Artificial Intelligence Analysis of Gene Expression Data Predicted the Prognosis of Patients with Diffuse Large B-Cell Lymphoma.

We aimed to identify new biomarkers in Diffuse Large B-cell Lymphoma (DLBCL) using the deep learning technique.The multilayer perceptron (MLP) analysis was performed in the GSE10846 series, divided into discovery (n = 100) and validation (n = 414) sets. The top 25 gene-probes from a total of 54,614 were selected based on their normalized importance for outcome prediction (dead/alive). By Gene Set Enrichment Analysis (GSEA) the association to unfavorable prognosis was confirmed. In the validation set, by univariate Cox regression analysis, high expression of , , , , , , , , , , and associated to poor, and high , , and to favorable outcome. A multivariate analysis confirmed , and as risk factors and and as protective factors. Using a risk score formula, the 25 genes identified two groups of patients with different survival that was independent to the cell-of-origin molecular classification (5-year OS, low vs. high risk): 65% vs. 24%, respectively (Hazard Risk = 3.2, P < 0.000001). Finally, correlation with known DLBCL markers showed that high expression of all , and associated to the worst outcome.By artificial intelligence we identified a set of genes with prognostic relevance.