Current literatures suggest a growing body of evidence highlighting the pivotal role of Immunogenic Cell Death (ICD) in multiple tumor types. Nevertheless, the potential and mechanisms of ICD in diffuse large B-cell lymphoma (DLBCL) remain inadequately studied. To address this gap, our current study aims to examine the impact of ICD on DLBCL and identify a corresponding gene signature in DLBC. Using the expression profiles of ICD-associated genes, the gene expression omnibus (GEO) samples were segregated into ICD-high and ICD-low subtypes utilizing non-negative matrix factorization clustering. Next, univariate and LASSO Cox regression analyses were employed to establish the ICD-related gene signature. Subsequently, the CIBERSORT tool, ssGSEA, and ESTIMATE algorithm were utilized to examine the association between the signature and tumor immune microenvironment of DLBC. Finally, the oncoPredict algorithm was implemented to evaluate the drug sensitivity prediction of DLBCL patients. These findings suggest that the immune microenvironment of the ICD-high group with a poor prognosis was significantly suppressed. An 8-gene ICD-related signature was identified and validated to prognosticate and evaluate the tumor immune microenvironment in DLBCL. Similarly, the high-risk group exhibited a worse prognosis compared to the low-risk group, and the immune function was considerably suppressed. Moreover, the results of oncoPredict algorithm indicated that patients in the high-risk group exhibited higher sensitivity to Cisplatin, Cytarabine, Epirubicin, Oxaliplatin, and Vincristine with low IC50. In conclusion, the present study provides novel insights into the role of ICD in DLBCL by identifying a new biomarker for the disease and may have implications for the development of immune-targeted therapies for the tumor.

目前的文献表明，越来越多的证据强调了免疫原性细胞死亡（ICD）在多种肿瘤类型中的关键作用。然而，ICD 在弥漫性大 B 细胞淋巴瘤 (DLBCL) 中的潜力和机制仍未得到充分研究。为了解决这一差距，我们当前的研究旨在检查 ICD 对 DLBCL 的影响并确定 DLBC 中相应的基因特征。使用 ICD 相关基因的表达谱，利用非负矩阵分解聚类将基因表达综合 (GEO) 样本分为 ICD 高和 ICD 低亚型。接下来，采用单变量和 LASSO Cox 回归分析来建立 ICD 相关基因特征。随后，利用 CIBERSORT 工具、ssGSEA 和 ESTIMATE 算法检查 DLBC 特征与肿瘤免疫微环境之间的关联。最后，实施oncoPredict算法来评估DLBCL患者的药物敏感性预测。这些发现表明，预后不良的ICD高组的免疫微环境受到显着抑制。鉴定并验证了 8 基因 ICD 相关特征，以预测和评估 DLBCL 中的肿瘤免疫微环境。同样，与低风险组相比，高风险组的预后较差，免疫功能受到相当大的抑制。此外，oncoPredict算法的结果表明，高危组患者对顺铂、阿糖胞苷、表阿霉素、奥沙利铂和长春新碱表现出较高的敏感性，且IC50较低。总之，本研究通过鉴定一种新的疾病生物标志物，为 ICD 在 DLBCL 中的作用提供了新的见解，并可能对肿瘤免疫靶向疗法的开发产生影响。