MATN3 (Matrilin-3) Antibody (Clone#:4B6)
- Known as:
- MATN3 (Matrilin-3) Antibody (Clone#:4B6)
- Catalog number:
- amm11595c
- Product Quantity:
- USD
- Category:
- -
- Supplier:
- Aviva Systems Biology
- Gene target:
- MATN3 (Matrilin-3) Antibody (Clone#:4B6)
Related genes to: MATN3 (Matrilin-3) Antibody (Clone#:4B6)
- Gene:
- MATN3 NIH gene
- Name:
- matrilin 3
- Previous symbol:
- -
- Synonyms:
- EDM5, HOA
- Chromosome:
- 2p24.1
- Locus Type:
- gene with protein product
- Date approved:
- 1998-04-29
- Date modifiied:
- 2016-10-05
Related products to: MATN3 (Matrilin-3) Antibody (Clone#:4B6)
Related articles to: MATN3 (Matrilin-3) Antibody (Clone#:4B6)
- Jeju native pig (JNP) is an indigenous breed originating from Korea, characterized by short black hair, small stature, and superior meat quality compared with commercial breeds. This study investigated meat quality and transcriptome differences in the muscles of Landrace and JNP pigs. Phenotypic analysis of meat quality traits was performed on each breed, revealing significant differences in cooking loss, crude fat, moisture, CIE L*, CIE a*, shear force, pH, hardness, gumminess, and chewiness (p<0.05). JNP exhibited significantly higher intramuscular fat content and CIE a* (p<0.001), which increases consumer preference, suggesting that JNP have superior meat quality traits. To understand transcript expression differences, indicating differences in meat quality characteristics and growth between the two breeds, RNA sequencing was performed on muscle samples from each breed. Overall, 427 differentially expressed genes (DEG) were upregulated in JNP, while 821 genes were upregulated in Landrace. Among these, , and were key candidate genes. Enrichment analysis in Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathways suggested DEG are involved in biological processes such as the cell cycle, extracellular space, collagen trimer, platelet-derived growth factor binding, and motor proteins. This study provides important insights into the genetic expression characteristics of Landrace and JNP and elucidates the mechanisms related to economic traits, such as superior meat quality and growth rate. Additionally, these findings provide foundational data for improving meat quality and suggest strategies for the genetic improvement of both breeds. - Source: PubMed
Publication date: 2025/11/01
Kim Na-YoungLee SanghoonKim Hyeon-AhKang Yong-JunCho In-CheolPark Jong-Eun - Multiple epiphyseal dysplasia (MED), caused by mutations in MATN3, is a chondrodysplasia affecting the cartilage growth plate and is characterised by delayed epiphyseal ossification, short stature, and early onset osteoarthritis. Here we generated an in vitro human pluripotent stem cell (hPSC) model of cartilage growth-plate development to identify pathogenic mechanisms underlying MED. - Source: PubMed
Publication date: 2026/02/04
Woods StevenBates NicolaCain StuartHumphreys Paul E AMancini Fabrizio EAguero Burgos BrendaHarley PeterAlqahtani Rayed Ali AKamprom WitchayaponMironov AleksandrAdamson AntonyDonaldson Ian JMortier GeertChandler KateNicolaou AnnaBaldock ClairSchwartz Jean-MarcKimber Susan J - The current cattle reference genome assembly, a pseudo-linear sequence produced using sequences from a single Hereford cow, represents a limitation when performing genetic studies, especially when investigating the whole spectrum of genetic variations within the species. Detecting structural variations (SVs) poses significant challenges when relying solely on conventional methods of sequencing read mapping to the current bovine genome assembly. - Source: PubMed
Publication date: 2025/10/23
Sorin ValentinNaji Maulana MughitzBirbes ClémentGrohs CécileEscouflaire ClémentineFritz SébastienEché CamilleMarcuzzo CamilleSuin AmandineDonnadieu CécileGaspin ChristineIampietro CaroleMilan DenisDrouilhet LaurenceTosser-Klopp GwenolaBoichard DidierKlopp ChristopheSanchez Marie-PierreBoussaha Mekki - Gastric cancer (GC) is a highly heterogeneous disease that requires highly accurate prognostic models. Machine learning is a powerful tool for identifying predictive biomarkers and developing prognostic models. Here, we aim to integrate bioinformatics and machine learning algorithms to construct a risk model to predict prognosis of GC patients. Transcriptome data and clinical information of GC patients were obtained from the Cancer Genome Atlas (TCGA) database. Microarray data (GSE84437 and GSE26253) were obtained from the Gene Expression Omnibus (GEO) database. Univariate Cox regression analysis was used to screen prognostic genes. The risk genes closely related to prognosis were screened by machine learning algorithms and the risk score was calculated. Kaplan-Meier survival curve, time-dependent receiver operating characteristic (ROC) curve, univariate and multivariate Cox regression analysis were used to verify the validity of the risk model. The protein expression of hub genes in GC tissues was evaluated by immunohistochemical staining. 7 hub genes (CGB5, FEM1A, MATN3, ZNF101, MARCKS, BRI3BP and APOD) were identified and correlated with GC prognosis. A high-precision risk model based on random survival forest (RSF) and generalized boosted regression modelling (GBM) was constructed using these 7 hub genes. The risk model has good predictive ability for GC patients' prognosis, and the risk score could be used as an independent prognostic factor for GC. In addition, the protein expression levels of CGB5, MATN3, MARCKS and APOD in GC tissues were significantly higher than those in normal tissues, and correlated with the pathological characteristics of GC patients. The risk model composed of 7 hub genes can accurately evaluate the prognosis of GC patients, which may contribute to the precise and personalized treatment of GC patients. - Source: PubMed
Publication date: 2025/10/14
Yang XueliHuang XuYing WangDeng TaoZhang JunDing Qianshan - Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal cancer, with drug resistance significantly impeding effective chemotherapy. The clinical importance of key genes in PDAC chemoresistance remains unclear. - Source: PubMed
Publication date: 2025/10/10
Qin ChengLi TianyuZhao BangboLi ZeruZhao YutongZhang XiangyuXie YipingYan YutongHan JiashuShao QianqianLin ChenWang Weibin