Yang Xuemei, Wang Yifan
Department of Clinical Laboratory, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, People's Republic of China.
Medicine (Baltimore). 2025 Apr 18;104(16):e42146. doi: 10.1097/MD.0000000000042146.
Disulfidptosis and ferroptosis, recently identified patterns of programmed cell death, play pivotal roles in the progression of breast cancer. This study aimed to explore the potential of disulfidptosis and ferroptosis in the prognostic stratification of Breast Cancer. Correlation analysis, Least Absolute Shrinkage and Selection Operator (LASSO) regression algorithm, univariate and multivariate Cox regression analyses were performed to identify the core long non-coding RNAs associated with disulfidptosis and ferroptosis. A risk signature and a prognostic nomogram were constructed based on these findings. Additionally, investigations concerning functional pathways, mutation landscapes, immune infiltration, and drug sensitivity were conducted in different risk stratification groups. Machine learning analyses revealed a risk signature comprising seven long non-coding RNAs closely associated with disulfidptosis and ferroptosis. Validated in two datasets, breast cancer patients with high-risk scores exhibited a poorer prognosis. The prognostic nomogram, integrating the risk signature with age and TNM stage, demonstrated a favorable predictive capability for survival outcomes. Furthermore, the high-risk group showed a higher tumor mutation burden compared to the low-risk group, which was also characterized by immune suppression and sensitivity to cisplatin, lapatinib and olaparib. Our study highlights the crucial role of disulfidptosis and ferroptosis in guiding clinical decision-making for patients with breast cancer, which also characterizes the intricate landscape of breast cancer and deepens our understanding of tumor heterogeneity.
二硫化物诱导的细胞死亡和铁死亡是最近发现的程序性细胞死亡模式，在乳腺癌进展中起关键作用。本研究旨在探讨二硫化物诱导的细胞死亡和铁死亡在乳腺癌预后分层中的潜力。进行了相关性分析、最小绝对收缩和选择算子（LASSO）回归算法、单因素和多因素Cox回归分析，以确定与二硫化物诱导的细胞死亡和铁死亡相关的核心长链非编码RNA。基于这些发现构建了风险特征和预后列线图。此外，还对不同风险分层组的功能途径、突变图谱、免疫浸润和药物敏感性进行了研究。机器学习分析揭示了一个由七个与二硫化物诱导的细胞死亡和铁死亡密切相关的长链非编码RNA组成的风险特征。在两个数据集中得到验证，高风险评分的乳腺癌患者预后较差。将风险特征与年龄和TNM分期相结合的预后列线图对生存结果显示出良好的预测能力。此外，高风险组与低风险组相比显示出更高的肿瘤突变负担，其特征还包括免疫抑制以及对顺铂、拉帕替尼和奥拉帕尼敏感。我们的研究强调了二硫化物诱导的细胞死亡和铁死亡在指导乳腺癌患者临床决策中的关键作用，这也描绘了乳腺癌的复杂图景并加深了我们对肿瘤异质性的理解。
