Prognostic significance of a long non-coding RNA signature in patients with diffuse large B-cell lymphoma
-
摘要:
目的 识别与弥漫大B细胞淋巴瘤（diffuse large B-cell lymphoma，DLBCL）患者预后相关的长链非编码RNA（long non-coding RNA，lncRNA）并评估其预后价值。 方法 从高通量基因表达数据库（gene expression omnibus，GEO）下载DLBCL患者的基因和临床信息：GSE31312（n=424，训练集）和GSE10846（n=295，验证集）。在训练集中通过套索算法（least absolute shrinkage and selection operator，LASSO）结合平稳选择法以及多因素Cox回归筛选与患者预后相关的lncRNA。根据所筛lncRNA将患者进行危险分层（高危组和低危组）并进行log-rank检验。然后分别建立国际预后指数（international prognostic index，IPI）、lncRNA及IPI+lncRNA的Cox比例风险模型，依据时点/动态受试者工作特征（receiver operating characteristic，ROC）曲线法对各模型的预测准确度进行评价和比较。 结果 共发现8个与DLBCL预后相关的lncRNA。训练和验证集中，两组患者生存曲线的差异均有统计学意义（训练集：χ2=73.1，P < 0.001；验证集：χ2=13.4，P < 0.001），IPI+lncRNA模型的C指数与其他两个模型相比，差异也具有统计学意义（训练集：IPI+lncRNA vs. IPI：Z=76.536，P < 0.001；IPI+lncRNA vs. lncRNA：Z=17.714，P < 0.001；验证集：IPI+lncRNA vs. IPI：Z=42.427，P < 0.001；IPI+lncRNA vs. lncRNA：Z=30.587，P < 0.001）。 结论 本研究发现的8个lncRNA可以作为DLBCL的预后标志物，与IPI结合可提高对DLBCL患者预后判断的准确性。 Abstract:Objective To identify the relationship between a set of long non-coding RNA (lncRNA) signature and the prognosis of diffuse large B-cell lymphoma (DLBCL) patients, so as to evaluate their prognostic significance. Methods LncRNA expression profiles and clinical data of DLBCL patients were extracted from the gene expression omnibus (GEO) database: GSE31312 (n=424, training set) and GSE10846 (n=295, validation set). In the training set, a set of 8-lncRNA signature associated with the overall survival (OS) of DLBCL patients was established through least absolute shrinkage and selection operator (LASSO) combined with stability selection method and multivariate Cox regression analysis.Based on this 8-lncRNA signature, the patients in each data set could be classified into high-risk group and low-risk group. The survival curves of patients in both groups were compared by log-rank test. Then three Cox proportional hazard models, namely, international prognostic index (IPI), lncRNA signature and IPI+lncRNA signature model were constructed. The incident/dynamic receiver operating characteristic (ROC) curves method was applied to evaluate and compare their predict accuracy. Results A total of 8 lncRNAs related to the prognosis of DLBCL were found.In the training and validation sets, the difference of survival curves between high-risk group and low-risk group was statistically significant respectively (training set: χ2=73.1, P < 0.001, validation set: χ2=13.4, P < 0.001), the difference of C-index between IPI+lncRNA signature model and either of the other two models were also statistically significant (training set: IPI+lncRNA vs. IPI: Z=76.536, P < 0.001; IPI+lncRNA vs. lncRNA: Z=17.714, P < 0.001;validation set: IPI+lncRNA vs. IPI: Z=42.427, P < 0.001; IPI+lncRNA vs. lncRNA: Z=30.587, P < 0.001). Conclusions The 8-lncRNA signature can be used as a composite prognostic biomarker of DLBCL. Besides, lncRNA combined with IPI has better prognostic predict ability than IPI alone. -
Key words:
- Diffuse large B-cell lymphoma /
- Long non-coding RNA /
- Prognosis
-
图 1 训练和验证集中高危组和低危组患者的Kaplan-Meier生存曲线
Figure 1. Kaplan-Meier survival curves of patients between high-risk group and low-risk group in the training and validation sets
图 2 训练集和验证集中IPI、lncRNA和IPI+lncRNA模型的预测准确度
注：P值是根据Z检验所得，均是和IPI+lncRNA模型进行比较。
Figure 2. The predict accuracy for IPI, lncRNA signature and IPI +lncRNA signature model in the training and validation sets
