Colon cancer is one of the most common cancers. In this study, we isolated a lignan [(-)-(2R,3R)-1,4-O-diferuloylsecoisolariciresinol, DFS] from Alnus japonica (Betulaceae) and investigated its biological activity and mechanism of action on colon cancer. DFS reduced the viability of colon cancer cells and induced cell cycle arrest. DFS also suppressed β-catenin nuclear translocation and β-catenin target gene expression through a reduction in FoxM1 protein. To assess the mechanism of the action of DFS, we investigated the effect of DFS on endogenous and exogenous FoxM1 protein degradation in colon cancer cells. DFS-induced FoxM1 protein degradation was suppressed by lysosomal inhibitors, chloroquine and bafilomycin A1, but not by knock-down of proteasomal proteins. The mechanism of DFS for FoxM1 degradation is lysosomal dependent, which was not reported before. Furthermore, we found that FoxM1 degradation was partially lysosomal-dependent under normal conditions. These observations indicate that DFS from A. japonica suppresses colon cancer cell proliferation by reducing β-catenin nuclear translocation. DFS induces lysosomal-dependent FoxM1 protein degradation. This is the first report on the lysosomal degradation of FoxM1 by a small molecule. DFS may be useful in treating cancers that feature the elevated expression of FoxM1.
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http://dx.doi.org/10.1038/srep45951 | DOI Listing |
Circulation
January 2025
Pulmonary Hypertension Research Group, Québec Heart and Lung Institute Research Center, Quebec City, QC, Canada (S.-E.L., Y.G., T.Y., T.S., M.M., C.R., M.S., S.B.-B., A.B., C.T., A.P., R.E.K., S.M., K.Y., F.P., S.P., O.B., S.B.).
Background: Pulmonary arterial hypertension (PAH) is characterized by obliterative vascular remodeling of the small pulmonary arteries (PAs) and progressive increase in pulmonary vascular resistance leading to right ventricular failure. Although several drugs are approved for the treatment of PAH, mortality rates remain high. Accumulating evidence supports a pathological function of integrins in vessel remodeling, which are gaining renewed interest as drug targets.
View Article and Find Full Text PDFDrug Dev Res
February 2025
Department of Gynecology and Obstetrics, Affiliated Hospital of Nantong University, Nantong, China.
Ovarian cancer is the seventh most common lethal tumor among women in the world. FOXM1 is a transcription factor implicated in the initiation and progression of ovarian cancer by regulating key oncogenic genes. The role of regulatory regions in regulating the expression of FOXM1 in ovarian cancer is not completely clarified.
View Article and Find Full Text PDFJ Biochem Mol Toxicol
February 2025
Department of Gynecologic Oncology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, China.
TWIST1 is aberrantly expressed in ovarian cancer (OC). MFAP2 is a downstream target of TWIST1, and we previously found MFAP2 facilitated OC development by activating FOXM1/β-catenin. We planned to investigate the mechanisms of TWIST1 in OC.
View Article and Find Full Text PDFFEBS J
January 2025
State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China.
Lactate dehydrogenase A (LDHA) is upregulated in multiple cancer types and contributes to the Warburg effect. Several studies have found that many tumor-related genes have subtypes and play important roles in promoting cancer development. Here, we identified a novel LDHA transcript, which produced a new protein 3 kDa larger than LDHA, which we named LDHAα.
View Article and Find Full Text PDFJ Cell Mol Med
January 2025
Cancer Biology Research Center, Cancer Institute, Tehran University of Medical Sciences, Tehran, Iran.
This study identifies microRNAs (miRNAs) with significant discriminatory power in distinguishing melanoma from nevus, notably hsa-miR-26a and hsa-miR-211, which have exhibited diagnostic potential with accuracy of 81% and 78% respectively. To enhance diagnostic accuracy, we integrated miRNAs into various machine-learning (ML) models. Incorporating miRNAs with AUC scores above 0.
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