Department of Breast Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.
Xingyuan Hospital of Yulin City, Yulin City, 719051, Shanxi Province, China.
Sci Rep. 2024 Feb 3;14(1):2859. doi: 10.1038/s41598-024-52981-w.
As the malignancy with the highest global incidence, breast cancer represents a significant threat to women's health. Recent advances have shed light on the importance of mitochondrial function in cancer, particularly in metabolic reprogramming within tumors. Recognizing this, we developed a novel risk signature based on mitochondrial-related genes to improve prognosis prediction and risk stratification in breast cancer patients. In this study, transcriptome data and clinical features of breast cancer samples were extracted from two sources: the TCGA, serving as the training set, and the METABRIC, used as the independent validation set. We developed the signature using LASSO-Cox regression and assessed its prognostic efficacy via ROC curves. Furthermore, the signature was integrated with clinical features to create a Nomogram model, whose accuracy was validated through clinical calibration curves and decision curve analysis. To further elucidate prognostic variations between high and low-risk groups, we conducted functional enrichment and immune infiltration analyses. Additionally, the study encompassed a comparison of mutation landscapes and drug sensitivity, providing a comprehensive understanding of the differing characteristics in these groups. Conclusively, we established a risk signature comprising 8 mitochondrial-related genes-ACSL1, ALDH2, MTHFD2, MRPL13, TP53AIP1, SLC1A1, ME3, and BCL2A1. This signature was identified as an independent risk predictor for breast cancer patient survival, exhibiting a significant high hazard ratio (HR = 3.028, 95%CI 2.038-4.499, P < 0.001). Patients in the low-risk group showed a more favorable prognosis, with enhanced immune infiltration, distinct mutation landscapes, and greater sensitivity to anti-tumor drugs. In contrast, the high-risk group exhibited an adverse trend in these aspects. This risk signature represents a novel and effective prognostic indicator, suggesting valuable insights for patient stratification in breast cancer.
作为全球发病率最高的恶性肿瘤，乳腺癌对女性健康构成了重大威胁。最近的研究进展揭示了线粒体功能在癌症中的重要性，尤其是在肿瘤内的代谢重编程方面。基于此，我们开发了一种基于线粒体相关基因的新型风险特征，以改善乳腺癌患者的预后预测和风险分层。在这项研究中，我们从两个来源提取了乳腺癌样本的转录组数据和临床特征：TCGA 作为训练集，METABRIC 作为独立验证集。我们使用 LASSO-Cox 回归开发了特征，并通过 ROC 曲线评估了其预后效果。此外，我们将特征与临床特征相结合，创建了一个 Nomogram 模型，并通过临床校准曲线和决策曲线分析验证了其准确性。为了进一步阐明高风险组和低风险组之间的预后差异，我们进行了功能富集和免疫浸润分析。此外，我们还比较了突变景观和药物敏感性，从而全面了解这两组的不同特征。总之，我们建立了一个由 8 个线粒体相关基因（ACSL1、ALDH2、MTHFD2、MRPL13、TP53AIP1、SLC1A1、ME3 和 BCL2A1）组成的风险特征。该特征被确定为乳腺癌患者生存的独立预后预测因子，具有显著的高风险比（HR=3.028，95%CI 2.038-4.499，P<0.001）。低风险组患者的预后更为有利，免疫浸润增强，突变景观明显不同，对抗肿瘤药物的敏感性更高。相比之下，高风险组在这些方面表现出不利趋势。这个风险特征代表了一种新的、有效的预后指标，为乳腺癌患者分层提供了有价值的见解。
