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Last update 08 May 2025

POLR2J

Last update 08 May 2025

Basic Info

Synonyms

DNA-directed RNA polymerase II subunit J-1, DNA-directed RNA polymerase II subunit RPB11-a, hRPB14

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Introduction

Core component of RNA polymerase II (Pol II), a DNA-dependent RNA polymerase which synthesizes mRNA precursors and many functional non-coding RNAs using the four ribonucleoside triphosphates as substrates.

Drugs associated with POLR2J

HG-1078

Target

POLR2J

Mechanism

POLR2J modulators

Active Org.-

Originator Org.

Human Genome Sciences, Inc.

Active Indication-

Inactive Indication

Neoplasms

Drug Highest PhasePending

First Approval Ctry. / Loc.-

First Approval Date20 Jan 1800

100 Clinical Results associated with POLR2J

100 Translational Medicine associated with POLR2J

0 Patents (Medical) associated with POLR2J

Literatures (Medical) associated with POLR2J

01 Jan 2024·The Journal of Gene Medicine

Comprehensive transcriptomic analysis and machine learning reveal unique gene expression profiles in patients with immune‐mediated necrotizing myopathy

Article

Author: Liu, Hongjiang ; Yin, Geng ; Chen, Bo ; Zhang, Jiaqian ; Xie, Qibing ; Liu, Huan ; Guo, Yixue ; Ran, Jingjing ; Deng, Lin

AbstractBackgroundImmune‐mediated necrotizing myopathy (IMNM) is an autoimmune myopathy characterized by severe proximal weakness and muscle fiber necrosis, yet its pathogenesis remains unclear. So far, there are few bioinformatics studies on underlying pathogenic genes and infiltrating immune cell profiles of IMNM. Therefore, we aimed to characterize differentially expressed genes (DEGs) and infiltrating cells in IMNM muscle biopsy specimens, which may be useful for elucidating the pathogenesis of IMNM.MethodsThree datasets (GSE39454, GSE48280 and GSE128470) of gene expression profiling related to IMNM were obtained from the Gene Expression Omnibus database. Data were normalized, and DEG analysis was performed using the limma package. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of DEGs were performed using clusterProfiler. The CIBERSORT algorithm was performed to identify infiltrating cells. Machine learning algorithm and gene set enrichment analysis (GSEA) were performed to find distinctive gene signatures and the underlying signaling pathways of IMNM.ResultsDEG analysis identified upregulated and downregulated in IMNM muscle compared to the gene expression levels of other groups. GO and KEGG analysis showed that the pathogenesis of IMNM was notable for the under‐representation of pathways that were important in dermatomyositis and inclusion body myositis. Three immune cells (M2 macrophages, resting dendritic cells and resting natural killer cells) with differential infiltration and five key genes (NDUFAF7, POLR2J, CD99, ARF5 and SKAP2) in patients with IMNM were identified through the CIBERSORT and machine learning algorithm. The GSEA results revealed that the key genes were remarkably enriched in diverse immunological and muscle metabolism‐related pathways.ConclusionsWe comprehensively explored immunological landscape of IMNM, which is indicative for the research of IMNM pathogenesis.

20 Sep 2023·Briefings in bioinformatics

A workflow to study mechanistic indicators for driver gene prediction with Moonlight.

Article

Author: Nourbakhsh, Mona ; Chen, Xi Steven ; Saksager, Astrid ; Colaprico, Antonio ; Olsen, Catharina ; Tom, Nikola ; Tiberti, Matteo ; Papaleo, Elena

Prediction of driver genes (tumor suppressors and oncogenes) is an essential step in understanding cancer development and discovering potential novel treatments. We recently proposed Moonlight as a bioinformatics framework to predict driver genes and analyze them in a system-biology-oriented manner based on -omics integration. Moonlight uses gene expression as a primary data source and combines it with patterns related to cancer hallmarks and regulatory networks to identify oncogenic mediators. Once the oncogenic mediators are identified, it is important to include extra levels of evidence, called mechanistic indicators, to identify driver genes and to link the observed gene expression changes to the underlying alteration that promotes them. Such a mechanistic indicator could be for example a mutation in the regulatory regions for the candidate gene. Here, we developed new functionalities and released Moonlight2 to provide the user with a mutation-based mechanistic indicator as a second layer of evidence. These functionalities analyze mutations in a cancer cohort to classify them into driver and passenger mutations. Those oncogenic mediators with at least one driver mutation are retained as the final set of driver genes. We applied Moonlight2 to the basal-like breast cancer subtype, lung adenocarcinoma and thyroid carcinoma using data from The Cancer Genome Atlas. For example, in basal-like breast cancer, we found four oncogenes (COPZ2, SF3B4, KRTCAP2 and POLR2J) and nine tumor suppressor genes (KIR2DL4, KIF26B, ARL15, ARHGAP25, EMCN, GMFG, TPK1, NR5A2 and TEK) containing a driver mutation in their promoter region, possibly explaining their deregulation. Moonlight2R is available at https://github.com/ELELAB/Moonlight2R.

01 Jan 2023·Frontiers in genetics

Using multi-tissue transcriptome-wide association study to identify candidate susceptibility genes for respiratory infectious diseases.

Article

Author: Li, Juntong ; Tu, Junlan ; Zou, Yanzheng ; Jia, Linna ; Zhu, Xiaobo ; Huang, Peng ; Ye, Xiangyu ; Yang, Sheng ; Yu, Rongbin ; Zou, Yixin

Objective: We explore the candidate susceptibility genes for influenza A virus (IAV), measles, rubella, and mumps and their underlying biological mechanisms. Methods: We downloaded the genome-wide association study summary data of four virus-specific immunoglobulin G (IgG) level data sets (anti-IAV IgG, anti-measles IgG, anti-rubella IgG, and anti-mumps virus IgG levels) and integrated them with reference models of three potential tissues from the Genotype-Tissue Expression (GTEx) project, namely, whole blood, lung, and transformed fibroblast cells, to identify genes whose expression is predicted to be associated with IAV, measles, mumps, and rubella. Results: We identified 19 significant genes (ULK4, AC010132.11, SURF1, NIPAL2, TRAP1, TAF1C, AC000078.5, RP4-639F20.1, RMDN2, ATP1B3, SRSF12, RP11-477D19.2, TFB1M, XXyac-YX65C7_A.2, TAF1C, PCGF2, and BNIP1) associated with IAV at a Bonferroni-corrected threshold of p < 0.05; 14 significant genes (SOAT1, COLGALT2, AC021860.1, HCG11, METTL21B, MRPL10, GSTM4, PAQR6, RP11-617D20.1, SNX8, METTL21B, ANKRD27, CBWD2, and TSFM) associated with measles at a Bonferroni-corrected threshold of p < 0.05; 15 significant genes (MTOR, LAMC1, TRIM38, U91328.21, POLR2J, SCRN2, Smpd4, UBN1, CNTROB, SCRN2, HOXB-AS1, SLC14A1, AC007566.10, AC093668.2, and CPD) associated with mumps at a Bonferroni-corrected threshold of p < 0.05; and 13 significant genes (JAGN1, RRP12, RP11-452K12.7, CASP7, AP3S2, IL17RC, FAM86HP, AMACR, RRP12, PPP2R1B, C11orf1, DLAT, and TMEM117) associated with rubella at a Bonferroni-corrected threshold of p < 0.05. Conclusions: We have identified several candidate genes for IAV, measles, mumps, and rubella in multiple tissues. Our research may further our understanding of the pathogenesis of infectious respiratory diseases.

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POLR2J

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