利用数据库信息构建乳腺癌免疫关联lncRNAs风险评估模型

目的 筛选乳腺癌中免疫关联长链非编码RNA(lncRNA),并构建乳腺癌预后风险评估模型,探索预后相关因素。 方法 从UCSC Xena(https://xena.ucsc.edu/)、TCGA、immport(https://www.immport.org/home)官网分别下载乳腺癌患者的测序数据、临床信息以及免疫基因集,并将这些数据进行整理和清洗,最终得到乳腺癌免疫关联lncRNA表达矩阵及临床信息。利用单因素Cox和多因素Cox回归分析筛选出与预后相关的免疫关联 lncRNA,用于构建预后风险评分。根据风险评分的中位数,将患者分为高风险组和低风险组,利用Kaplan-Meier(K-M)生存分析、受试者工作特征曲线(ROC)分析及独立预后因素评估对模型进行评价,并将此模型联合其他临床因素构建列线图,对乳腺癌患者进行生存率预测。 结果 最终确定10个免疫关联 lncRNAs 用来构建风险评分模型;高风险组较低风险组预后差;风险评分可作为乳腺癌患者的独立预后因素;列线图的C指数(CI)为0.751,校准图显示预测值与实际观测值一致性较好。 结论 由10个免疫关联lncRNAs 组成的风险评分模型可用于评估乳腺癌患者的预后,由此建立的列线图可进一步预测乳腺癌患者的生存率。

Construct of a risk assessment model of breast cancer immune-related lncRNAs based on the database information

Objective To screen the immune-related long non-coding RNA(lncRNA)in breast cancer, construct a breast cancer prognostic risk assessment model, and explore the prognostic factors. Methods The sequencing data, clinical information and immune gene set of breast cancer patients from the official websites of UCSC Xena(https://xena.ucsc.edu/), TCGA, and immport(https://www.immport.org/home)were downloaded. The data were sorted and cleaned, and finally breast cancer immune-related lncRNA expression matrix and clinical information were got. Univariate Cox regression analysis and Multivariate Cox regression analysis were used to screen out lncRNAs related to prognosis, so as to construct a prognostic risk score. According to the median risk score, the patients were divided into high-risk and low-risk groups. Kaplan-Meier(K-M)survival analysis, receiver operating characteristic(ROC)curve analysis and independent prognostic factor evaluation were used to evaluate the model. Combined with other clinical factors and risk scores of breast cancer, a nomogram was drawn to predict the individual survival rate of breast cancer patients. Results Ten immune-related lncRNAs were determined to construct a risk scoring model. The high-risk group had a poorer prognosis compared with the low-risk group. Risk score could be used as an independent prognostic factor for breast cancer. The C-index(CI)of the nomogram was 0.751. The calibration chart showed that the predicted value was in good agreement with the actual observation value. Conclusion A risk scoring model composed of 10 immune-related lncRNAs can be used to assess the prognosis of the breast cancer patients, and the corresponding nomogram can further predict the survival rate.