本研究旨在开发基于免疫相关长非编码 RNA (lncRNA) 的预后风险模型。通过分析特定长非编码RNA的表达谱，目的是构建预测模型来准确评估乳腺癌（BC）患者的生存预后。这项工作旨在为患者提供个性化的治疗策略并改善临床结果。根据中位风险值，将300例三阴性BC（TNBC）患者样本分为高风险组（HR组，n = 140）和低风险组（LR组，n = 160）。通过结合患者风险评分和临床信息进行多变量Cox（MVC）分析，以评估预后风险（PR）模型的预后价值。从 300 个 TNBC 样本中获得了总共 371 个与 TNBC 预后相关的免疫相关 lncRNA。通过单变量Cox（UVC）分析获得9个与预后相关的样本，并通过MVC分析选择3个（AC090181.2、LINC01235和LINC01943）用于构建TNBC PR模型。生存分析显示不同组间TNBC患者存在较大差异(P<0.001)。受试者工作特征（ROC）曲线显示该模型具有良好的ROC曲线下面积（AUC），为0.928。患者RS结合临床信息以及MVC分析显示RS是TNBC预后的独立危险因素（IRF）（P<0.05，HR=1.033286）。因此，通过生物信息学分析可以筛选与TNBC免疫相关的lncRNA，建立的TNBC PR模型可以更好地预测TNBC患者的预后，具有较高的临床应用价值。©2024。作者。

This study aimed to develop a prognostic risk model based on immune-related long non-coding RNAs (lncRNAs). By analyzing the expression profiles of specific long non-coding RNAs, the objective was to construct a predictive model to accurately assess the survival prognosis of breast cancer (BC) patients. This effort seeks to provide personalized treatment strategies for patients and improve clinical outcomes. Based on the median risk value, 300 samples of triple-negative BC (TNBC) patients were rolled into a high-risk group (HR group, n = 140) and a low-risk group (LR group, n = 160). Multivariate Cox (MVC) analysis was performed by combining the patient risk score and clinical information to evaluate the prognostic value of the prognostic risk (PR) model. A total of 371 immune-related lncRNAs associated with the prognosis of TNBC were obtained from 300 TNBC samples. Nine associated with prognosis were obtained by univariate Cox (UVC) analysis, and 3 (AC090181.2, LINC01235, and LINC01943) were selected by MVC analysis for the construction of TNBC PR model. Survival analysis showed a great difference in TNBC patients in different groups (P < 0.001). The receiver operator characteristic (ROC) curve showed the model possessed a good area under ROC curve (AUC), which was 0.928. The patient RS jointing with clinical information as well as the MVC analysis revealed that RS was an independent risk factor (IRF) for prognosis of TNBC (P < 0.05, HR = 1.033286). Therefore, the lncRNAs associated with TNBC immunity can be screened by bioinformatics analysis, and the established PR model of TNBC could better predict the prognosis of patients with TNBC, exhibiting a high application value in clinic.© 2024. The Author(s).