Pan Yu-Biao, Wang Wei, Cai Hong-Kai, Zhang Jia, Teng Ya, Xue Jiji, Zhu Min, Luo Wen-Da
Taizhou Hospital of Zhejiang Province, Zhejiang University, Hangzhoua, China.
Department of Hematology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai, China.
Front Genet. 2022 Aug 11;13:911443. doi: 10.3389/fgene.2022.911443. eCollection 2022.
Diffuse large B-cell lymphoma (DLBCL), which is considered to be the most common subtype of lymphoma, is an aggressive tumor. Necroptosis, a novel type of programmed cell death, plays a bidirectional role in tumors and participates in the tumor microenvironment to influence tumor development. Targeting necroptosis is an intriguing direction, whereas its role in DLBCL needs to be further discussed. We obtained 17 DLBCL-associated necroptosis-related genes by univariate cox regression screening. We clustered in GSE31312 depending on their expressions of these 17 genes and analyzed the differences in clinical characteristics between different clusters. To investigate the differences in prognosis across distinct clusters, the Kaplan-Meier method was utilized. The variations in the tumor immune microenvironment (TME) between distinct necroptosis-related clusters were investigated via "ESTIMATE", "Cibersort" and single-sample geneset enrichment analysis (ssGSEA). Finally, we constructed a 6-gene prognostic model by lasso-cox regression and subsequently integrated clinical features to construct a prognostic nomogram. Our analysis indicated stable but distinct mechanism of action of necroptosis in DLBCL. Based on necroptosis-related genes and cluster-associated genes, we identified three groups of patients with significant differences in prognosis, TME, and chemotherapy drug sensitivity. Analysis of immune infiltration in the TME showed that cluster 1, which displayed the best prognosis, was significantly infiltrated by natural killer T cells, dendritic cells, CD8 T cells, and M1 macrophages. Cluster 3 presented M2 macrophage infiltration and the worst prognosis. Importantly, the prognostic model successfully differentiated high-risk from low-risk patients, and could forecast the survival of DLBCL patients. And the constructed nomogram demonstrated a remarkable capacity to forecast the survival time of DLBCL patients after incorporating predictive clinical characteristics. The different patterns of necroptosis explain its role in regulating the immune microenvironment of DLBCL and the response to R-CHOP treatment. Systematic assessment of necroptosis patterns in patients with DLBCL will help us understand the characteristics of tumor microenvironment cell infiltration and aid in the development of tailored therapy regimens.
弥漫性大B细胞淋巴瘤(DLBCL)被认为是淋巴瘤最常见的亚型,是一种侵袭性肿瘤。坏死性凋亡是一种新型的程序性细胞死亡,在肿瘤中发挥双向作用,并参与肿瘤微环境以影响肿瘤发展。靶向坏死性凋亡是一个有趣的方向,但其在DLBCL中的作用仍需进一步探讨。我们通过单变量cox回归筛选获得了17个与DLBCL相关的坏死性凋亡相关基因。我们根据这17个基因的表达在GSE31312中进行聚类,并分析不同聚类之间临床特征的差异。为了研究不同聚类之间预后的差异,采用了Kaplan-Meier方法。通过“ESTIMATE”、“Cibersort”和单样本基因集富集分析(ssGSEA)研究了不同坏死性凋亡相关聚类之间肿瘤免疫微环境(TME)的差异。最后,我们通过lasso-cox回归构建了一个6基因预后模型,随后整合临床特征构建了一个预后列线图。我们的分析表明坏死性凋亡在DLBCL中具有稳定但独特的作用机制。基于坏死性凋亡相关基因和聚类相关基因,我们确定了三组患者,他们在预后、TME和化疗药物敏感性方面存在显著差异。对TME中免疫浸润的分析表明,预后最好的聚类1被自然杀伤T细胞、树突状细胞、CD8 T细胞和M1巨噬细胞显著浸润。聚类3呈现M2巨噬细胞浸润且预后最差。重要的是,该预后模型成功区分了高危和低危患者,并可以预测DLBCL患者的生存情况。并且构建的列线图在纳入预测性临床特征后显示出预测DLBCL患者生存时间的显著能力。坏死性凋亡的不同模式解释了其在调节DLBCL免疫微环境和对R-CHOP治疗反应中的作用。对DLBCL患者坏死性凋亡模式的系统评估将有助于我们了解肿瘤微环境细胞浸润的特征,并有助于制定个性化的治疗方案。