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An Immune-Related Prognostic Classifier Is Associated with Diffuse Large B Cell Lymphoma Microenvironment

机译:免疫相关的预后分类器与弥漫性大B细胞淋巴瘤微环境有关

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Background . Diffuse large B cell lymphoma (DLBCL) is a life-threatening malignant tumor characterized by heterogeneous clinical, phenotypic, and molecular manifestations. Given the association between immunity and tumors, identifying a suitable immune biomarker could improve DLBCL diagnosis. Methods . We systematically searched for DLBCL gene expression microarray datasets from the GEO database. Immune-related genes (IRGs) were obtained from the ImmPort database, and 318 transcription factor (TF) targets in cancer were retrieved from the Cistrome Cancer database. An immune-related classifier for DLBCL prognosis was constructed using Cox regression and LASSO analysis. To assess differences in overall survival between the low- and high-risk groups, we analyzed the tumor microenvironment (TME) and immune infiltration in DLBCL using the ESTIMATE and CIBERSORT algorithms. WGCNA was applied to study the molecular mechanisms explaining the clinical significance of our immune-related classifier and TFs. Results . Eighteen IRGs were selected to construct the classifier. The multi-IRG classifier showed powerful predictive ability. Patients with a high-risk score had poor survival. Based on the AUC for three- and five-year survival, the classifier exhibited better predictive power than clinical data. Discrepancies in overall survival between the low- and high-risk score groups might be explained by differences in immune infiltration, TME, and transcriptional regulation. Conclusions . Our study describes a novel prognostic IRG classifier with strong predictive power in DLBCL. Our findings provide valuable guidance for further analysis of DLBCL pathogenesis and clinical treatment.
机译:背景 。弥漫性大B细胞淋巴瘤(DLBCL)是一种危及生命的恶性肿瘤,其特征是异质临床,表型和分子表现形式。鉴于免疫和肿瘤之间的关联,鉴定合适的免疫生物标志物可以改善DLBCL诊断。方法 。我们系统地搜索了来自Geo数据库的DLBCL基因表达式微阵列数据集。免疫相关基因(IRGS)从Immport数据库获得,并从动脉癌癌数据库中检索癌症中的318个转录因子(TF)靶标。使用COX回归和套索分析构建免疫相关分类剂的DLBCL预后。为了评估低风险群体之间整体生存的差异,我们分析了使用估计和cibersort算法的DLBCL中的肿瘤微环境(TME)和免疫浸润。应用WGCNA研究分子机制解释我们免疫相关分类器和TFS的临床意义。结果 。选择了十八个Irgs来构建分类器。多IRG分类器表现出强大的预测能力。高风险评分的患者存活差。基于AUC的三年和五年生存,分类器表现出比临床数据更好的预测力。低风险分数群之间的整体存活的差异可能会通过免疫浸润,TME和转录调节的差异来解释。结论。我们的研究描述了一种新型预测IRG分类器,具有在DLBCL中具有强大预测力的预测力。我们的研究结果为进一步分析DLBCL发病机制和临床治疗提供了有价值的指导。

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