RNase A ENZYMES_COENZYMES
- Known as:
- RNase A ENZYMES_COENZYMES
- Catalog number:
- AK2760
- Product Quantity:
- Bulk
- Category:
- -
- Supplier:
- Akro
- Gene target:
- RNase ENZYMES_COENZYMES
Related genes to: RNase A ENZYMES_COENZYMES
- Gene:
- RNASE6 NIH gene
- Name:
- ribonuclease A family member k6
- Previous symbol:
- RNS6
- Synonyms:
- RAD1, RNaseK6
- Chromosome:
- 14q11.2
- Locus Type:
- gene with protein product
- Date approved:
- 1997-01-08
- Date modifiied:
- 2016-10-05
Related products to: RNase A ENZYMES_COENZYMES
0.2ml PCR Tube, dome cap, clear, RNase, DNase, and pyrogen free, sterilized0.2ml PCR Tube, dome cap, clear, RNase, DNase, and pyrogen free, sterilized; premium package**0.2ml PCR Tube, dome cap, clear, RNase, DNase, and pyrogenic free0.2ml PCR Tube, dome cap, clear, RNase, DNase, and pyrogenic free; premium package**0.2ml PCR Tube, flat cap, clear, RNase, DNase, and pyrogen free0.2ml PCR Tube, flat cap, clear, RNase, DNase, and pyrogen free, sterilized0.2ml PCR Tube, flat cap, clear, RNase, DNase, and pyrogen free, sterilized; premium package**0.2ml PCR Tube, flat cap, clear, RNase, DNase, and pyrogen free; premium package**1.5 mL PCR clean Eppendorf® Safe-Lock™ microcentrifuge tubes, RNAse & DNAse free, box of 5001.5ml Axygen screw-cap tubes with caps (pack of 500), sterile, PCR clean, RNase & DNase free1.5ml microcentrifuge tube, clear, RNase, DNase, and Pyrogen free1.5ml microcentrifuge tube, clear, RNase, DNase, and Pyrogen free, sterilized1.5ml microcentrifuge tube, clear, RNase, DNase, and Pyrogen free, sterilized; premium package**1.5ml microcentrifuge tube, clear, RNase, DNase, and Pyrogen free; premium package**1.5ml Pestles, Assorted DNase, RNase & DNA Free Related articles to: RNase A ENZYMES_COENZYMES
- This study aimed to screen lysosome-related genes that distinguish sepsis from Systemic Inflammatory Response Syndrome (SIRS), in order to provide potential targets for the differential diagnosis of sepsis and for lysosome-targeted therapeutic strategies. Peripheral blood samples were collected from 12 SIRS patients and 20 sepsis patients for RNA sequencing and differential expression analysis. Meanwhile, lysosome-related gene sets were obtained from the Gene Ontology database. The intersection was taken between the differentially expressed genes and the lysosomal gene sets. Subsequently, Protein-Protein Interaction (PPI), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed on these overlapping genes. Meta-analysis was used to screen for core genes, and their diagnostic efficacy was evaluated using Receiver Operating Characteristic (ROC) curves. Furthermore, single-cell RNA sequencing was performed to identify the immune cell types that predominantly express the core genes, facilitating the selection of appropriate cell models for subsequent experimental validation. Functional enrichment analysis revealed that these 21 overlapping genes were significantly enriched in biological pathways such as receptor metabolic process, autophagy, vacuolar transport, cellular catabolic process, and lysosomal transport. Meta-analysis identified four core genes: CD1C, RNASE6, and SNCA were significantly downregulated in the sepsis group, while DRAM1 was significantly upregulated. Diagnostic efficacy evaluation demonstrated that all four core genes possessed good discriminatory value, with AUC as follows: CD1C (0.758), DRAM1 (0.888), RNASE6 (0.737), and SNCA (0.765). Single-cell RNA sequencing analysis suggested that CD1C and RNASE6 are primarily expressed in circulating monocyte-macrophages and B cells, DRAM1 is mainly expressed in circulating monocyte-macrophages, and SNCA is predominantly expressed in circulating monocyte-macrophages and platelets. The four core genes identified in this study could serve as potential diagnostic biomarkers to distinguish sepsis from SIRS. Their expression is mainly enriched in circulating monocyte-macrophages in peripheral blood, providing new directions for future research. - Source: PubMed
Publication date: 2026/05/21
Wang ChenglinMu LingLiu LuLi HailiHu YingchunYin DefengJiang Hao - Several studies have reported an up-regulation of interleukin (IL)-36 in the serum of patients with systemic lupus erythematosus (SLE). Here, we sought to define the mechanisms whereby IL-36 may contribute to the over-activation of type I Interferon (IFN) responses observed in SLE. - Source: PubMed
Publication date: 2026/01/13
Welsh Emma JMcCluskey DanielBaum PatrickLewis Myles JCapon Francesca - Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease characterized by the interplay of genetic and environmental factors, and currently, there there is a lack of effective diagnostic or therapeutic strategies available. This study aims to identify circulating biomarkers for ALS and investigate their interactions with environmental toxins. - Source: PubMed
Publication date: 2025/11/06
Xu LeiHuang BinZhou YaqiuLiao XiaolinChen TingHe Hongping - Cutaneous melanoma is a highly invasive tumor. It enhances metastasis and resistance to immunotherapy immunosuppressive mechanisms. Understanding RNA-binding proteins (RBPs) in melanoma's immune alterations is limited. This study explores immune-regulatory RBPs in metastasis and clarifies RNASE6's role in immune regulation. - Source: PubMed
Publication date: 2025/10/07
Gao PengfeiGao XiaoluZeng XueHua XiangHe WanmeiMin LiYuan ZiqiZhang QianweiPeng Xuebiao - Alzheimer's disease (AD) is a neurodegenerative disorder characterized by progressive cognitive decline and memory loss. While the precise causes of AD remain unclear, emerging evidence suggests that messenger RNA (mRNA) dysregulation contributes to AD pathology and risk. This study examined exosomal mRNA expression profiles of 15 individuals diagnosed with AD and 15 healthy controls from Barranquilla, Colombia. Utilizing advanced bioinformatics and machine learning (ML) techniques, we identified differentially expressed mRNAs and assessed their predictive power for AD diagnosis and AD age of onset (ADAOO). Our results showed that ENST00000331581 () and ENST00000382258 () were significantly upregulated in AD patients. Key predictors for AD diagnosis included ENST00000311550 (), ENST00000278765 (), ENST00000331581 (), ENST00000372572 (), and ENST00000636358 (), achieving > 90% accuracy in both training and testing datasets. For ADAOO, ENST00000340552 () expression correlated with a delay of ~12.6 years, while ENST00000304677 (), ENST00000640218 (), ENST00000602017 (), ENST00000224950 (), and ENST00000322088 () emerged as the most important predictors. ENST00000304677 () and ENST00000602017 () showed promising predictive accuracy in unseen data. These findings suggest that mRNA expression profiles may serve as effective biomarkers for AD diagnosis and ADAOO, providing a cost-efficient and minimally invasive tool for early detection and monitoring. Further research is needed to validate these results in larger, diverse cohorts and explore the biological roles of the identified mRNAs in AD pathogenesis. - Source: PubMed
Publication date: 2024/11/15
Bolívar Daniel AMosquera-Heredia María IVidal Oscar MBarceló ErnestoAllegri RicardoMorales Luis CSilvera-Redondo CarlosArcos-Burgos MauricioGaravito-Galofre PilarVélez Jorge I