Xu Yan-Feng, Wang Guan-Yun, Zhang Ming-Yu, Yang Ji-Gang
Department of Nuclear Medicine, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China.
World J Clin Oncol. 2023 Oct 24;14(10):357-372. doi: 10.5306/wjco.v14.i10.357.
Burkitt lymphoma (BL) is an exceptionally aggressive malignant neoplasm that arises from either the germinal center or post-germinal center B cells. Patients with BL often present with rapid tumor growth and require high-intensity multi-drug therapy combined with adequate intrathecal chemotherapy prophylaxis, however, a standard treatment program for BL has not yet been established. It is important to identify biomarkers for predicting the prognosis of BLs and discriminating patients who might benefit from the therapy. Microarray data and sequencing information from public databases could offer opportunities for the discovery of new diagnostic or therapeutic targets.
To identify hub genes and perform gene ontology (GO) and survival analysis in BL.
Gene expression profiles and clinical traits of BL patients were collected from the Gene Expression Omnibus database. Weighted gene co-expression network analysis (WGCNA) was applied to construct gene co-expression modules, and the cytoHubba tool was used to find the hub genes. Then, the hub genes were analyzed using GO and Kyoto Encyclopedia of Genes and Genomes analysis. Additionally, a Protein-Protein Interaction network and a Genetic Interaction network were constructed. Prognostic candidate genes were identified through overall survival analysis. Finally, a nomogram was established to assess the predictive value of hub genes, and drug-gene interactions were also constructed.
In this study, we obtained 8 modules through WGCNA analysis, and there was a significant correlation between the yellow module and age. Then we identified 10 hub genes (, , , , , , , , and ) by cytoHubba tool. Within these hubs, two genes were found to be associated with OS (, = 0.029 and , = 0.0066) by survival analysis. Additionally, we combined these two hub genes and age to build a nomogram. Moreover, the drugs related to and might have a potential therapeutic role in relapsed and refractory BL.
From WGCNA and survival analysis, we identified and that might be prognostic markers for BL.
伯基特淋巴瘤（BL）是一种异常侵袭性的恶性肿瘤，起源于生发中心或生发中心后B细胞。BL患者通常表现为肿瘤快速生长，需要高强度的多药联合化疗并辅以充分的鞘内化疗预防，但尚未建立BL的标准治疗方案。识别用于预测BL预后以及区分可能从治疗中获益的患者的生物标志物非常重要。来自公共数据库的微阵列数据和测序信息可为发现新的诊断或治疗靶点提供机会。
识别BL中的核心基因并进行基因本体（GO）和生存分析。
从基因表达综合数据库收集BL患者的基因表达谱和临床特征。应用加权基因共表达网络分析（WGCNA）构建基因共表达模块，并使用cytoHubba工具查找核心基因。然后，使用GO和京都基因与基因组百科全书分析对核心基因进行分析。此外，构建了蛋白质-蛋白质相互作用网络和遗传相互作用网络。通过总生存分析确定预后候选基因。最后，建立列线图以评估核心基因的预测价值，并构建药物-基因相互作用。
在本研究中，我们通过WGCNA分析获得了8个模块，黄色模块与年龄之间存在显著相关性。然后我们通过cytoHubba工具识别了10个核心基因（、、、、、、、、和）。在这些核心基因中，通过生存分析发现两个基因与总生存期相关（，=0.029和，=0.0066）。此外，我们将这两个核心基因与年龄相结合构建了列线图。而且，与和相关的药物可能对复发和难治性BL具有潜在治疗作用。
通过WGCNA和生存分析，我们识别出和可能是BL的预后标志物。
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