表 1 训练集和验证集中DLBCL患者的主要临床特征
Table 1. Clinical characteristics of DLBCL patients in the training and validation sets
临床因素 GSE31312(424) GSE10846(295) 性别 男 248 158 女 176 124 缺失 0 13 年龄(岁) ≤60 177 138 ＞60 247 157 结外病变受侵部位数(个) ＜2 332 272 ≥2 92 23 乳酸脱氢酶(LDH) 正常 141 151 高于正常 263 144 缺失 20 0 行为状态评分(ECOG)(分) ＜2 359 223 ≥2 65 72 分期 Ⅰ或Ⅱ期 - 137 Ⅲ或Ⅳ期 - 158 亚型 GCB 201 128 ABC 185 122 未分类 38 45 生存状态 存活 269 167 死亡 155 128 注：表格中“-”表示缺失。 
下载: 导出CSV
表 2 与DLBCL患者预后相关的候选lncRNA
Table 2. Candidate lncRNA signature associated with prognosis of DLBCL patients
lncRNA β HR(95% CI)值 P值 RB1-DTa 0.470 1.599 (1.374~1.862) < 0.001 RPARP-AS1 0.176 1.193 (1.000~1.422) 0.049 SNHG20a 0.321 1.378 (1.146~1.658) < 0.001 H19a -0.276 0.758 (0.633~0.909) 0.003 MIR155HGa -0.182 0.834 (0.707~0.983) 0.031 PAXIP1-AS1a 0.236 1.266 (1.067~1.503) 0.007 LINC01857 -0.164 0.849 (0.706~1.021) 0.083 PRMT5-AS1a 0.305 1.357 (1.176~1.565) < 0.001 LINC01578 -0.092 0.912 (0.767~1.085) 0.297 EP300-AS1 -0.162 0.851 (0.706~1.025) 0.089 PWAR5a -0.289 0.851 (0.622~0.901) 0.002 ASAP1-IT1a -0.246 0.782 (0.645~0.947) 0.012 注：表格中所列lncRNA为初筛所得，a：最终筛选结果。
下载: 导出CSV
表 3 训练集和验证集中Cox比例风险模型结果
Table 3. Results of Cox proportional hazard models in the training and validation sets
模型 变量 β值 SE Wald值 HR(95% CI)值 P值 GSE31312 模型1 IPI 1.099 0.162 46.03 3.002(2.185~4.125) < 0.001 模型2 lncRNA 0.985 0.087 127.00 2.677(2.255~3.177) < 0.001 模型3 IPI 0.689 0.168 141.20 1.991(1.432~2.769) < 0.001 lncRNA 0.890 0.092 2.434(2.034~2.912) < 0.001 GSE10846 模型1 IPI 1.088 0.182 35.78 2.969(2.078~4.241) < 0.001 模型2 lncRNA 0.488 0.097 25.53 1.629(1.348~1.968) < 0.001 模型3 IPI 1.195 0.183 66.92 3.303(2.305~4.732) < 0.001 lncRNA 0.538 0.097 1.712(1.416~2.071) < 0.001
下载: 导出CSV
-
[1] Younes A. Prognostic significance of diffuse large B-cell lymphoma cell of origin: seeing the forest and the trees[J]. J Clin Oncol, 2015, 33(26): 2835-2836. DOI: 10.1200/jco.2015.61.9288. [2] 阙喜妹, 郗彦凤, 王彤. Mir-27a高表达对弥漫性大B细胞淋巴瘤患者预后的影响[J]. 中华疾病控制杂志, 2018, 22(10): 1004-1007. DOI: 10.16462/j.cnki.zhjbkz.2018.10.006.Que XM, Xi YF, Wang T. Prognostic significance of high expression of mir-27a in patients with diffuse large B cell lymphoma[J]. Chin J Dis Control Prev, 2018, 22(10): 1004-1007. DOI: 10.16462/j.cnki.zhjbkz.2018.10.006. [3] Balas MM, Johnson AM. Exploring the mechanisms behind long noncoding RNAs and cancer[J]. Noncoding RNA Res, 2018, 3(3): 108-117. DOI: 10.1016/j.ncrna.2018.03.001. [4] Brazao TF, Johnson JS, Muller J, et al. Long noncoding RNAs in B-cell development and activation[J]. Blood, 2016, 128(7): e10-19. DOI: 10.1182/blood-2015-11-680843. [5] Sun J, Cheng L, Shi H, et al. A potential panel of six-long non-coding RNA signature to improve survival prediction of diffuse large-B-cell lymphoma[J]. Sci Rep, 2016, 6: 27842. DOI: 10.1038/srep27842. [6] Peng W, Wu J, Feng J. LincRNA-p21 predicts favorable clinical outcome and impairs tumorigenesis in diffuse large B cell lymphoma patients treated with R-CHOP chemotherapy[J]. Clin Exp Med, 2017, 17(1): 1-8. DOI: 10.1007/s10238-015-0396-8. [7] Deng L, Jiang L, Tseng KF, et al. Aberrant NEAT1_1 expression may be a predictive marker of poor prognosis in diffuse large B cell lymphoma[J]. Cancer Biomark, 2018, 23(2): 157-164. DOI: 10.3233/cbm-160221. [8] Yan Y, Han J, Li Z, et al. Elevated RNA expression of long noncoding HOTAIR promotes cell proliferation and predicts a poor prognosis in patients with diffuse large B cell lymphoma[J]. Mol Med Rep, 2016, 13(6): 5125-5131. DOI: 10.3892/mmr.2016.5190. [9] 许树红, 董晓强, 陶然, 等. 