1. 三阴性乳腺癌免疫相关 LncRNA 筛选 及预后预测模型构建.
- Author
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曹雷雨, 刘伟, 高艳, 马晓丽, 魏瑜, 渠成程, 努尔斯曼古丽·买买提明, and 张莉
- Abstract
Objective To screen out the immune-related LncRNA genes in triple-negative breast cancer (TNBC) and to construct a prognostic prediction model of TNBC. Methods ① We downloaded the transcriptome RNA expression profiling raw data of TNBC tissues and para-cancer tissues from the Cancer Genome Atlas (TCGA) project, and combined with the Immport database to screen TNBC immune-related lncRNAs. Furthermore, univariate and multivariate Cox analyses were used to screen immune-related lncRNAs closely related to TNBC and prognosis, and then immune-related ln cRNAs closely related to prognosis were determined according to the optimal AIC value. ② Based on the immune-related lncRNAs, the prognostic prediction model of TNBC was constructed. ③ The TNBC patient samples were divided into highrisk and low-risk groups based on the median of the risk score values calculated from the TNBC prognostic risk model, and survival was compared between the high- and low-risk groups. ④ ROC and principal component analysis (PCA) were used to evaluate the predictive efficacy of the TNBC prognostic prediction model. ⑤ Multivariate Cox analysis was used to analyze the independence of TNBC prognostic prediction model in predicting efficacy. Results A total of 369 immune-related lncRNAs were obtained from 65 TNBC tissue samples. Three immune-related lncRNAs (AC090181. 2, LINC01235, and LINC01943) were obtained by univariate and multivariate Cox analyses, and the prognostic prediction model of TNBC was constructed. The formula of the model was as follows: risk score (RS) = (– 6. 24904×AC090181. 2) + (0. 240562×LINC01235) + (–2. 18153×LINC01943). There was significant difference between the high-risk group and the low-risk group (P<0. 001). The predictive model of TNBC had a good predictive effect on the prognosis of patients (AUC = 0. 910; the distribution of projection points was different between the high- and low-risk groups, and the discrimination degree was high). TNBC prognostic prediction model could predict TNBC prognosis independently (HR=1. 028,P< 0. 05). Conclusion We have successfully constructed a prognostic prediction model consisting of three immune-related LncRNAs (AC090181. 2, LINC01235, and LINC01943) closely related to prognosis, which can better predict the prognosis of TNBC patients. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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