Artificial Intelligence Analysis of the Gene Expression of Follicular Lymphoma Predicted the Overall Survival and Correlated with the Immune Microenvironment Response Signatures

Abstract

Follicular lymphoma (FL) is the second most common lymphoma in Western countries. FL is characterized by being incurable, usually having an indolent clinical course with frequent relapses, and an eventual patient’s death or transformation to Diffuse Large B-cell Lymphoma. The immune response and the tumoral immune microenvironment, including FOXP3+Tregs, PD-1+TFH cells, TNFRSF14 (HVEM), and BTLA play a role in the pathogenesis. We aimed to analyze the gene expression of FL by Artificial Intelligence (machine learning, deep learning), to identify genes associated with the prognosis of the patients and with the microenvironment in terms of overall survival (OS). A series of 184 cases of the GSE16131 dataset was analyzed by multilayer perceptron (MLP) and radial basis function (RBF) neural networks. In the analysis, MLP and RBF had a synergistic effect. From an initial set of 22,215 genes probes, a final set of 43 genes was highlighted. These 43 genes predicted the OS and correlated with the immune microenvironment: in a multivariate Cox analysis, 18 genes were associated with a poor prognosis (namely, MED8, KRT19, CDC40, SLC24A2, PRB1, KIAA0100, EVA1B, KLK10, TMEM70, BTN2A3P, TRPM4, MED6, FRYL, CBFA2T2, RANBP9, BNIP2, PTP4A2 and ALDH1L1) and 25 genes were associated with a good prognosis of the patients. Gene set enrichment analysis (GSEA) confirmed these findings and showed a typical sinusoidal-like shape. Some of the most relevant genes for poor OS were EVA1B, KRT19, BTN2A3P, KLK10, TRPM4, TMEM70, and SLC24A2 (hazard risk = from 1.7 to 4.3, p < 0.005) and for good OS, these were TDRD12 and ZNF230 (HR = 0.34 and 0.28, p < 0.001). EVA1B, KRT19, BTN2AP3, KLK10, and TRPM4 also associated with M2-like macrophage markers including CD163, MRC1 (CD206), and IL10 in the core enrichment for dead OS outcome by GSEA and to poor OS by Kaplan–Meier with Log rank test. The scientific literature showed that some of these genes also play a role in other types of cancer. In conclusion, by Artificial Intelligence, we have identified new biomarkers with prognostic relevance in FL.

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

“…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].…”

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

“…Several software were used in this research according to the manufacturer's instructions: R software for statistical computing version 3.6. 2a-d) and Radial Basis Function analysis using the immunohistochemical data was performed following the manufacturer's instructions, and as we have thoroughly described in our previous publications [39,40]. In this neural network analysis, the prediction of Caspase-8 by the other related markers of the pathway was performed using the immunohistochemical data of Caspase-8 as a dichotomic variable (high vs. low, with the same cut-off of the overall survival).…”

Integrative Statistics, Machine Learning and Artificial Intelligence Neural Network Analysis Correlated CSF1R with the Prognosis of Diffuse Large B-Cell Lymphoma

“…Then, the genes associated with the high or low CSF1R groups were searched using the multilayer perceptron analysis. The multilayer perceptron analysis was performed as thoroughly described in our recent publications [26][27][28]. First, the 20,683 genes were ranked according to their normalized importance for their association with the high or low CSF1R expression groups.…”