finally, splits with few contributions to the model are removed. This model can only predict a categorical target [58,62].…”

“…Additional descriptions of machine learning and neural network models are presented in the companion manuscript "Artificial Intelligence Analysis and Reverse Engineering of Molecular Subtypes of Diffuse Large B-Cell Lymphoma Using Gene Expression Data". BioMedInformatics 2024, 4, 295-320. https://doi.org/10.3390/ biomedinformatics4010017 (accessed on 15 February 2024) [58].…”