ANKRD29
- Known as:
- ANKRD29
- Catalog number:
- 001627A
- Product Quantity:
- 250ul
- Category:
- -
- Supplier:
- ABM
- Gene target:
- ANKRD29
Related genes to: ANKRD29
- Gene:
- ANKRD29 NIH gene
- Name:
- ankyrin repeat domain 29
- Previous symbol:
- -
- Synonyms:
- FLJ25053
- Chromosome:
- 18q11.2
- Locus Type:
- gene with protein product
- Date approved:
- 2004-04-30
- Date modifiied:
- 2015-08-24
Related products to: ANKRD29
Related articles to: ANKRD29
- Plumage coloration in birds is a complex genetic trait involving both direct pigmentation genes and their modifiers. The Columbian pattern, characterized by black feathers restricted to the neck, wing tips, and tail, remains poorly understood at the genetic level. This study aimed to conduct a genome-wide association study of the Columbian pattern across 29 chicken breeds of diverse origins. Blood samples were collected from breeds with ( = 11) and without ( = 18) the Columbian pattern. Genotyping was performed using the Illumina Chicken 60K SNP chip, and GWAS was conducted using EMMAX with Bonferroni correction. A total of 10 significant and nine suggestive SNPs on chromosomes GGA1, 2, 5, 7, 10, and 11 were identified. Eight significant SNPs mapped to a ~0.7 Mb region on GGA11 containing and multiple functionally diverse genes involved in melanocyte adhesion (), signaling (, ), transcription (, ), protein degradation (, ), and vesicular trafficking (). On GGA2, a significant SNP within a lncRNA gene was located in a QTL for yellow plumage, with positional candidates (, , , , ) suggesting links to pheomelanin deposition. A suggestive locus near on GGA5 was also identified. This study refines the genetic architecture of the Columbian pattern, implicating a core region on GGA11 modulating melanocyte function and a distinct locus on GGA2 involved in pheomelanin synthesis. - Source: PubMed
Publication date: 2026/07/11
Azovtseva Anastasiia IRyabova Anna EShcherbakov Yuri SLarkina Tatiana AVakhrameev Anatoly BDementieva Natalia V - The scarcity of reliable biomarkers and predictive models for platinum resistance in lung adenocarcinoma (LUAD) poses a significant clinical challenge. This study endeavors to identify molecular subtypes related to platinum resistance and construct a robust predictive model through multi-omics techniques. We performed integrative analysis of public datasets using advanced bioinformatics strategies, including spatial transcriptome deconvolution and consensus clustering. Bulk RNA deconvolution analysis was conducted to characterize tumor microenvironment heterogeneity. Feature selection was performed using the Supervised Principal Component (SuperPC) algorithm, followed by diagnostic model construction validated through receiver operating characteristic (ROC) analysis. Functional validation was performed through cytological experiments measuring cisplatin IC50 alterations following gene manipulation in LUAD cell lines. Consensus clustering revealed distinct LUAD subtypes, with Cluster1 demonstrating significant platinum resistance. We first subtyped the patients in the bulk transcriptome data based on consistency clustering, and then analyzed the differences between different platinum-resistant subtypes (Cluster 1 and Cluster 2), so as to screen 333 isotype-specific differentially expressed genes and 15 platinum resistance-related (PRR) genes were selected through machine learning. A refined 5-gene signature (ANKRD29/CACNA2D2/DSP/HSD17B6/SPP1) achieved exceptional predictive performance (AUC = 0.9639). Spatial transcriptomics demonstrated compartmentalized expression patterns: SPP1/DSP localized to tumor niches, HSD17B6/CACNA2D2 to epithelial regions, and ANKRD29 depletion in stromal areas. Cellular colocalization analysis revealed malignant epithelial PH proximity to myeloid and mast cells. Functional validation confirmed that ANKRD29/CACNA2D2 overexpression sensitized A549/DDP cells to cisplatin, while DSP/SPP1/HSD17B6 overexpression induced resistance. Experiments in nude mice have shown that these genes are closely related to cisplatin resistance in LUAD. This study identifies the Cluster1 subtype and malignant epithelial PH as crucial determinants of platinum resistance in LUAD. Our innovative 5-gene predictive model exhibits clinical-grade diagnostic accuracy, and spatial transcriptomic characterization offers mechanistic insights into the dynamics of the tumor microenvironment. - Source: PubMed
