Lin Jinsong, Lu Shubiao, Jiang Zhijian, Hu Chongjing, Zhang Zhiqiao
Department of Internal Medicine, The Affiliated Chencun Hospital of Shunde Hospital, Southern Medical University, Shunde, Guangdong, china.
PeerJ. 2021 May 7;9:e11412. doi: 10.7717/peerj.11412. eCollection 2021.
Individual mortality risk predicted curve at the individual level can provide valuable information for directing individual treatment decision. The present study attempted to explore potential post-transcriptional biological regulatory mechanism related with overall survival of lung adenocarcinoma (LUAD) patients through competitive endogenous RNA (ceRNA) network and develop two precision medicine predictive tools for predicting the individual mortality risk curves for overall survival of LUAD patients.
Multivariable Cox regression analyses were performed to explore the potential prognostic indicators, which were used to construct a prognostic model for overall survival of LUAD patients. Time-dependent receiver operating characteristic (ROC) curves were used to assess the predictive performance of prognostic model.
There were 494 LUAD patients in model cohort and 233 LUAD patients in validation cohort. Differentially expressed mRNAs, miRNAs, and lncRNAs were identified between LUAD tissues and normal tissues. A ceRNA regulatory network was constructed on previous differentially expressed mRNAs, miRNAs, and lncRNAs. Fourteen mRNA biomarkers were identified as independent risk factors by multivariate Cox regression and used to develop a prognostic model for overall survival of LUAD patients. The C-indexes of prognostic model in model group were 0.786 (95% CI [0.744-0.828]), 0.736 (95% CI [0.694-0.778]) and 0.766 (95% CI [0.724-0.808]) for one year, two year and three year overall survival respectively. Two precision medicine predicted tools were developed for predicting individual mortality risk curves for LUAD patients.
The current study explored potential post-transcriptional biological regulatory mechanism and prognostic biomarkers for overall survival of LUAD patients. Two on-line precision medicine predictive tools were helpful to predict the individual mortality risk predicted curves for overall survival of LUAD patients. Smart Cancer Survival Predictive System could be used at https://zhangzhiqiao2.shinyapps.io/Smart_cancer_predictive_system_9_LUAD_E1002/.
个体水平的个体死亡风险预测曲线可为指导个体治疗决策提供有价值的信息。本研究试图通过竞争性内源性RNA（ceRNA）网络探索与肺腺癌（LUAD）患者总生存相关的潜在转录后生物调控机制，并开发两种精准医学预测工具，用于预测LUAD患者总生存的个体死亡风险曲线。
进行多变量Cox回归分析以探索潜在的预后指标，这些指标用于构建LUAD患者总生存的预后模型。采用时间依赖性受试者工作特征（ROC）曲线评估预后模型的预测性能。
模型队列中有494例LUAD患者，验证队列中有233例LUAD患者。在LUAD组织和正常组织之间鉴定出差异表达的mRNA、miRNA和lncRNA。基于先前差异表达的mRNA、miRNA和lncRNA构建了一个ceRNA调控网络。通过多变量Cox回归鉴定出14个mRNA生物标志物作为独立危险因素，并用于开发LUAD患者总生存的预后模型。模型组中预后模型对于1年、2年和3年总生存的C指数分别为0.786（95%CI[0.744-0.828]）、0.736（95%CI[0.694-0.778]）和0.766（95%CI[0.724-0.808]）。开发了两种精准医学预测工具，用于预测LUAD患者的个体死亡风险曲线。
本研究探索了LUAD患者总生存的潜在转录后生物调控机制和预后生物标志物。两种在线精准医学预测工具有助于预测LUAD患者总生存的个体死亡风险预测曲线。可通过https://zhangzhiqiao2.shinyapps.io/Smart_cancer_predictive_system_9_LUAD_E1002/使用智能癌症生存预测系统。
