Machine learning-based classification of diffuse large B-cell lymphoma patients by eight gene expression profiles.

ABSTRACT: Gene expression profiling (GEP) had divided the diffuse large B-cell lymphoma (DLBCL) into molecular subgroups: germinal center B-cell like (GCB), activated B-cell like (ABC), and unclassified (UC) subtype. However, this classification with prognostic significance was not applied into clinical practice since there were more than 1000 genes to detect and interpreting was difficult. To classify cancer samples validly, eight significant genes (MYBL1, LMO2, BCL6, MME, IRF4, NFKBIZ, PDE4B, and SLA) were selected in 414 patients treated with CHOP/R-CHOP chemotherapy from Gene Expression Omnibus (GEO) data sets. Cutoffs for each gene were obtained using receiver-operating characteristic curves (ROC) new model based on the support vector machine (SVM) estimated the probability of membership into o

SUBMITTER: Zhao S 

PROVIDER: S-EPMC4864813 | biostudies-literature | 2016 May

REPOSITORIES: biostudies-literature