Background: As an invasive malignant tumor, osteosarcoma (OS) has high mortality. Parathyroid hormone receptor 1 (PTHR1) contributes to maintaining proliferation and undifferentiated state of OS. This study is designed to reveal the action mechanisms of PTHR1 in OS.
Methods: Microarray dataset GSE46861, which included six PTHR1 knockdown OS samples and six control OS samples, was obtained from the Gene Expression Omnibus database. The differentially expressed genes (DEGs) were identified and then performed with enrichment analysis separately using the limma package and DAVID online tool. Then, protein-protein interaction (PPI) network and module analyses were conducted using Cytoscape software. Using the WebGestalt tool, microRNAs (miRNAs) were predicted for the DEGs involved in the PPI network. Following this, transcription factors (TFs) were predicted and an integrated network was constructed by Cytoscape software.
Results: There were 871 DEGs in the PTHR1 knockdown OS samples compared with the control OS samples. Besides, upregulated ZFPM2 was involved in the miRNA-DEG regulatory network. Moreover, TF LEF1 was predicted for the miRNA-DEG regulatory network of the downregulated genes. In addition, LEF1, NR4A2, HAS2, and RHOC had higher degrees in the integrated network.
Conclusions: ZFPM2, LEF1, NR4A2, HAS2, and RHOC might be potential targets of PTHR1 in OS.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5609044 | PMC |
http://dx.doi.org/10.1186/s12957-017-1242-0 | DOI Listing |
J Biomol Struct Dyn
March 2025
School of Mechatronic Engineering and automation, Shanghai University, Shanghai, China.
Prediction of protein-ligand interactions is critical for drug discovery and repositioning. Traditional prediction methods are computationally intensive and limited in modeling structural changes. In contrast, data-driven deep learning methods significantly reduce computational costs and offer a more efficient approach for drug discovery.
View Article and Find Full Text PDFAdv Mater
March 2025
Institute of Biomedical Engineering, College of Medicine, Southwest Jiaotong University, Chengdu, 610031, China.
Patients with hand dysfunction require joint rehabilitation for functional restoration, and wearable electronics can provide physical signals to assess and guide the process. However, most wearable electronics are susceptible to failure under large deformations owing to instability in the layered structure, thereby weakening signal reliability. Herein, an in-situ self-welding strategy that uses dynamic hydrogen bonds at interfaces to integrate conductive elastomer layers into highly robust electronics is proposed.
View Article and Find Full Text PDFNanomaterials (Basel)
February 2025
School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044, China.
Exoskeletons play a crucial role in joint healthcare by providing targeted support and rehabilitation for individuals with musculoskeletal diseases. As an assistive device, the accurate monitoring of the user's joint signals and exoskeleton status using wearable sensors is essential to ensure the efficiency of conducting complex tasks in various scenarios. However, balancing sensitivity and stretchability in wearable devices for exoskeleton applications remains a significant challenge.
View Article and Find Full Text PDFCells
February 2025
Department of Microbiology, Immunology, and Molecular Genetics, UT Health Science Center, San Antonio, TX 78229, USA.
The family of forkhead box O (FoxO) transcription factors regulate cellular processes involved in glucose metabolism, stress resistance, DNA damage repair, and tumor suppression. FoxO transactivation activity is tightly regulated by a complex network of signaling pathways and post-translational modifications. While it has been well established that phosphorylation promotes FoxO cytoplasmic retention and inactivation, the mechanism underlying dephosphorylation and nuclear translocation is less clear.
View Article and Find Full Text PDFJ Chem Inf Model
March 2025
Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China.
With the rapid advancements in the field of fluorescent dyes, accurate prediction of optical properties and efficient retrieval of dye-related data are essential for effective dye design. However, there is a lack of tools for comprehensive data integration and convenient data retrieval. Moreover, existing prediction models mainly focus on a single property of fluorescent dyes and fail to account for the diverse fluorophores and solutions in a systematic manner.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!