DNMT3A Cooperates with YAP/TAZ to Drive Gallbladder Cancer Metastasis
Source abstract
Abstract
Abstract Gallbladder cancer (GBC) is an extremely lethal malignancy with aggressive behaviors, including liver or distant metastasis; however, the underlying mechanisms driving the metastasis of GBC remain poorly understood. In this study, it is found that DNA methyltransferase DNMT3A is highly expressed in GBC tumor tissues compared to matched adjacent normal tissues. Clinicopathological analysis shows that DNMT3A is positively correlated with liver metastasis and poor overall survival outcomes in patients with GBC. Functional analysis confirms that DNMT3A promotes the metastasis of GBC cells in a manner dependent on its DNA methyltransferase activity. Mechanistically, DNMT3A interacts with and is recruited by YAP/TAZ to recognize and access the CpG island within the CDH1 promoter and generates hypermethylation of the CDH1 promoter, which leads to transcriptional silencing of CDH1 and accelerated epithelial‐to‐mesenchymal transition. Using tissue microarrays, the association between the expression of DNMT3A, YAP/TAZ, and CDH1 is confirmed, which affects the metastatic ability of GBC. These results reveal a novel mechanism through which DNMT3A recruitment by YAP/TAZ guides DNA methylation to drive GBC metastasis and provide insights into the treatment of GBC metastasis by targeting the functional connection between DNMT3A and YAP/TAZ.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Epigenetics and DNA Methylation · Hippo pathway signaling and YAP/TAZ · Ubiquitin and proteasome pathways
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Biliary‐Pancreatic Surgery Renji Hospital, School of Medicine Shanghai Jiao Tong University Shanghai 200125 China
Department of Thyroid and Breast Surgery The First Affiliated Hospital of Fujian Medical University Fuzhou 350005 China
Central Laboratory Fujian Medical University Union Hospital Fuzhou 350001 China
Funding
National Natural Science Foundation of China
References (57)
Show source-provided references
1. 10.1016/j.canlet.2021.09.00410.1016/j.canlet.2021.09.004 ↗
2. 10.1038/s41572-022-00398-y10.1038/s41572-022-00398-y ↗
3. 10.1016/j.jss.2018.04.06710.1016/j.jss.2018.04.067 ↗
4. 10.4143/crt.2019.27110.4143/crt.2019.271 ↗
5. 10.1186/s12876-022-02234-910.1186/s12876-022-02234-9 ↗
6. 10.1007/s13277-016-5355-910.1007/s13277-016-5355-9 ↗
7. 10.1245/ASO.2003.02.01410.1245/ASO.2003.02.014 ↗
8. 10.1158/1078-0432.CCR-04-057910.1158/1078-0432.CCR-04-0579 ↗
9. 10.1002/hep.3158510.1002/hep.31585 ↗
10. 10.1016/j.tig.2021.05.00210.1016/j.tig.2021.05.002 ↗
11. 10.1038/nrg.2017.3310.1038/nrg.2017.33 ↗
12. 10.1038/s43018-022-00361-610.1038/s43018-022-00361-6 ↗
13. 10.1186/s13046-019-1400-z10.1186/s13046-019-1400-z ↗
14. 10.1038/s41388-018-0285-110.1038/s41388-018-0285-1 ↗
15. 10.3389/fphar.2022.95814610.3389/fphar.2022.958146 ↗
16. 10.1038/s41467-019-13420-x10.1038/s41467-019-13420-x ↗
17. 10.1186/s13046-021-02186-010.1186/s13046-021-02186-0 ↗
18. 10.1016/j.ccr.2014.02.01010.1016/j.ccr.2014.02.010 ↗
19. 10.1093/bioinformatics/18.11.142710.1093/bioinformatics/18.11.1427 ↗
20. 10.1016/j.ccell.2016.05.00510.1016/j.ccell.2016.05.005 ↗
21. 10.1146/annurev-biochem-013118-11182910.1146/annurev-biochem-013118-111829 ↗
22. 10.1016/j.molcel.2018.10.03010.1016/j.molcel.2018.10.030 ↗
23. 10.1126/science.aav017310.1126/science.aav0173 ↗
24. 10.1053/j.gastro.2020.12.07710.1053/j.gastro.2020.12.077 ↗
25. 10.1158/0008-5472.CAN-21-093310.1158/0008-5472.CAN-21-0933 ↗
26. 10.1016/j.canlet.2014.08.03610.1016/j.canlet.2014.08.036 ↗
27. 10.1186/s12967-023-04157-910.1186/s12967-023-04157-9 ↗
28. 10.1038/nature1013710.1038/nature10137 ↗
29. 10.1242/dev.19667510.1242/dev.196675 ↗
30. 10.15252/embr.20205180310.15252/embr.202051803 ↗
31. 10.1158/0008-5472.CAN-20-303310.1158/0008-5472.CAN-20-3033 ↗
32. 10.1056/NEJMoa100514310.1056/NEJMoa1005143 ↗
33. 10.1073/pnas.170347611410.1073/pnas.1703476114 ↗
34. 10.1093/nar/gku129310.1093/nar/gku1293 ↗
35. 10.1038/emboj.2010.23910.1038/emboj.2010.239 ↗
36. 10.1016/j.ccell.2016.05.00810.1016/j.ccell.2016.05.008 ↗
37. 10.1038/s41467-020-16213-910.1038/s41467-020-16213-9 ↗
38. 10.1074/jbc.M112.36662510.1074/jbc.M112.366625 ↗
39. 10.1182/blood-2013-02-48348710.1182/blood-2013-02-483487 ↗
40. 10.1038/s41598-018-31635-810.1038/s41598-018-31635-8 ↗
41. 10.1074/jbc.RA119.01012610.1074/jbc.RA119.010126 ↗
42. 10.1038/nrc389510.1038/nrc3895 ↗
43. 10.1038/s41586-019-1534-310.1038/s41586-019-1534-3 ↗
44. 10.1038/s41588-021-00856-510.1038/s41588-021-00856-5 ↗
45. 10.1126/sciadv.abl522010.1126/sciadv.abl5220 ↗
46. 10.1128/MCB.00840-0810.1128/MCB.00840-08 ↗
47. 10.1074/jbc.RA120.01589610.1074/jbc.RA120.015896 ↗
48. 10.1158/0008-5472.CAN-19-241510.1158/0008-5472.CAN-19-2415 ↗
49. 10.1016/j.ccell.2019.12.00610.1016/j.ccell.2019.12.006 ↗
50. 10.1016/j.canlet.2020.11.03710.1016/j.canlet.2020.11.037 ↗
51. 10.1038/s41588-022-01119-710.1038/s41588-022-01119-7 ↗
52. Piccolo S. · Nat. Cancer. · 2023 · 4 · 9
53. 10.1093/nar/gkad10710.1093/nar/gkad107 ↗
54. 10.1186/s13148-021-01154-x10.1186/s13148-021-01154-x ↗
55. 10.1182/bloodadvances.202000292910.1182/bloodadvances.2020002929 ↗
56. 10.1093/nar/gkn29410.1093/nar/gkn294 ↗
57. 10.1023/B:BILE.0000044922.43572.2d10.1023/B:BILE.0000044922.43572.2d ↗