School of Computer Science and Technology, Shaanxi Engineering Research Center of Medical and Health Big Data, Xi'an Jiaotong University, Xi'an, China.
State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.
BMC Med Inform Decis Mak. 2020 Jul 9;20(Suppl 3):136. doi: 10.1186/s12911-020-1115-2.
Bladder cancer (BC) is regarded as one of the most fatal cancer around the world. Nevertheless, there still lack of sufficient markers to predict the prognosis of BC patients. Herein, we aim to establish a prognosis predicting signature based on long-noncoding RNA (lncRNA) for the invasive BC patients.
The lncRNA expression profile was downloaded from The Cancer Genome Atlas (TCGA) database, along with the correlated clinicopathological information. The univariate Cox regression test was employed to screen out the recurrence-free survival (RFS)-related lncRNAs. Then, the LASSO method was conducted to construct the signature based on these RFS-related lncRNA candidates. Genes correlated with these fourteen lncRNAs were extracted from the mRNA expression profile, with the Pearson correlation coefficient > 0.60 or < - 0.40. Subsequently, the Proteomap pathway enrichment analyses were conducted to classify the function of these correlated genes. Furthermore, the multivariate analyses were executed to reveal the independent role of the proposed signature with the clinicopathological features.
We established an lncRNA-based RFS predicting signature by the LASSO Cox regression test, and proved its usage and stability on both the training and validation cohorts by the Kaplan-Meier and receiver operating characteristic (ROC) curves. Notably, the multivariate Cox regression analysis found that our classifier was an independent indicator for muscle-invasive BC patients rather than sex, age and tumor grade, with higher predictive value than the existing ones. Besides, we did the pathway analyses for these genes that highly correlated with the proposed fourteen lncRNAs, as well as the differentially expressed genes (DEGs) derived from the high-risk vs. low-risk groups, and the recurrence vs. non-recurrence groups, respectively. Notably, these results were consistent, and these genes were mostly enriched in the transcription factors, G protein-coupled receptors, MAPK signaling pathways, which were proved significantly associated with tumor progression and drug resistance.
Our results suggested that the fourteen-lncRNA-based RFS predicting signature is an independent indicator for BC patients. Further prospective studies with more samples are needed to verify our findings.
膀胱癌(BC)被认为是全球最致命的癌症之一。然而,目前仍然缺乏足够的标志物来预测 BC 患者的预后。在此,我们旨在基于长链非编码 RNA(lncRNA)为侵袭性 BC 患者建立一个预后预测特征。
从癌症基因组图谱(TCGA)数据库下载 lncRNA 表达谱,并附有相关的临床病理信息。使用单因素 Cox 回归检验筛选与无复发生存(RFS)相关的 lncRNA。然后,基于这些 RFS 相关的 lncRNA 候选物,使用 LASSO 方法构建特征。从 mRNA 表达谱中提取与这 14 个 lncRNA 相关的基因,皮尔逊相关系数>0.60 或<-0.40。随后,进行 Proteomap 通路富集分析以分类这些相关基因的功能。此外,进行多变量分析以揭示所提出的特征与临床病理特征的独立作用。
我们通过 LASSO Cox 回归检验建立了一个基于 lncRNA 的 RFS 预测特征,并通过 Kaplan-Meier 和接受者操作特征(ROC)曲线在训练和验证队列中证明了其使用和稳定性。值得注意的是,多变量 Cox 回归分析发现,我们的分类器是肌层浸润性 BC 患者的独立指标,而不是性别、年龄和肿瘤分级,其预测价值高于现有指标。此外,我们对与所提出的 14 个 lncRNA 高度相关的这些基因以及来自高风险与低风险组、复发与非复发组的差异表达基因(DEG)分别进行了通路分析。值得注意的是,这些结果是一致的,这些基因主要富集在转录因子、G 蛋白偶联受体、MAPK 信号通路中,这些通路被证明与肿瘤进展和耐药性显著相关。
我们的结果表明,基于 14 个 lncRNA 的 RFS 预测特征是 BC 患者的独立指标。需要更多样本的前瞻性研究来验证我们的发现。