Identification of M2 macrophage-related genes associated with diffuse large B-cell lymphoma via bioinformatics and machine learning approaches.

Abstract

M2 macrophages play a crucial role in the initiation and progression of various tumors, including diffuse large B-cell lymphoma (DLBCL). However, the characterization of M2 macrophage-related genes in DLBCL remains incomplete. In this study, we downloaded DLBCL-related datasets from the Gene Expression Omnibus (GEO) database and identified 77 differentially expressed genes (DEGs) between the control group and the treat group. We assessed the immune cell infiltration using CIBERSORT analysis and identified modules associated with M2 macrophages through weighted gene co-expression network analysis (WGCNA). Using the Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Machine Recursive Feature Elimination (SVM-RFE), and Random Forest (RF) algorithms, we screened for seven potential diagnostic biomarkers with strong diagnostic capabilities: SMAD3, IL7R, IL18, FAS, CD5, CCR7, and CSF1R. Subsequently, the constructed logistic regression model and nomogram demonstrated robust predictive performance. We further investigated the expression levels, prognostic values, and biological functions of these biomarkers. The results showed that SMAD3, IL7R, IL18, FAS and CD5 were associated with the survival of DLBCL patients and could be used as markers to predict the prognosis of DLBCL. Our study introduces a novel diagnostic strategy and provides new insights into the potential mechanisms underlying DLBCL. However, further validation of the practical value of these genes in DLBCL diagnosis is warranted before clinical application.