Hnrnpc siRNA_Lentivectors
- Known as:
- Hnrnpc siRNA_Lentivectors
- Catalog number:
- i060807d
- Product Quantity:
- 500ng
- Category:
- -
- Supplier:
- ABM
- Gene target:
- Hnrnpc siRNA_Lentivectors
Related genes to: Hnrnpc siRNA_Lentivectors
- Gene:
- HNRNPC NIH gene
- Name:
- heterogeneous nuclear ribonucleoprotein C (C1/C2)
- Previous symbol:
- HNRPC
- Synonyms:
- hnRNPC
- Chromosome:
- 14q11.2
- Locus Type:
- gene with protein product
- Date approved:
- 1997-08-28
- Date modifiied:
- 2014-11-19
Related products to: Hnrnpc siRNA_Lentivectors
Related articles to: Hnrnpc siRNA_Lentivectors
- Persistent activation of the Wnt/β-catenin signaling pathway is a key driver of esophageal squamous cell carcinoma (ESCC) progression. Circular RNAs (circRNAs) have emerged as critical regulators of oncogenic signaling in cancer. However, how circRNAs contribute to the sustained activation of Wnt/β-catenin signaling in ESCC is poorly defined. - Source: PubMed
Publication date: 2026/05/28
Dai SuliZhang CongWei ZishuanLiu YaxinWen YangChen JinxiaLi XiaoyaWei SisiSun GuoguiZhao Lianmei - Diabetes is one of the most common and fastest-growing diseases worldwide, and diabetic atherosclerotic calcification is a frequent and fatal complication, the underlying mechanisms of which remain unclear. In this study, we investigated the mechanism by which HNRNPC regulates diabetic vascular calcification. - Source: PubMed
Publication date: 2026/05/25
Yang BoMao XiangRen LiqunLi LihuaShao ChenWang XinyueShi XinyangXu SuiningWang Zhongqun - Breast cancer (BC) is a highly heterogeneous malignancy and remains the leading cause of cancer-related mortality among women worldwide. Although advances in molecular classification and targeted therapies have improved outcomes for certain subtypes, robust prognostic biomarkers applicable across clinical contexts are still lacking. The CRISPR-Cas9 system offers a powerful platform for identifying cancer cell vulnerabilities and may facilitate the development of clinically relevant prognostic models. - Source: PubMed
Publication date: 2026/05/14
Xiao Wen-TaoHe Jun-YanYang DongXun Yi - Asthma is a chronic inflammatory disease characterized byimmune dysregulation. This study aimed to perform unbiased analysis of transcriptomic data to identify differentially expressed m6A-related genes in asthma, with a focus on exploring their potential as biomarkers and therapeutic targets. Gene Expression Omnibus (GEO) (GSE134544) dataset was analyzed to identify differentially expressed m6A-related genes. Functional enrichment analysis was performed clusterProfiler, immune infiltration profiling was conducted with CIBERSORT, and a competing endogenous RNA (ceRNA, including microRNA [miR] and lncRNA) network was constructed. Drug enrichment analysis was carried out using DSigDB, and molecular docking was utilized to assess the interaction between dabigatran and the METTL3 protein. From 192 differentially expressed genes, four m6A-related genes (METTL3, HNRNPC, IGFBP2, and RBMX) were identified as the intersecting genes between the m6A-related gene set and differentially expressed genes (DEGs) from the GSE134544 dataset. Gene Ontology (GO) analysis revealed significant enrichment in biological processes related to RNA metabolic processes and post-transcriptional regulation, while Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis identified important pathways such as spliceosome and p53 signaling pathways. METTL3 and HNRNPC were central in the ceRNA network, interacting with miRs such as hsa-miR-93-3p and lncRNAs like LINC01529. Drug enrichment analysis identified dabigatran as a potential METTL3 inhibitor, with molecular docking confirming a stable binding affinity (-5.9 kcal/mol). This study emphasizes the critical role of m6A-related genes, particularly METTL3 and HNRNPC, as macromolecules in asthma pathophysiology, and provides insights into their potential as biomarkers and therapeutic targets for asthma treatment. - Source: PubMed
Publication date: 2026/02/01
Jiang KaichongLi QiaoDuan LingZhu XieyingWu Shuang - RNA-binding proteins (RBPs) regulate every aspect of post-transcriptional gene expression, yet our ability to compare how selectively different RBPs recognize their targets remains limited. Binding affinity, expressed as a dissociation constant, provides a universal quantity for comparing binding strength, but no equivalent metric exists for binding specificity. Here we introduce two quantitative metrics to fill this gap: inherent specificity, which measures how selectively an RBP distinguishes its strongest binding motif from all other sequences, and variation sensitivity, which measures tolerance to single nucleotide changes within that motif. Analyzing high-throughput sequencing data across 100 RBPs and 27 in cells, we find strong correspondence between in vitro and cellular measurements for sequence-driven RBPs. Domain swap CLIP experiments demonstrate that specificity can be transferred between protein contexts. Mathematical modeling and cellular competition experiments reveal that low-specificity RBPs can paradoxically sharpen the target discrimination of high-specificity partners by occupying non-preferred sites, an emergent property not predictable from affinity alone. These metrics and accompanying R packages provide a practical framework for comparing RBP binding behaviors and modeling how RBPs compete for RNA targets across the transcriptome. - Source: PubMed
Publication date: 2026/04/29
Yi SoonSingh Shashi SYe XuanKrishna RohanKothwela VidushaJankowsky EckhardLuna Joseph M