Zhu Wanfang, Qian Wenxin, Liao Wenting, Huang Xiaoxian, Xu Jiawen, Qu Wei, Xue Jingwei, Feng Feng, Liu Wenyuan, Liu Fulei, Han Lingfei
Department of Pharmaceutical Analysis, China Pharmaceutical University, Nanjing 210009, China.
College of Pharmacy, Changchun University of Chinese Medicine, Changchun 130117, China.
Cancers (Basel). 2022 Nov 14;14(22):5589. doi: 10.3390/cancers14225589.
Breast cancer (BC) is a serious threat to women's health and metastasis is the major cause of BC-associated mortality. Various techniques are currently used to preoperatively describe the metastatic status of tumors, based on which a comprehensive treatment protocol was determined. However, accurately staging a tumor before surgery remains a challenge, which may lead to the miss of optimal treatment options. More severely, the failure to detect and remove occult micrometastases often causes tumor recurrences. There is an urgent need to develop a more precise and non-invasive strategy for the detection of the tumor metastasis in lymph nodes and distant organs. Based on the facts that tumor metastasis is closely related to the primary tumor microenvironment (TME) evolutions and that metabolomics profiling of the circulatory system can precisely reflect subtle changes within TME, we suppose whether metabolomic technology can be used to achieve non-invasive and real-time monitoring of BC metastatic status. In this study, the metastasis status of BC mouse models with different tumor-bearing times was firstly depicted to mimic clinical anatomic TNM staging system. Metabolomic profiling together with metastasis-related changes in TME among tumor-bearing mice with different metastatic status was conducted. A range of differential metabolites reflecting tumor metastatic states were screened and in vivo experiments proved that two main metastasis-driving factors in TME, TGF-β and hypoxia, were closely related to the regular changes of these metabolites. The differential metabolites level changes were also preliminarily confirmed in a limited number of clinical BC samples. Metabolite lysoPC (16:0) was found to be useful for clinical N stage diagnosis and the possible cause of its changes was analyzed by bioinformatics techniques.
乳腺癌(BC)是对女性健康的严重威胁,转移是BC相关死亡的主要原因。目前使用各种技术在术前描述肿瘤的转移状态,并据此确定综合治疗方案。然而,术前准确对肿瘤进行分期仍然是一项挑战,这可能导致错过最佳治疗方案。更严重的是,未能检测和清除隐匿性微转移常常导致肿瘤复发。迫切需要开发一种更精确、非侵入性的策略来检测淋巴结和远处器官中的肿瘤转移。基于肿瘤转移与原发性肿瘤微环境(TME)演变密切相关以及循环系统代谢组学分析能够精确反映TME内细微变化的事实,我们推测代谢组学技术是否可用于实现对BC转移状态的非侵入性实时监测。在本研究中,首先描绘了具有不同荷瘤时间的BC小鼠模型的转移状态,以模拟临床解剖学TNM分期系统。对具有不同转移状态的荷瘤小鼠进行了代谢组学分析以及TME中与转移相关的变化研究。筛选出一系列反映肿瘤转移状态的差异代谢物,体内实验证明TME中的两个主要转移驱动因素,即转化生长因子-β(TGF-β)和缺氧,与这些代谢物的规律性变化密切相关。在有限数量的临床BC样本中也初步证实了差异代谢物水平的变化。发现代谢物溶血磷脂酰胆碱(16:0)对临床N分期诊断有用,并通过生物信息学技术分析了其变化的可能原因。