Identification of immune-related genes and development of a prognostic model in mantle cell lymphoma.

ABSTRACT:

Background

The immune landscape, prognostic model, and molecular variations of mantle cell lymphoma (MCL) remain unclear. Hence, an integrated bioinformatics analysis of MCL datasets is required for the development of immunotherapy and the optimization of targeted therapies.

Methods

Data were obtained from the Gene Expression Omnibus (GEO) database (GSE32018, GSE45717 and GSE93291). The differentially expressed immune-related genes were selected, and the hub genes were screened by three machine learning algorithms, followed by enrichment and correlation analyses. Next, MCL molecular clusters based on the hub genes were identified by K-Means clustering, the probably approximately correct (PAC) algorithm, and principal component analysis (PCA). The landscape of immune cell inf

SUBMITTER: Zhang W 

PROVIDER: S-EPMC9843426 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature