LAUSR

Simple Summary Breast cancer (BC) is a serious threat to women’s health and metastasis is the major cause of BC-associated mortality. Failure to detect and remove occult micrometastases limits the control of tumor recurrences. More precise non-invasive strategy needs to be developed for the detection of the tumor metastasis in lymph nodes and distant organs. Here, we suppose that the metabolomic method can be used to achieve non-invasive and real-time monitoring of BC metastatic status. We firstly described the metastatic status of BC mouse models with different tumor-bearing times. Secondly, metabonomics analysis and metastasis-related changes in the tumor microenvironment (TME) were performed in tumor-bearing mice with different metastatic states. The results showed that TME evolution can establish a link between metabolomics characteristics and tumor metastatic status. Finally, the changes of differential metabolite levels were also preliminarily confirmed in clinical BC samples and found that metabolite lysoPC (16:0) can be used for clinical N-stage diagnosis, and the possible causes of its changes was analyzed through bioinformatics technology. Abstract 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.