Wnt/beta-catenin signaling pathway was mutated in about 90% of the sporadic and hereditary colorectal cancers. The abnormally activated beta-catenin increases the cancer cell proliferation, differentiation and metastasis through increasing the expression of its oncogenic target genes. In this study, we identified an inhibitor of beta-catenin dependent Wnt pathway from rhizomes of Atractylodes macrocephala Koidzumi (Compositae). The active compound was purified by activity-guided purification and the structure was identified as 2,8-dimethyl-6-hydroxy-2-(4-methyl-3-pentenyl)-2H-chromene (atractylochromene, AC). AC suppressed beta-catenin/T-cell factor transcriptional activity of HEK-293 reporter cells when they were stimulated by Wnt3a or inhibitor of glycogen synthase kinase-3beta. AC down-regulated the nuclear level of beta-catenin through the suppression of galectin-3 mediated nuclear translocation of beta-catenin in SW-480 colon cancer cells. Furthermore, AC inhibits proliferation of colon cancer cell. Taken together, AC from A. macrocephala might be a potential chemotherapeutic agent for the prevention and treatment of human colon cancer.
Atractylodes ; beta Catenin ; Cell Proliferation ; Colonic Neoplasms* ; Colorectal Neoplasms ; Galectin 3 ; Glycogen Synthase ; Humans ; Neoplasm Metastasis ; Rhizome ; Wnt Signaling Pathway

Background: Conventional diagnostic approaches for adrenal tumors require multi-step processes, including imaging studies and dynamic hormone tests. Therefore, this study aimed to discriminate adrenal tumors from a single blood sample based on the combination of liquid chromatography-mass spectrometry (LC-MS) and machine learning algorithms in serum profiling of adrenal steroids. Methods: The LC-MS-based steroid profiling was applied to serum samples obtained from patients with nonfunctioning adenoma (NFA, n=73), Cushing’s syndrome (CS, n=30), and primary aldosteronism (PA, n=40) in a prospective multicenter study of adrenal disease. The decision tree (DT), random forest (RF), and extreme gradient boost (XGBoost) were performed to categorize the subtypes of adrenal tumors. Results: The CS group showed higher serum levels of 11-deoxycortisol than the NFA group, and increased levels of tetrahydrocortisone (THE), 20α-dihydrocortisol, and 6β-hydroxycortisol were found in the PA group. However, the CS group showed lower levels of dehydroepiandrosterone (DHEA) and its sulfate derivative (DHEA-S) than both the NFA and PA groups. Patients with PA expressed higher serum 18-hydroxycortisol and DHEA but lower THE than NFA patients. The balanced accuracies of DT, RF, and XGBoost for classifying each type were 78%, 96%, and 97%, respectively. In receiver operating characteristics (ROC) analysis for CS, XGBoost, and RF showed a significantly greater diagnostic power than the DT. However, in ROC analysis for PA, only RF exhibited better diagnostic performance than DT. Conclusion The combination of LC-MS-based steroid profiling with machine learning algorithms could be a promising one-step diagnostic approach for the classification of adrenal tumor subtypes.