Xiao Wei, Yu Kuai, Deng Xuefei, Zeng Yunxin
Department of Hematology, The Seventh Affiliated Hospital, Sun Yat-sen University, No. 628 Zhenyuan Road, Guangming District, Shenzhen 518107, Guangdong Province, China.
Department of Blood Transfusion, The First Affiliated Hospital of Nanchang University, No. 17 Yongwaizheng Street, Nanchang 330209, Jiangxi Province, China; Key Laboratory of Jiangxi Province for Transfusion Medicine, The First Affiliated Hospital of Nanchang University, No. 17 Yongwaizheng Street, Nanchang 330209, Jiangxi Province, China.
Cytokine. 2024 Oct;182:156726. doi: 10.1016/j.cyto.2024.156726. Epub 2024 Aug 7.
NK cells are essential for the detection, identification and prediction of cancer. However, so far, there is no prognostic risk model based on NK cell-related genes to predict the prognosis and treatment outcome of DLBCL patients. This study aimed to explore a risk assessment model that could accurately predict the prognosis and treatment efficacy of DLBCL.
Bioinformatics analysis of the expression profiles of DLBCL samples in the GEO database was performed. Cox regression and LASSO regression analysis were used to determine NK cell-related genes associated with patient's prognosis. Based on these genes, a risk assessment model was constructed to predict the prognosis of patients and the effectiveness of treatment. Finally, qRT-PCR was used to verify the expression of gene tags in clinical samples.
We identified seven prognosis-related NK cell-related genes (MAP2K1, PRKCB, TNFRSF10B, IL18, LAMP1, RASGRP1, and SP110), and DLBCL patients were divided into low- and high-risk groups based on these genes. Survival analysis showed that the prognosis of patients with low-risk group was better. Pathway enrichment analysis showed that the differentially expressed genes between the two risk groups were related to immune response pathways. Compared with the high-risk group, the low-risk group had higher infiltration of immune cells in tumor tissues. Besides, compared with high-risk group, low-risk patients by immunotherapy or other commonly used anti-tumor drugs might have better efficacy after treatment. In addition, qRT-PCR showed that the expression of risk genes including TNFRSF10B, IL18 and LAMP1 were significantly increased in most DLBCL samples compared to control samples, while the expression of protective genes including MAP2K1, PRKCB, RASGRP1 and SP110 were significantly decreased.
The NK cell-related gene signatures were proved to be a reliable indicator of the success of immunotherapy in patients with DLBCL, thus providing a unique evaluation method.
自然杀伤（NK）细胞对于癌症的检测、识别和预测至关重要。然而，迄今为止，尚无基于NK细胞相关基因的预后风险模型来预测弥漫性大B细胞淋巴瘤（DLBCL）患者的预后和治疗结果。本研究旨在探索一种能够准确预测DLBCL患者预后和治疗疗效的风险评估模型。
对基因表达综合数据库（GEO数据库）中DLBCL样本的表达谱进行生物信息学分析。采用Cox回归和套索回归分析来确定与患者预后相关的NK细胞相关基因。基于这些基因，构建一个风险评估模型以预测患者的预后和治疗效果。最后，采用定量逆转录聚合酶链反应（qRT-PCR）来验证临床样本中基因标签的表达。
我们鉴定出7个与预后相关的NK细胞相关基因（丝裂原活化蛋白激酶激酶1（MAP2K1）、蛋白激酶Cβ（PRKCB）、肿瘤坏死因子受体超家族成员10B（TNFRSF10B）、白细胞介素18（IL18）、溶酶体相关膜蛋白1（LAMP1）、RAS鸟苷酸释放蛋白1（RASGRP1）和SP110），并根据这些基因将DLBCL患者分为低风险组和高风险组。生存分析表明，低风险组患者的预后较好。通路富集分析表明，两个风险组之间的差异表达基因与免疫反应通路相关。与高风险组相比，低风险组肿瘤组织中免疫细胞的浸润更高。此外，与高风险组相比，接受免疫治疗或其他常用抗肿瘤药物治疗的低风险患者治疗后可能具有更好的疗效。另外，qRT-PCR显示，与对照样本相比，大多数DLBCL样本中包括TNFRSF10B、IL18和LAMP1在内的风险基因的表达显著增加，而包括MAP2K1、PRKCB、RASGRP1和SP110在内的保护基因的表达显著降低。
NK细胞相关基因特征被证明是DLBCL患者免疫治疗成功的可靠指标，从而提供了一种独特的评估方法。