Publication date: 2026/02/07
Chen JieChen YixinLu YiHe YuyuJiang FengHu LijuanWang Yumin - This study aims to identify key modules and targets during the transition from gastric precancerous lesions to gastric cancer by performing weighted gene co-expression network analysis (WGCNA) on gene microarray datasets from the Gene Expression Omnibus (GEO) database containing gastritis, gastric cancer and precancerous lesions, providing insights for early intervention in gastric cancer. - Source: PubMed
Publication date: 2025/11/25
Li HengLi WenYang ZhenLiu HaiyuZhang XiaopingZhao YufengGu Hao - Studies have shown that patients with periodontitis (PD) have an increased risk of breast cancer (BC). However, the exact mechanism remains to be further investigated. This study aimed to investigate the genes, pathways and immune cells that may interact with PD and BC. From the Gene Expression Omnibus (GEO) and TCGA databases, we retrieved the gene expression profiles of samples with PD and BC, respectively. Common genes between two diseases were found using differential expression analysis and weighted gene co-expression network analysis (WGCNA). Machine learning methods were used to find shared diagnostic genes. Single-sample GSEA (ssGSEA) was performed to study the expression profiles of 28 immune cells in PD and BC, and single-cell RNA sequencing (scRNA-seq) data was used to visualize localization of shared genes. Finally, we employed qRT-PCR and immunohistochemistry staining to confirm the expression of hub genes in two diseases. PD and BC had 21 shared crosstalk genes, which were primarily related to peptide hormone response, organic acid transmembrane transport, and carboxylic acid transmembrane transport. By using machine learning methods, ANKRD29 and TDO2 were the most efficient shared diagnostic biomarkers, which were confirmed by Immunohistochemical staining and qRT-PCR. ssGSEA showed that immunology was involved in both diseases and that ANKRD29 and TDO2 may be involved in both diseases by mediating immune cells. scRNA-seq further confirms the importance of these genes in regulating immunity in both diseases. In brief, our study identified 2 genes that may serve as biomarkers and targets for the diagnosis and treatment of PD and BC. - Source: PubMed
Publication date: 2025/04/02
Wu ErliLiang JiahuiZhao JingxinGu FeihanZhang YuanyuanHong BiaoWang QingqingShao WeiSun Xiaoyu - Milk production is the most important economic trait of dairy goats and a key indicator for genetic improvement and breeding. However, milk yield is a complex phenotypic trait, and its genetic mechanisms are still not fully understood. This study focuses on dairy goats and non-dairy goats. By analyzing the population structure of these two groups, we found that there is a significant genetic distance between the populations of dairy goats and non-dairy goats. Using SNP and Indel analyses to identify selection signals, we identified several genes associated with milk production traits, including MPP7, PRPF6, DNAJC5, TPD52L2, HNF4G, LAMA3, FAM13A, and EPHA5. Through longitudinal GWAS of the milk production traits of 298 dairy goats, we discovered additional genes such as TRNAS-GGA-102, TTC39C, LAMA3, ANKRD29, NPC1, C24H18orf8, LOC108633789, RIOK3, TMEM241, CABLES1, LOC108633781, and RBBP8. Transcriptome sequencing of breast tissues at different lactation stages reveals dynamic LAMA3 expression changes. Three non-synonymous mutations in LAMA3 are identified, with the TT genotype at one site correlating significantly with average milk production in dairy goats. Our study discovered new genetic markers for improving dairy goat genetics and provided valuable insights into the genetic mechanisms underlying complex traits. - Source: PubMed
Publication date: 2024/12/28
Zhao JianqingShi ChenboKamalibieke JiayidaerGong PingMu YuanpanZhu LuLv XuefengWang WeiLuo Jun