Zhang Jiayi, Jia Zhixiang, Zhang Jiahui, Mu Xiaohui, Ai Limei
Department of Hematology, The First Affiliated Hospital of Jinzhou Medical University, Jinzhou, China.
Medical College, Sanmenxia Vocational and Technical College, Sanmenxia, China.
Biol Direct. 2025 Apr 29;20(1):58. doi: 10.1186/s13062-025-00649-4.
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.
M2巨噬细胞在包括弥漫性大B细胞淋巴瘤(DLBCL)在内的各种肿瘤的发生和发展中起着关键作用。然而,DLBCL中M2巨噬细胞相关基因的特征仍不完整。在本研究中,我们从基因表达综合数据库(GEO)下载了与DLBCL相关的数据集,并确定了对照组和治疗组之间的77个差异表达基因(DEG)。我们使用CIBERSORT分析评估免疫细胞浸润,并通过加权基因共表达网络分析(WGCNA)确定与M2巨噬细胞相关的模块。使用最小绝对收缩和选择算子(LASSO)、支持向量机递归特征消除(SVM-RFE)和随机森林(RF)算法,我们筛选出了七个具有强大诊断能力的潜在诊断生物标志物:SMAD3、IL7R、IL18、FAS、CD5、CCR7和CSF1R。随后,构建的逻辑回归模型和列线图显示出强大的预测性能。我们进一步研究了这些生物标志物的表达水平、预后价值和生物学功能。结果表明,SMAD3、IL7R、IL18、FAS和CD5与DLBCL患者的生存相关,可作为预测DLBCL预后的标志物。我们的研究引入了一种新的诊断策略,并为DLBCL潜在机制提供了新的见解。然而,在临床应用之前,有必要进一步验证这些基因在DLBCL诊断中的实际价值。
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