C57BL/6JCya-Polr2kem1/Cya
Common Name:
Polr2k-KO
Product ID:
S-KO-18383
Background:
C57BL/6JCya
Product Type
Age
Genotype
Sex
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Basic Information
Strain Name
Polr2k-KO
Strain ID
KOCMP-17749-Polr2k-B6J-VB
Gene Name
Product ID
S-KO-18383
Gene Alias
ABC10-alpha; MafY; Mt1a; RPABC4; RPB10alpha; RPB12; RPB7.0
Background
C57BL/6JCya
NCBI ID
Modification
Conventional knockout
Chromosome
15
Phenotype
Document
Application
--
Note: When using this mouse strain in a publication, please cite “C57BL/6JCya-Polr2kem1/Cya mice (Catalog S-KO-18383) were purchased from Cyagen.”
Strain Description
Ensembl Number
ENSMUST00000057177
NCBI RefSeq
NM_001039368
Target Region
Exon 2~3
Size of Effective Region
~1.6 kb
Detailed Document
Overview of Gene Research
Polr2k, also known as RNA polymerase II subunit K, is a component of the RNA polymerase II complex. This complex is crucial for transcribing DNA into messenger RNA, thus playing a fundamental role in gene expression, a process vital for various biological functions and the normal operation of cells [4,5,6].
In various disease-related studies, Polr2k has emerged as a potentially important gene. In nonspecific orbital inflammation (NSOI), it was identified as one of the seven purine metabolism-related genes (PMGs) closely associated with the disease. Functional analyses indicated its possible involvement in processes like peroxisome targeting sequence binding, seminiferous tubule development, and ciliary transition zone organization, and it showed promising diagnostic capabilities in differentiating NSOI from non-affected states [1].
In chronic kidney disease (CKD), Polr2k was screened from the co-expression network in peripheral blood mononuclear cells (PBMC), and its correlation with clinical parameters such as serum creatinine levels and estimated glomerular filtration rate demonstrated its clinical relevance [2].
In Parkinson's disease, through integrated analysis of single-cell RNA sequencing and bulk transcriptome data, Polr2k was identified as one of the pyroptosis-related diagnostic genes, and a diagnostic model based on it showed good performance [3].
In pneumonia, it was determined as an important protein-encoding gene in the protein-protein interaction (PPI) network constructed from predicted target genes of differentially expressed miRNAs [4].
In hepatocellular carcinoma (HCC), POLR2K was among the genes involved in transcription and protein biosynthesis that were up-regulated [6].
In mantle cell lymphoma (MCL), POLR2K was identified as a hub gene in the top weighted network, and the blue module containing it might play a vital role in MCL pathogenesis [7].
In colorectal cancer, POLR2K was identified as a hub gene among the HSF4 methylation-related genes, with HSF4 methylation potentially being one of the ways to mediate the CRC process [8].
In breast cancer, POLR2K was ranked as one of the best cancer immunotherapy-related proteins predicted by a model using molecular descriptors and artificial neural networks [9].
In conclusion, Polr2k, as an important part of the RNA polymerase II complex, is involved in fundamental gene-expression processes. Its role in various diseases such as NSOI, CKD, Parkinson's disease, pneumonia, HCC, MCL, colorectal cancer, and breast cancer has been revealed through multiple studies. These findings contribute to a better understanding of the molecular mechanisms of these diseases and may potentially lead to new diagnostic and therapeutic strategies.
References:
1. Wu, Zixuan, Fang, Chi, Hu, Yi, Yao, Xiaolei, Peng, Qinghua. 2024. Bioinformatic validation and machine learning-based exploration of purine metabolism-related gene signatures in the context of immunotherapeutic strategies for nonspecific orbital inflammation. In Frontiers in immunology, 15, 1318316. doi:10.3389/fimmu.2024.1318316. https://pubmed.ncbi.nlm.nih.gov/38605967/
2. Xia, Jia, Hou, Yutong, Cai, Anxiang, Huang, Masha, Mou, Shan. 2023. An integrated co-expression network analysis reveals novel genetic biomarkers for immune cell infiltration in chronic kidney disease. In Frontiers in immunology, 14, 1129524. doi:10.3389/fimmu.2023.1129524. https://pubmed.ncbi.nlm.nih.gov/36875100/
3. Wang, Lin, Qin, Yidan, Song, Jia, Li, Jia, Chen, Jiajun. 2024. Integrated analysis of single-cell RNA sequencing and bulk transcriptome data identifies a pyroptosis-associated diagnostic model for Parkinson's disease. In Scientific reports, 14, 28548. doi:10.1038/s41598-024-80185-9. https://pubmed.ncbi.nlm.nih.gov/39558055/
4. Huang, Sai, Feng, Cong, Zhai, Yong-Zhi, Lv, Fa-Qin, Li, Tan-Shi. 2017. Identification of miRNA biomarkers of pneumonia using RNA-sequencing and bioinformatics analysis. In Experimental and therapeutic medicine, 13, 1235-1244. doi:10.3892/etm.2017.4151. https://pubmed.ncbi.nlm.nih.gov/28413462/
5. Bhandari, Nikita, Acharya, Disha, Chatterjee, Annesha, Malakar, Pushkar, Shukla, Sudhanshu K. 2023. Pan-cancer integrated bioinformatic analysis of RNA polymerase subunits reveal RNA Pol I member CD3EAP regulates cell growth by modulating autophagy. In Cell cycle (Georgetown, Tex.), 22, 1986-2002. doi:10.1080/15384101.2023.2265676. https://pubmed.ncbi.nlm.nih.gov/37795959/
6. Liu, Yuefang, Zhu, Xiaojing, Zhu, Jin, Zhang, Jianping, Feng, Zhenqing. . Identification of differential expression of genes in hepatocellular carcinoma by suppression subtractive hybridization combined cDNA microarray. In Oncology reports, 18, 943-51. doi:. https://pubmed.ncbi.nlm.nih.gov/17786358/
7. Guo, Dongmei, Wang, Hongchun, Sun, Li, Li, Chunpu, Teng, Qingliang. 2020. Identification of key gene modules and hub genes of human mantle cell lymphoma by coexpression network analysis. In PeerJ, 8, e8843. doi:10.7717/peerj.8843. https://pubmed.ncbi.nlm.nih.gov/32219041/
8. Zhang, Wen-Jing, Yue, Ke-Lin, Wang, Jing-Zhai, Zhang, Yu. . Association between heat shock factor protein 4 methylation and colorectal cancer risk and potential molecular mechanisms: A bioinformatics study. In World journal of gastrointestinal oncology, 15, 2150-2168. doi:10.4251/wjgo.v15.i12.2150. https://pubmed.ncbi.nlm.nih.gov/38173437/
9. López-Cortés, Andrés, Cabrera-Andrade, Alejandro, Vázquez-Naya, José M, Tejera, Eduardo, Munteanu, Cristian R. 2020. Prediction of breast cancer proteins involved in immunotherapy, metastasis, and RNA-binding using molecular descriptors and artificial neural networks. In Scientific reports, 10, 8515. doi:10.1038/s41598-020-65584-y. https://pubmed.ncbi.nlm.nih.gov/32444848/
Quality Control Standard
Sperm Test
Pre-cryopreservation: Measurement of sperm concentration, determination of sperm viability.
Post-cryopreservation: A vial of cryopreserved sperms is selected for in-vitro fertilization from each batch.
Environmental Standards:SPF
Available Region:Global
Source:Cyagen