A5102772466- Radiopharmaceutical Chemistry and Applications
- Nanoplatforms for cancer theranostics
- S100 Proteins and Annexins
- Graphene and Nanomaterials Applications
- Immune cells in cancer
- Esophageal and GI Pathology
- Fibroblast Growth Factor Research
- Mitochondrial Function and Pathology
- RNA modifications and cancer
- Cancer Cells and Metastasis
- Bioinformatics and Genomic Networks
- Ubiquitin and proteasome pathways
- Cancer, Hypoxia, and Metabolism
- Gene expression and cancer classification
- Cancer-related Molecular Pathways
- Esophageal Cancer Research and Treatment
- Nanoparticle-Based Drug Delivery
- 14-3-3 protein interactions
- Glycosylation and Glycoproteins Research
- RNA Research and Splicing
Tianjin Medical University Cancer Institute and Hospital
2012-2025
Northwestern Polytechnical University
2024
Chinese Academy of Sciences
2023
Changchun Institute of Applied Chemistry
2023
Tianjin University of Traditional Chinese Medicine
2022
National Public Health Laboratory
2007-2012
Abstract Objective The metastatic ability of breast cancer cells with chemoresistant properties is higher when compared to that their parental wild‐type cells. Expression AnnexinA2 (Anxa2), a 36‐kDa calcium‐dependent phospholipid binding protein, increased in tumours and has been found be associated the phenotype drug resistance metastasis. Materials Methods Results In present study, we up‐regulation Anxa2 correlates enhanced migration invasion MCF ‐7 both vitro vivo . Western blot analysis...
Objective: ANXA2 plays a crucial role in cancer metastasis, but its mechanism is not yet fully understood. Therefore, it necessary to establish an gene knockout mouse model provide effective tool for subsequent studies on ANXA2-related mechanisms. Methods: A was constructed using CRISPR/Cas9 technology. The validated through tissue DNA extraction followed by polymerase chain reaction (PCR), sequencing, and western blot confirm genotype protein expression. successfully models were divided...
Identifying cancer genes is vital for diagnosis and treatment. However, because of the complexity occurrence limited knowledge, it hard to identify accurately using only a few omics data, overall performance existing methods being called further improvement. Here, we introduce two-stage gradual-learning strategy GLIMS predict integrative features from multi-omics data. Firstly, uses semi-supervised hierarchical graph neural network initial candidate by integrating data protein-protein...