Ask about this productRelated genes to: Prdm12 Blocking Peptide
- Gene:
- PRDM12 NIH gene
- Name:
- PR/SET domain 12
- Previous symbol:
- -
- Synonyms:
- PFM9
- Chromosome:
- 9q34.12
- Locus Type:
- gene with protein product
- Date approved:
- 2000-11-28
- Date modifiied:
- 2018-03-02
Related products to: Prdm12 Blocking Peptide
Related articles to: Prdm12 Blocking Peptide
- Charcot–Marie–Tooth disease and related neuropathies (CMTR) are heterogeneous inherited neuropathies with variable progression, yet data on pediatric disease progression by neurophysiological subtype are limited. We evaluated 1-year clinical and functional changes in children with CMTR and compared axonal versus demyelinating trajectories. - Source: PubMed
Publication date: 2026/04/21
Inmongkol ChonladaVorasan NutchavadeeLimpaninlachat SivapornSereephaowong NiramonKulsirichawaroj PimchanokSanmaneechai OraneeBurns Joshua - Aggressive behavior in Muscovy ducks (Cairna moschata) has become a predominant concern in intensive farming systems, leading to reduced animal welfare and production losses. To unravel the molecular mechanisms underlying this behavior, transcriptomic profiling was performed on the hypothalamus, a key regulatory hub for aggressive responses. A total of 120 healthy 60-day-old female Muscovy ducks were continuously monitored for 24 h/day over one month using Media Recorder 2.0 software. Based on instantaneous and continuous behavioral observations, the ducks were categorized into three groups: aggressor (Experimental group I, actively attacking conspecifics), victim (Experimental group II, receiving aggression), and non-aggressive (Control group, no aggressive interactions). Hypothalamic tissues were collected from each group (n = 4 per group) for Illumina HiSeq 2000 high-throughput transcriptome sequencing. Functional annotation and enrichment analysis of differentially expressed genes (DEGs) were performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases, followed by quantitative real-time PCR (qRT-PCR) validation. GO analysis identified 626 DEGs in the aggressor group and 649 DEGs in the victim group compared to the control group, with 26 DEGs directly involved in aggressive behavior regulation. Integration of GO and KEGG annotations revealed 69 candidate genes associated with aggressive behavior, enriched in two GO terms (behavior [GO:0007610] and sensory perception of pain [GO:0019233]) and the ERBB signaling pathway (map04012). qRT-PCR validation of 14 randomly selected candidate genes (e.g., NPY, ERBB4, MAPK9, PRDM12) confirmed that their expression patterns were consistent with transcriptomic data, verifying the reliability of the sequencing results. These findings provide novel insights into the molecular genetic basis of aggressive behavior in Muscovy ducks and lay a foundation for developing targeted strategies to mitigate aggression in intensive farming systems. - Source: PubMed
Publication date: 2026/03/18
Liu AiWang XupingZhou XuanYao BiqiongZhu JinjinRao YifuLiao FuyouYao BingnongBoonanuntan SurintornYang Shenglin - Congenital insensitivity to pain (CIP) is a rare sensory neuropathy marked by absent nociception that predisposes patients to injuries and complications. Variants in genes, particularly , underlie the condition. We investigated the molecular basis of CIP in 2 unrelated families. - Source: PubMed
Publication date: 2026/02/02
Pho-Iam TheeraphongKulsirichawaroj PimchanokLikasitwattanakul SurachaiRidchuayrod NumpuengSanmaneechai OraneeLimwongse ChaninZuchner Stephan - - Source: PubMed
Publication date: 2026/01/08
Akash SrivastavaPriyanka BhatnagarPardal P K - Hepatocellular carcinoma (HCC), as a cancer with high morbidity and mortality, urgently requires the development of a clinical prediction model with high robustness and generalizability and its prognostic study of the tumor microenvironment to provide personalized clinical treatment for patients. Key prognostic genes were screened by analyzing mRNA expression data from GTEx and The Cancer Genome Atlas (TCGA) using limma difference analysis, Cox analysis, and machine learning (ML) algorithms. TCGA database was used as a training set, and the International Cancer Genome Consortium database was used as a test set to screen the best prognostic modeling algorithms using a combination of 101 ML algorithms for training and constructing Nomo score plots based on the algorithmic risk scores as well as Shiny online prediction models. Based on shapley additive explanations analysis, drug sensitivity analysis, and immune infiltration analysis were performed on the 6 genes screened to visualize the importance of prognostic genes. HCC tumor mutation load analysis was also performed. A risk prediction model for HCC death was developed based on the RSF algorithm, with an RSF model C-index of 0.765 and AUC values of 0.978, 0.989, and 0.964 for 1-, 3-, and 5-year ROC curves for the Nomo score model, respectively. LPL, RAET1E, RNASEH2A, GTF2H4, SCML2, and PRDM12 were potential diagnostic and prognostic markers, among which SCML2 and PRDM12 were significantly correlated with multiple drugs in drug sensitivity analysis.TP53 mutations were correlated with patients' age, chronological age, gender, histological tumor stage, T stage, and lymph node metastasis. An online HCC mortality risk prediction model was developed using the RSF algorithm. LPL, RAET1E, RNASEH2A, GTF2H4, SCML2, and PRDM12 are potential prognostic target genes, whereas TP53 mutations are associated with clinical features that may inform the development of HCC therapy. - Source: PubMed
Wang JiamingShen TongpingWang Shihao