Ask about this productRelated genes to: KIAA1704 antibody
- Gene:
- GPALPP1 NIH gene
- Name:
- GPALPP motifs containing 1
- Previous symbol:
- KIAA1704
- Synonyms:
- bA245H20.2, AD029, LSR7
- Chromosome:
- 13q14.12
- Locus Type:
- gene with protein product
- Date approved:
- 2004-04-16
- Date modifiied:
- 2015-08-25
Related products to: KIAA1704 antibody
Related articles to: KIAA1704 antibody
- The tooth serves as an exemplary model for developmental studies, encompassing epithelial-mesenchymal transition and cell differentiation. The essential factors and pathways identified in tooth development will help understand the natural development process and the malformations of mineralized tissues such as skeleton. The time-dependent proteomic changes were investigated through the proteomics of healthy human molars during embryonic stages, ranging from the cap-to-early bell stage. A comprehensive analysis revealed 713 differentially expressed proteins (DEPs) exhibiting five distinct temporal expression patterns. Through the application of weighted gene co-expression network analysis (WGCNA), 24 potential driver proteins of tooth development were screened, including CHID1, RAP1GDS1, HAPLN3, AKAP12, WLS, GSS, DDAH1, CLSTN1, AFM, RBP1, AGO1, SET, HMGB2, HMGB1, ANP32A, SPON1, FREM1, C8B, PRPS2, FCHO2, PPP1R12A, GPALPP1, U2AF2, and RCC2. Then, the proteomics and transcriptomics expression patterns of these proteins were further compared, complemented by single-cell RNA-sequencing (scRNA-seq). In summary, this study not only offers a wealth of information regarding the molecular intricacies of human embryonic epithelial and mesenchymal cell differentiation but also serves as an invaluable resource for future mechanistic inquiries into tooth development. - Source: PubMed
Publication date: 2024/03/24
Chen XiaohangLi GaochiZhang JianHu LiangZhao GuoqiangWu BulingWei FengxiangXiong Fu - The aim of this study was to construct a model used for the accurate diagnosis of Atopic dermatitis (AD) using pyroptosis related biological markers (PRBMs) through the methods of machine learning. - Source: PubMed
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