Wen Ying, Tang Yuanyuan, Zou Qiongyan
Department of General Surgery, The Second Xiangya Hospital, Central South University, Clinical Research Center for Breast Disease in Hunan Province, No. 139, Renmin Road, Changsha, Hunan, 410011, China.
Plastic surgery of breast cancer, Hunan Cancer Hospital, the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, Hunan, 410013, China.
Int J Med Sci. 2025 Aug 11;22(14):3763-3778. doi: 10.7150/ijms.119142. eCollection 2025.
Triple-negative breast cancer (TNBC) is an aggressive breast cancer subtype with a high risk of recurrence and poor clinical outcomes. However, the factors contributing to its relapse remain inadequately understood. In this study, we utilized transcriptomic data from The Cancer Genome Atlas (TCGA) to identify lncRNA pairs associated with both recurrence and immune response. A risk prediction model was constructed through the integration of LASSO regression, Cox proportional hazards analysis, and random forest algorithms. To validate its predictive capability, we employed an external validation cohort along with a backpropagation neural network (BPNN) to assess the model's performance. Our findings indicate that the proposed risk model correlates strongly with multiple clinical features, including immune cell infiltration, response to immunotherapy, tumor mutational burden (TMB), and chemotherapy sensitivity. Additionally, a nomogram integrating risk scores with clinical parameters demonstrated superior predictive accuracy compared to models based solely on risk scores. Experimental validation confirmed that silencing LINC01605 significantly impaired TNBC cell proliferation, migration, and invasion. Overall, this risk model provides a novel approach for predicting tumor recurrence and prognosis in TNBC patients. The study also highlights the potential of LINC01605 as a therapeutic target, offering new perspectives for personalized treatment strategies.
三阴性乳腺癌（TNBC）是一种侵袭性乳腺癌亚型，复发风险高且临床预后较差。然而，导致其复发的因素仍未得到充分了解。在本研究中，我们利用来自癌症基因组图谱（TCGA）的转录组数据来识别与复发和免疫反应相关的lncRNA对。通过整合LASSO回归、Cox比例风险分析和随机森林算法构建了一个风险预测模型。为了验证其预测能力，我们使用了一个外部验证队列以及一个反向传播神经网络（BPNN）来评估该模型的性能。我们的研究结果表明，所提出的风险模型与多种临床特征密切相关，包括免疫细胞浸润、对免疫治疗的反应、肿瘤突变负担（TMB）和化疗敏感性。此外，与仅基于风险评分的模型相比，将风险评分与临床参数相结合的列线图显示出更高的预测准确性。实验验证证实，沉默LINC01605会显著损害TNBC细胞的增殖、迁移和侵袭。总体而言，这种风险模型为预测TNBC患者的肿瘤复发和预后提供了一种新方法。该研究还强调了LINC01605作为治疗靶点的潜力，为个性化治疗策略提供了新的视角。
