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.
Highlights
Predictive neural networks are usually the chosen tool for many data-mining applications because they can handle complex processes, such as the analysis of gene expression data of human pathological conditions
The training time was higher in the radial basis function (RBF) analysis: multilayer perceptron (MLP) vs. RBF, training time in seconds, 11.9 ± 20.9 vs. 384.3 ± 168.0% (p = 4.871 × 10−4), respectively
From an initial set of 22,215 gene probes, after several neural network analyses, the threshold for the 70% normalized importance and 1% averaged normalized importance, the pooling and deletion of duplicates, we finished with a set of 1005 genes
Summary
Predictive neural networks are usually the chosen tool for many data-mining applications because they can handle complex processes, such as the analysis of gene expression data of human pathological conditions. The 2016 revision of the World Health Organization classification of lymphoid neoplasms classifies the entity of follicular lymphoma into the Mature B-cell neoplasms (as a part of the non-Hodgkin lymphomas), which includes other relevant subtypes such as the chronic lymphocytic leukemia/small lymphocytic lymphoma, splenic marginal zone lymphoma, plasma cell myeloma, extranodal marginal zone lymphoma of mucosa-associated lymphoid tissue (MALT lymphoma), mantle cell lymphoma, and diffuse large b-cell lymphoma, among others [6]. All these tumors are originated from B-lymphocytes in a defined stage of B-cell maturation and differentiation. It is clinically relevant [6,7]
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