Deep learning identification of novel autophagic protein-protein interactions and experimental validation of Beclin 2-Ubiquilin 1 axis in triple-negative breast cancer
This study not only enhances our understanding of autophagy regulation in TNBC but also identifies the Beclin 2-Ubiquilin 1 axis as a promising target for precision therapy. These findings open new avenues for drug discovery and offer inspiration for more effective treatments for this aggressive cancer subtype.
Authors
- Lei Wu (ORCID: https://orcid.org/0000-0003-1875-7880)
- Weilin Jin (ORCID: https://orcid.org/0000-0001-8011-2405)
- Xin Xie (ORCID: https://orcid.org/0000-0003-2314-4800)
- Xiang Li (ORCID: https://orcid.org/0000-0001-6346-1574)
- Huan Wang (ORCID: https://orcid.org/0000-0003-2803-3317)
- Wei Huang (ORCID: https://orcid.org/0000-0002-7279-0932)
- Bo Liu (ORCID: https://orcid.org/0000-0003-1685-7968)
Institutions
- Sichuan University (CN)
- State Key Laboratory of Biotherapy
- Chengdu University of Traditional Chinese Medicine (CN)
Publication Details
- Journal
- Oncology Research Featuring Preclinical and Clinical Cancer Therapeutics
- Published
- 2024-11-19
- DOI
- https://doi.org/10.32604/or.2024.055921
- Citations
- 1
- Primary Topic
- Machine Learning in Bioinformatics
- Type
- article
- Field-Weighted Citation Impact
- 0.23