Identification of FCER1G related to Activated Memory CD4+ T Cells Infiltration by Gene Co-expression Network and Construction of a Risk Prediction Module in Diffuse Large B-Cell Lymphoma

Abstract

Diffuse large B cell lymphoma (DLBCL) is a group of biologically heterogeneous tumors with different prognoses. The tumor microenvironment plays a vital role in the tumorigenesis and development of DLBCL, and activated memory CD4⁺ T cells are an essential component of immunological cells in the lymphoma microenvironment. So far, there are few reports about activated memory CD4+T cells infiltration and related genes in the DLBCL tumor microenvironment. This study obtained the mRNA expression profile information of the testing GSE87371 dataset and another six validation datasets (GSE53786, GSE181063, GSE10846, GSE32918, GSE32018, GSE9327, GSE3892, TCGA-DLBC) from the GEO and TCGA databases. Weighted Gene Co-expression Network Analysis (WGCNA) screened gene module associated with activated memory CD4⁺ T cells infiltration. CIBERSORT and TIMER (immune cells infiltrating estimation analysis tools) were used to identify the relationship between activated memory CD4⁺ T cells and genes associated with immune infiltrating cells in the tumor microenvironment. The least absolute shrinkage and selection operator (LASSO) built the risk prediction model and verified it using nomogram and Kaplan-Meier analysis. Further functional characterization includes Gene Ontology, KEGG pathway analysis and Gene Set Enrichment Analysis (GSEA) to investigate the role and underlying mechanisms of these genes. These results suggest that the expression of FCER1G can reflect the invasion of activated memory CD4⁺ T cells in DLBCL, which provides a new idea for studying the tumor microenvironment and may become a potential predictive biomarker for the assessment of DLBCL.

Keywords: FCER1G; diffuse large B-cell lymphoma; memory activated CD4+ T cells; prognosis biomarkers; weighted gene co-expression network analysis.