Gao Hongyu, Wu Bin, Jin Hong, Yang Wei
Department of Hematology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, P.R. China.
Department of Pathogen Biology, China Medical University, Shenyang, Liaoning, P.R. China.
J Biochem Mol Toxicol. 2021 Jun;35(6):1-12. doi: 10.1002/jbt.22768. Epub 2021 Mar 12.
Diffuse large B cell lymphoma (DLBCL) comprises distinct entities due to its heterogeneity. The currently used international prognostic index for DLBCL prognosis prediction is only based on clinical factors and cannot reflect the molecular mechanisms underlying its progression. Here, we aimed to establish a long noncoding RNAs (lncRNA)-based signature for DLBCL prognosis prediction. The data were retrieved from the Gene Expression Omnibus and The Cancer Genome Atlas database. After identifying the differentially expressed lncRNAs (DELs), univariate COX regression, LASSO regression, and stepwise regression analysis were performed to construct a 6-lncRNA risk score system. Kaplan-Meier survival presented that the high-risk group had a significantly poorer overall survival. Based on the risk score and clinical characters, a nomogram was established, which had better predictive accuracy than each factor alone. Finally, weighted gene co-expression network analysis showed that these lncRNAs might regulate immune response, metabolism process, and signal transduction to influence the outcome. Conclusively, our model and nomogram could be reliable prognostic tools for DLBCL patients.
弥漫性大B细胞淋巴瘤(DLBCL)因其异质性而包含不同的实体。目前用于预测DLBCL预后的国际预后指数仅基于临床因素,无法反映其进展的分子机制。在此,我们旨在建立一种基于长链非编码RNA(lncRNA)的特征用于预测DLBCL的预后。数据从基因表达综合数据库和癌症基因组图谱数据库中获取。在鉴定出差异表达的lncRNA(DEL)后,进行单变量COX回归、LASSO回归和逐步回归分析以构建一个包含6个lncRNA的风险评分系统。Kaplan-Meier生存分析表明,高危组的总生存期明显更差。基于风险评分和临床特征,建立了列线图,其预测准确性优于单独的每个因素。最后,加权基因共表达网络分析表明,这些lncRNA可能通过调节免疫反应、代谢过程和信号转导来影响预后。总之,我们的模型和列线图可为DLBCL患者提供可靠的预后工具。