Ask about this productRelated genes to: RNASEH2A antibody
- Gene:
- RNASEH2A NIH gene
- Name:
- ribonuclease H2 subunit A
- Previous symbol:
- -
- Synonyms:
- RNASEHI, RNHIA, RNHL, AGS4
- Chromosome:
- 19p13.13
- Locus Type:
- gene with protein product
- Date approved:
- 2002-06-05
- Date modifiied:
- 2019-04-23
Related products to: RNASEH2A antibody
Related articles to: RNASEH2A antibody
- Triple-negative breast cancer (TNBC) lacks effective targeted therapies and carries a poor prognosis. TNBC cells escape oncogene-induced senescence and adapt to elevated replication stress. Here, we show that cells escaping senescence depend on overexpression of RNase H2, which removes misincorporated ribonucleotides from genomic DNA. RNASEH2A, the catalytic subunit of RNase H2, is overexpressed in TNBC tumors and correlates with poor survival. Genetic silencing or pharmacological inhibition of RNase H2 selectively impairs TNBC viability, spares non-tumorigenic mammary epithelial cells, and suppresses tumor growth in vivo. Mechanistically, RNase H2 inhibition increases replication stress, DNA damage, and cytosolic single-stranded DNA accumulation, triggering innate immune activation and upregulation of T cell-recruiting chemokines. RNase H2 inhibition synergizes with ATR and PARP inhibitors and enhances immune checkpoint blockade efficacy. Together, these findings identify RNase H2 as a therapeutic vulnerability in TNBC and support combined strategies integrating DNA damage modulation and immunotherapy. - Source: PubMed
Publication date: 2026/04/20
Nguyen Thai Quynh AnhZhang JingDai HuiMurat AysegulDu YongMcGrail Daniel JMeric-Bernstam FundaLin Shiaw-Yih - Aicardi-Goutières syndrome (AGS) is a rare, genetically-determined spectrum of neurodegenerative disorders that remains poorly understood. Owing to the paucity of data from Middle-Eastern population, we aimed to delineate the clinical, radiological, and genetic features of AGS in an under-represented Middle-Eastern cohort. - Source: PubMed
Publication date: 2026/03/16
Alwalid OsamahSubhi Marwa AlSerhan Ala Aldeen AlAbdulwahhab Saja BSamran ElhamThabet FarouqBenini RubaAlRayahi Jehan - - Source: PubMed
Publication date: 2025/12/30
Sugawara ShoOkada RyoLoo Tze MunTanaka HisamichiMiyata KenichiChiba MasatomoKawasaki HirokoKatoh KaoruKaji ShizuoMaezawa YoshiroYokote KoutaroNakayama MizuhoOshima MasanobuNagao KojiObuse ChikashiNagayama SatoshiTakubo KeiyoNakanishi AkiraKanemaki Masato THara EijiTakahashi Akiko - Mendelian type I interferonopathies are autoimmune diseases caused by genetic mutations that result in upregulation of interferon signalling. Small molecules that modulate proteins encoded by these genes may drive anti-tumour immunity in cancer patients by increasing interferon levels, but chemical probes and drugs are lacking. Covalent chemoproteomics and structural data were compiled to reveal ligandable cysteines, tyrosines and lysines across diverse proteins associated with interferonopathies. From this analysis, we identified several actionable targets, including ligandable sites on ADAR1, RNASEH2A and SAMHD1, and so our work provides a useful resource for future drug discovery efforts directed towards the development of immunotherapeutics. - Source: PubMed
Publication date: 2025/11/12
Alba Nathan MJones Carys RJones Ffion LJones Lyn H - 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