Lysophosphatidic acid (LPA), acting in an autocrine or paracrine fashion through G protein-coupled receptors, has been implicated in many physiologic and pathologic processes, including cancer. LPA is converted from lysophosphatidylcholine (LPC) by the secreted phospholipase autotaxin (ATX). Although various cell types can produce ATX, adipocyte-derived ATX is believed to be the major source of circulating ATX and also to be the major regulator of plasma LPA levels. In addition to ATX, adipocytes secrete numerous other factors (adipokines); although several adipokines have been implicated in breast cancer biology, the contribution of mammary adipose tissue-derived LPC/ATX/LPA (LPA axis) signaling to breast cancer is poorly understood. Using murine mammary fat-conditioned medium, we investigated the contribution of LPA signaling to mammary epithelial cancer cell biology and identified LPA signaling as a significant contributor to the oncogenic effects of the mammary adipose tissue secretome. To interrogate the role of mammary fat in the LPA axis during breast cancer progression, we exposed mammary adipose tissue to secreted factors from estrogen receptor-negative mammary epithelial cell lines and monitored changes in the mammary fat pad LPA axis. Our data indicate that bidirectional interactions between mammary cancer cells and mammary adipocytes alter the local LPA axis and increase ATX expression in the mammary fat pad during breast cancer progression. Thus, the LPC/ATX/LPA axis may be a useful target for prevention in patients at risk of ER-negative breast cancer. Cancer Prev Res; 9(5); 367-78. ©2016 AACR.
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http://dx.doi.org/10.1158/1940-6207.CAPR-15-0107 | DOI Listing |
Sci Rep
December 2024
Department of Clinical Pharmacy, Baoshan Hospital Affiliated to, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
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December 2024
Department of Breast Surgery, Second Affiliated Hospital of Dalian Medical University, No. 467 Zhongshan Road, Shahekou District, Dalian, China.
Early prediction of patient responses to neoadjuvant chemotherapy (NACT) is essential for the precision treatment of early breast cancer (EBC). Therefore, this study aims to noninvasively and early predict pathological complete response (pCR). We used dynamic ultrasound (US) imaging changes acquired during NACT, along with clinicopathological features, to create a nomogram and construct a machine learning model.
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