Xiang Chenxi, Ni Huimin, Wang Zhina, Ji Binbin, Wang Bo, Shi Xiaoli, Wu Wanna, Liu Nian, Gu Ying, Ma Dongshen, Liu Hui
Department of Pathology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China.
Department of Pathology, Xuzhou Medical University, Xuzhou, China.
Front Genet. 2021 Oct 14;12:756784. doi: 10.3389/fgene.2021.756784. eCollection 2021.
Over 50% of diffuse large B-cell lymphoma (DLBCL) patients are diagnosed at an advanced stage. Although there are a few therapeutic strategies for DLBCL, most of them are more effective in limited-stage cancer patients. The prognosis of patients with advanced-stage DLBCL is usually poor with frequent recurrence and metastasis. In this study, we aimed to identify gene expression and network differences between limited- and advanced-stage DLBCL patients, with the goal of identifying potential agents that could be used to relieve the severity of DLBCL. Specifically, RNA sequencing data of DLBCL patients at different clinical stages were collected from the cancer genome atlas (TCGA). Differentially expressed genes were identified using DESeq2, and then, weighted gene correlation network analysis (WGCNA) and differential module analysis were performed to find variations between different stages. In addition, important genes were extracted by key driver analysis, and potential agents for DLBCL were identified according to gene-expression perturbations and the Crowd Extracted Expression of Differential Signatures (CREEDS) drug signature database. As a result, 20 up-regulated and 73 down-regulated genes were identified and 79 gene co-expression modules were found using WGCNA, among which, the thistle1 module was highly related to the clinical stage of DLBCL. KEGG pathway and GO enrichment analyses of genes in the thistle1 module indicated that DLBCL progression was mainly related to the NOD-like receptor signaling pathway, neutrophil activation, secretory granule membrane, and carboxylic acid binding. A total of 47 key drivers were identified through key driver analysis with 11 up-regulated key driver genes and 36 down-regulated key diver genes in advanced-stage DLBCL patients. Five genes (, , , and ) appeared as hub genes, being closely related to the occurrence and development of DLBCL. Finally, both differentially expressed genes and key driver genes were subjected to CREEDS analysis, and 10 potential agents were predicted to have the potential for application in advanced-stage DLBCL patients. In conclusion, we propose a novel pipeline to utilize perturbed gene-expression signatures during DLBCL progression for identifying agents, and we successfully utilized this approach to generate a list of promising compounds.
超过50%的弥漫性大B细胞淋巴瘤（DLBCL）患者在晚期被诊断出来。尽管针对DLBCL有一些治疗策略，但其中大多数在局限性癌症患者中更有效。晚期DLBCL患者的预后通常较差，频繁复发和转移。在本研究中，我们旨在确定局限性和晚期DLBCL患者之间的基因表达和网络差异，以识别可用于缓解DLBCL严重程度的潜在药物。具体而言，从癌症基因组图谱（TCGA）收集不同临床阶段DLBCL患者的RNA测序数据。使用DESeq2鉴定差异表达基因，然后进行加权基因共表达网络分析（WGCNA）和差异模块分析以发现不同阶段之间的差异。此外，通过关键驱动因素分析提取重要基因，并根据基因表达扰动和群体提取的差异特征表达（CREEDS）药物特征数据库识别DLBCL的潜在药物。结果，鉴定出20个上调基因和73个下调基因，并使用WGCNA发现79个基因共表达模块，其中，蓟1模块与DLBCL的临床阶段高度相关。对蓟1模块中的基因进行KEGG通路和GO富集分析表明，DLBCL进展主要与NOD样受体信号通路、中性粒细胞活化、分泌颗粒膜和羧酸结合有关。通过关键驱动因素分析共鉴定出47个关键驱动因素，其中晚期DLBCL患者中有11个上调的关键驱动基因和36个下调的关键驱动基因。五个基因（、、、和）作为枢纽基因出现，与DLBCL的发生和发展密切相关。最后，对差异表达基因和关键驱动基因均进行CREEDS分析，预测有10种潜在药物有应用于晚期DLBCL患者的潜力。总之，我们提出了一种新颖的流程，利用DLBCL进展过程中受扰动的基因表达特征来识别药物，并且我们成功地利用这种方法生成了一份有前景的化合物清单。
