Ask about this productRelated genes to: GIMAP6 Blocking Peptide
- Gene:
- GIMAP6 NIH gene
- Name:
- GTPase, IMAP family member 6
- Previous symbol:
- -
- Synonyms:
- FLJ22690, IAN6
- Chromosome:
- 7q36.1
- Locus Type:
- gene with protein product
- Date approved:
- 2004-10-29
- Date modifiied:
- 2014-11-19
Related products to: GIMAP6 Blocking Peptide
Related articles to: GIMAP6 Blocking Peptide
- Controlling hyperlipidemia has reduced but not eliminated atherosclerotic cardiovascular disease as a predominant cause of human mortality. Here, we report that loss of the immunoregulatory gene GTPase of immunity-associated protein 6 (), causes an inflammatory vasculopathy and accelerated atherosclerosis in the absence of hyperlipidemia. These pathologic changes in turn result in progressive cardiac ischemia, myocardial infarction, and heart failure, culminating in early death. In humans, rare deleterious GIMAP6 variants are associated with premature severe cardiovascular disease. These findings reveal GIMAP6 to play an important protective role against atherosclerotic cardiovascular disease whose identification offers opportunities for improved risk management and a target for new therapies. - Source: PubMed
Publication date: 2026/01/28
Xiang ChenArusha KajaSpringer DanielleNakamori SachikoDu Jiang PingYang Zhi-HongCui JingZhang YuJing HuiePark Ann YZhu Maria HWeber Sarah ECagdas DenizAbolhassan HassanBehniafard NasrinSmelkinson Margery GLiang QihuiEverest ElifKun Julia FGrogan AlyssaTreat Jennifer DZerbe Christa SHolland Steven MVirmani RenuRemaley Alan TSu Helen CZheng LixinLenardo Michael J - Accurate staging is pivotal for tailoring treatment intensity, optimizing resource allocation, and improving long-term patient outcomes in IBD. The intestinal microbiota and transcriptional profiles emerge as critical determinants in IBD staging, demonstrating promise as non-invasive biomarkers for predicting disease progression and informing personalized therapeutic strategies. - Source: PubMed
Publication date: 2025/10/10
Tao YiWang Lin-FLi PanSun RuiLi Yong-JSun Ming-HongZhang Li-JYang Li-HJin Jia-JZhong Xiao-N - This study investigated Hippo signaling pathway-related biomarkers in acute myocardial infarction (AMI). First, differentially expressed genes (DEGs) between AMI patients and controls were identified. Consensus clustering then classified AMI subtypes, followed by subtype-specific DEG screening. Candidate genes were derived from intersecting initial DEGs with subtype-associated DEGs. Three machine-learning algorithms prioritized five biomarkers (NAMPT, CXCL1, CREM, GIMAP6, and GIMAP7), validated through multi-dataset analyses and cellular expression profiling. qRT-PCR and Western blot confirmed differential expression patterns between AMI and controls across experimental models. Notably, NAMPT, CXCL1, and GIMAP6 exhibited cell-type-specific expression in endothelial cells and macrophages. We further predicted 179 potential therapeutic agents targeting these biomarkers. Niclosamide and eugenol were observed to mitigate hypoxia-induced injury in neonatal mouse ventricular cardiomyocytes. In vivo experiments demonstrated upregulated NAMPT/CXCL1 and downregulated GIMAP6/GIMAP7 in AMI myocardial tissues, with significant NAMPT protein elevation. These biomarkers show clinical diagnostic potential and provide mechanistic insights into AMI pathogenesis. - Source: PubMed
Publication date: 2025/03/26
Li XingdaHe XueqiZhang YuHao XinyuanXiong AnqiHuang JiayuJiang BiyingTong ZaiyuHuang HaiyanYi LianChen Wenjia - Acute myocardial infarction (AMI), a critical cardiovascular condition, is often associated with serious health risks. Recent studies suggest a link between copper-induced apoptosis and immune cell infiltration. Specifically, abnormal accumulation of copper ions can lead to intracellular oxidative stress and apoptosis, while also affecting immune cell function and infiltration. Nevertheless, studies exploring this relationship in the context of AMI are notably scarce, underscoring the necessity of identifying biomarkers associated with cuproptosis in AMI. Consensus clustering analysis was employed to classify distinct subtypes of AMI in the GSE66360 dataset. Concurrently, differential expression analysis was performed to identify differentially expressed genes (DEGs) across subtypes and between AMI and control samples. We employed Venn diagrams to validate the selection of cuproptosis-related DEGs in patients with AMI. A protein-protein interaction network was constructed to pinpoint potential candidate genes. Receiver operating characteristic curves were generated to identify promising biomarkers. The immune infiltration milieu was analyzed using CIBERSORT algorithms. Finally, the expression levels of identified cuproptosis-related biomarkers were validated at the transcriptional level. We classified AMI into 2 distinct cuproptosis-related subtypes, leading to the identification of 157 cuproptosis-related DEGs. Further analysis refined this list to 10 potential candidate genes. Among these, 5 emerged as significant biomarkers for AMI: granzyme A (GZMA), GTPase immunity-associated proteins (GIMPAs) GIMAP7, GIMAP5, GIMAP6, and TRAF3 interacting protein 3 (TRAF3IP3). A comprehensive examination of immune infiltration in AMI samples revealed significant differences in the levels of 11 types of immune cells, with GZMA displaying the highest correlation with activated mast cells and CD8 + T cells. We observed markedly lower expression levels of GZMA, GIMAP6, and TRAF3IP3 in the AMI group compared to controls. This study identified 5 cuproptosis-related biomarkers (GZMA, GIMAP7, GIMAP5, GIMAP6, and TRAF3IP3) associated with AMI, laying a theoretical foundation for the treatment of AMI. - Source: PubMed
Zhang JingYue ZhijieZhu NaZhao Na - The prognosis for lung adenocarcinoma (LUAD) remains dismal, with a 5-year survival rate of <20%. Therefore, the purpose of this study was to identify potentially reliable biomarkers in LUAD by machine learning combination with Mendelian randomization (MR). - Source: PubMed
Publication date: 2024/09/19
Zhang ChuchuLiu YingLu YingdongChen ZehuiLiu YiMao QiyuanBao ShengchuanZhang GeZhang YingLin HongshengLi Haiyan