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 infiltration and immune checkpoint molecules in distinct clusters was explored by single-sample gene-set enrichment analysis (ssGSEA) as well as the CIBERSORT and xCell algorithms. The prognostic genes and prognostic risk score model for MCL clusters were identified by least absolute shrinkage and selection operator (LASSO)-Cox analysis and cross-validation for lambda. Correlation analysis was performed to explore the correlation between the screened prognostic genes and immune cells or immune checkpoint molecules.Results: Four immune-related hub genes (CD247, CD3E, CD4, and GATA3) were screened in MCL, mainly enriched in the T-cell receptor signaling pathway. Based on the hub genes, two MCL molecular clusters were recognized. The cluster 2 group had a significantly worse overall survival (OS), with downregulated hub genes, and a variety of activated immune effector cells declined. The majority of immune checkpoint molecules had also decreased. An efficient prognostic model was established, including six prognostic genes (LGALS2, LAMP3, ICOS, FCAMR, IGFBP4, and C1QA) differentially expressed between two MCL clusters. Patients with a higher risk score in the prognostic model had a poor prognosis.Furthermore, most types of immune cells and a range of immune checkpoint molecules were positively correlated with the prognostic genes.Conclusions: Our study identified distinct molecular clusters based on the immune-related hub genes, and showed that the prognostic model affected the prognosis of MCL patients. These hub genes, modulated immune cells, and immune checkpoint molecules might be involved in oncogenesis and could be potential prognostic biomarkers in MCL.

Establishment and validation of a prognostic signature for pancreatic ductal adenocarcinoma based on lactate metabolism-related genes

“…Patients who had a follow-up time no more than 30 days or had no clinical overall survival (OS) data were excluded, then Cox regression analysis was performed to identify the LMRGs that have potential prognostic value. Additionally, LASSO Cox regression (iteration = 100) model was utilized with the "glmnet" package to prevent overfitting, a low mean-squared error were obtained via lasso.min (Zhang W. et al, 2022a). Finally, the selected LMRGs were subjected to multivariate Cox Frontiers in Molecular Biosciences frontiersin.org regression analysis to determine the subset of genes that comprised the LMRGs signature.…”