基于LASSO的FDR控制方法及其在高维数据生存分析中的应用[J]. 中国卫生统计, 2018, 35(3): 322-3229. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGWT201803001.htmXu SH, Dong XQ, Tao R, et al. LASSO-based methods with the false discovery rate control and the application in survival analysis of high-dimensional data[J]. Chin J Health Statistics, 2018, 35(3): 322-329. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGWT201803001.htm [10] Meinshausen N, Bühlmann P. Stability selection[J]. J R Stat Soc Series B Stat Methodol, 2010, 72(4): 417-473. DOI: 10.1111/j.1467-9868.2010.00740.x. [11] Michiels S, Koscielny S, Hill C. Prediction of cancer outcome with microarrays: a multiplerandom validation strategy[J]. Lancet, 2005, 365(9458): 488-492. DO: 10.1016/s0140-6736(05)17866-0. doi: 10.1016/S0140-6736(05)17866-0 [12] Zhou M, Guo M, He D, et al. A potential signature of eight long non-coding RNAs predicts survival in patients with non-small cell lung cancer[J]. J Transl Med, 2015, 13: 231. DOI: 10.1186/s12967-015-0556-3. [13] 吴丽, 王彤, 吕志杰, 等. 时点/动态ROC曲线法在生存模型预后评价中的应用[J]. 中国卫生统计, 2015, 32(6): 1001-1003. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGWT201506026.htmWu L, Wang T, Lu ZJ, et al. The application of incident/dynamic ROC curves method in prognosis evaluation of survival model[J]. Chin J Health Statistics, 2015, 32(6): 1001-1003. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGWT201506026.htm [14] 范磊, 徐卫, 李建勇. 弥漫大B细胞淋巴瘤预后新指标[J]. 中国实用内科杂志, 2015, 35(2): 81-84. DOI: 10.7504/nk2015010101.Fan L, Xu W, Li JY. Novel prognostic factors of diffuse large B-cell lymphoma[J]. Chin J Pract Intern Med, 2015, 35(2): 81-84. DOI: 10.7504/nk2015010101. [15] Kluiver J, Poppema S, de Jong D, et al. BIC and miR-155 are highly expressed in Hodgkin, primary mediastinal and diffuse large B cell lymphomas[J].J Pathol, 2005, 207(2):243-249. DOI: 10.1002/path.1825.DOI:10.1002/path.1825. [16] Chen T, Yang P, He ZY. Long non-coding RNA H19 can predict a poor prognosis and lymph node metastasis: a meta-analysis in human cancer[J]. Minerva Med, 2016, 107(4): 251-258. http://europepmc.org/abstract/MED/27348443 [17] Wang XP, Shan C, Deng XL, et al. Long non-coding RNA PAR5 inhibits the proliferation and progression of glioma through interaction with EZH2[J]. Oncol Rep, 2017, 38(5): 3177-3186. DOI: 10.3892/or.2017.5986. [18] Iuvone PM, Gan J, Alonso-Gómez AL. Strong expression of EZH2 and accumulation of trimethylated H3K27 in diffuse large B-cell lymphoma (DLBCL) independent of cell of origin and EZH2 codon 641 mutation[J]. Leuk Lymphoma, 2015, 56(10): 2895-2901. DOI: 10.3109/10428194.2015.1006220. [19] 邓玉洁, 陈刚, 朱伟峰, 等. H3K27me3和EZH2在弥漫大B细胞淋巴瘤疗效预测中的意义[J]. 中国实验血液学杂志, 2018, 26(1): 159-165. DOI: 10.7534/j.issn.1009-2137.2018.01.027.Deng YJ, Chen G, Zhu WF, et al. Significance of H3K27me3 and EZH2 in predicting the therapeutic efficacy of diffuse large B-cell lymphoma[J]. J Exp Hematol, 2018, 26(1): 159-165. DOI: 10.7534/j.issn.1009-2137.2018.01.027. [20] Tang J, Xie Y, Xu X, et al. Bidirectional transcription of Linc00441 and RB1 via H3K27 modification-dependent way promotes hepatocellular carcinoma[J]. Cell death Dis, 2017, 8(3): e2675. DOI: 10.1038/cddis.2017.81. -
点击查看大图
图(2) / 表(3)
计量
- 文章访问数: 252
- HTML全文浏览量: 74
- PDF下载量: 17
- 被引次数: 0