The ER-α36 isoform is expressed in both ER-positive and ER-negative breast cancer cell lines. ER-α36 is implicated in promoting the proliferation, invasion, and metastasis of tumor cells. Runt-related transcription factor 2 (RUNX2) is widely recognized as a driver of bone-specific metastasis in breast cancer. The study aimed to examine the association between ER-α36 and RUNX2 in breast cancer and its relationship with cancer development. Immunohistochemical analysis of 25 cases revealed subcellular localization patterns of ER-α36 and RUNX2. ER-α36 showed nuclear positivity in 5 cases (20%), cytoplasmic/membranous positivity in 10 cases (40%), and combined nuclear/cytoplasmic positivity in 4 cases (16%). RUNX2 exhibited nuclear positivity in 5 cases (20%), cytoplasmic/membranous positivity in 9 cases (36%), and combined nuclear/cytoplasmic positivity in 6 cases (24%). Case-by-case analysis revealed that in specimens with nuclear ER-α36 positivity, RUNX2 was predominantly localized in the nucleus or nucleus/cytoplasm. Both proteins were predominantly expressed in grade 2 tumors. In vitro experiments, Knockdown of RUNX2 significantly reduced ER-α36 expression by 3.2-fold in MDA-MB-231 cells and by 8.9-fold in MCF-10ACE cells (**p < 0.01 and ***p < 0.001, respectively), whereas ER-α36 knockdown significantly decreased RUNX2 expression by 2.3-fold in MDA-MB-231 cells (*p < 0.05). The results of co-immunoprecipitation showed that the two proteins were significantly associated in MCF-10ACE and MDA-MB-231 cells. The luciferase reporter assay revealed that down-regulation of RUNX2 inhibited the binding of ER-α36 to estrogen response elements. These results suggest that RUNX2 interacts with ER-α36 and facilitates its transcriptional activation.
The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability, which underlines the importance of the technical and security perspectives for research investigating the adoption of technology.
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It is demonstrated that linker-free PROTACs can outperform traditional designs, marking a paradigm shift in PROTAC development for targeted protein degradation.
Pinal, a 16-billion-parameter foundation model that produces protein candidates from natural-language functional descriptions, supports natural language as a high-level interface for candidate generation in protein design, enabling programmable exploration with reduced reliance on manually specified structural or sequence constraints.
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.