Improved personalized survival prediction of patients with diffuse large B-cell Lymphoma using gene expression profiling.

ABSTRACT:

Background

Thirty to forty percent of patients with Diffuse Large B-cell Lymphoma (DLBCL) have an adverse clinical evolution. The increased understanding of DLBCL biology has shed light on the clinical evolution of this pathology, leading to the discovery of prognostic factors based on gene expression data, genomic rearrangements and mutational subgroups. Nevertheless, additional efforts are needed in order to enable survival predictions at the patient level. In this study we investigated new machine learning-based models of survival using transcriptomic and clinical data.

Methods

Gene expression profiling (GEP) of in 2 different publicly available retrospective DLBCL cohorts were analyzed. Cox regression and unsupervised clustering were performed in order to identify probes

SUBMITTER: Mosquera Orgueira A 

PROVIDER: S-EPMC7579992 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature