High Expression of Caspase-8 Associated with Improved Survival in Diffuse Large B-Cell Lymphoma: Machine Learning and Artificial Neural Networks Analyses

Abstract

High expression of the anti-apoptotic TNFAIP8 is associated with poor survival of the patients with diffuse large B-cell lymphoma (DLBCL), and one of the functions of TNFAIP8 is to inhibit the pro-apoptosis Caspase-8. We aimed to analyze the immunohistochemical expression of Caspase-8 (active subunit p18; CASP8) in a series of 97 cases of DLBCL from Tokai University Hospital, and to correlate with other Caspase-8 pathway-related markers, including cleaved Caspase-3, cleaved PARP, BCL2, TP53, MDM2, MYC, Ki67, E2F1, CDK6, MYB and LMO2. After digital image quantification, the correlation with several clinicopathological characteristics of the patients showed that high protein expression of Caspase-8 was associated with a favorable overall and progression-free survival (Hazard Risks = 0.3; p = 0.005 and 0.03, respectively). Caspase-8 also positively correlated with cCASP3, MDM2, E2F1, TNFAIP8, BCL2 and Ki67. Next, the Caspase-8 protein expression was modeled using predictive analytics, and a high overall predictive accuracy (>80%) was obtained with CHAID decision tree, Bayesian network, discriminant analysis, C5 tree, logistic regression, and Artificial Intelligence Neural Network methods (both Multilayer perceptron and Radial basis function); the most relevant markers were cCASP3, E2F1, TP53, cPARP, MDM2, BCL2 and TNFAIP8. Finally, the CASP8 gene expression was also successfully modeled in an independent DLBCL series of 414 cases from the Lymphoma/Leukemia Molecular Profiling Project (LLMPP). In conclusion, high protein expression of Caspase-8 is associated with a favorable prognosis of DLBCL. Predictive modeling is a feasible analytic strategy that results in a solution that can be understood (i.e., explainable artificial intelligence, “white-box” algorithms).

Artificial Neural Network Analysis of Gene Expression Data Predicted Non-Hodgkin Lymphoma Subtypes with High Accuracy

“…Figure 5 also showed a nonlinear prediction of FL vs. others using a Bayesian network; this graphical model displays variables (nodes) in a data set and the probabilistic, or conditional, independencies between them. Causal relationships between nodes (genes) can be represented by a Bayesian network; however, the links in the network (arcs) do not necessarily represent direct cause and effect [5,6,17,21,23]. In the Bayesian network, ITGA1 is located more centrally, with a connection to CXCR2 that connects to FGF1, PPARGC1A, MAGEC1, and ICAM4.…”

“…The analysis was performed to predict all subtypes simultaneously and then each subtype against the others. The multilayer perceptron was performed as we have recently described [17,[19][20][21][22][23].…”

“…Of note, other available rescaling methods were normalized [(x − min)/(max − min)], adjusted normalized [[2 * (x − min)/(max − min)] − 1] or none. Random assignation of the cases based on the relative number of cases (training 70%, testing 30%, and holdout 0%) partitioned the data set into training, testing, and holdout samples [5,6,17,[19][20][21][22][23].…”

“…The optimization algorithm was the scaled conjugate gradient or gradient descent. The options of the training set, initial lambda (0.0000005), initial sigma (0.00005), interval center (0), and interval offset (±0.5) [5,6,17,[19][20][21][22][23].…”

“…As for stopping rules, the maximum step without a decrease in error as set to 1, the maximum training time to 15 min, the minimum relative change in training error to 0.0001, and the minimum relative change in the training error ratio to 0.001. Missing values were excluded, if present [5,6,17,[19][20][21][22][23].…”