Ask about this productRelated genes to: GIMAP5 Blocking Peptide
- Gene:
- GIMAP5 NIH gene
- Name:
- GTPase, IMAP family member 5
- Previous symbol:
- IAN4L1
- Synonyms:
- HIMAP3, IAN5
- Chromosome:
- 7q36.1
- Locus Type:
- gene with protein product
- Date approved:
- 2002-01-16
- Date modifiied:
- 2015-09-10
Related products to: GIMAP5 Blocking Peptide
Related articles to: GIMAP5 Blocking Peptide
- The objective was to determine the association between serum IgE levels and the infiltration order of T lymphocytes and macrophages in pancreatic islets in relation to the loss of insulin and glucagon cells in presymptomatic congenic BB Gimap5-DP (Diabetes Prone) rats. - Source: PubMed
Publication date: 2025/10/03
Jönsson JosefineFaxius LindaTångrot JeanetteVance KrystenJerman StephanieBowman DougBogdani MarikaEricsson PeterBennet RasmusRamelius AnitaLernmark Åke - Tumor associated macrophages (TAMs) in Head and neck squamous cell carcinoma (HNSCC), particularly M2-polarized subtypes, are pivotal drivers of tumorigenesis, angiogenesis, and metastasis, contributing to adverse clinical outcomes. Current prognostic markers lack precision, underscoring the need for novel biomarkers and risk stratification models. Single-cell RNA sequencing (scRNA-seq) was applied to profile the transcriptional landscape of TAMs in HNSCC at single-cell resolution. 1,208 M2 TAMs were integrated from scRNA-seq data with bulk RNA sequencing to identify molecular signatures. Weighted correlation network analysis (WGCNA) and Uniform Manifold Approximation and Projection (UMAP) analysis were applied to dissect TAMs heterogeneity and interactions within the tumor microenvironment. experiments validated the efficacy of the prognostic signature model. In this study, high infiltration of M2 TAMs was strongly associated with advanced clinical stages, lymph node metastasis, and reduced overall survival (P<0.001). TCGA datasets were utilized for cross-platform verification. Multivariate Cox regression and survival analyses were performed to establish prognostic relevance. 11 prognostic signature genes (FCGBP, GIMAP5, WIPF1, RASGEF1B, GIMAP7, IGFLR1, GPR35, NCF1, CLECL1, HEXB, IL10) were identified through integrative analysis, which formed the basis of a robust risk stratification model. The distribution of biomarkers in the high-risk group, as determined by the signature we constructed, can serve as a better indicator for assessing poor prognosis. In clinical samples, prognosis signature has the potential to predict the prognosis effectively in patients with HNSCC.M2 TAMs-driven prognostic signature for HNSCC offers a clinically actionable tool for risk stratification and outcome prediction. - Source: PubMed
Publication date: 2025/08/12
Wang JialeLi HuanShi MingruiRen ChenghaoWei WuZhao QiHe XinxinYang ZihuiWei JianhuaYang Xinjie - Combination of CT imaging and RNA sequencing techniques was used to explore the potential biomarkers specific to lung adenocarcinoma within pulmonary ground-glass nodules. The imaging and pathological data of patients with pulmonary ground-glass nodules who underwent chest CT scanning were confirmed through surgical procedures. Based on the pathological results, the patients were categorized into a benign nodule group and a malignant nodule group. Subsequently, RNA sequencing was conducted to analyze gene expression information in the pulmonary ground-glass nodules of these 16 patients. CT signs demonstrated statistical significance in both benign and malignant nodules. A total of 2080 upregulated genes and 1240 downregulated genes were identified through RNA sequencing in malignant nodules compared to benign nodules. CST1 exhibited increased expression among the upregulated genes in lung adenocarcinoma tissues compared to lung tissues. Among the downregulated genes, only GIMAP1-GIMAP5 showed decreased expression in lung adenocarcinoma tissues. Finally, we validated the clinical significance of CST1 and GIMAP1-GIMAP5 in patients with lung adenocarcinoma, particularly highlighting a strong correlation between GIMAP1-GIMAP5 expression levels and prognosis for patients. A visual nomogram predictive model for pulmonary ground-glass nodules was constructed (area under the receiver operating characteristic curve (AUC) > 0.8). We constructed a nomogram combining CST1 and GIMAP1-GIMAP5 expression for predicting lung adenocarcinoma in ground-glass nodules in the context of COVID-19. This nomogram addresses the unique diagnostic challenges posed by COVID-19, where overlapping pulmonary imaging findings between viral pneumonia and early lung cancer necessitate robust molecular-aided discrimination. - Source: PubMed
Publication date: 2025/06/11
Li YamengZhang Qingxian - Electronic health records (EHRs) coupled with large-scale biobanks offer great promises to unravel the genetic underpinnings of treatment efficacy. However, medication-induced biomarker trajectories stemming from such records remain poorly studied. Here, we extract clinical and medication prescription data from EHRs and conduct GWAS and rare variant burden tests in the UK Biobank (discovery) and the All of Us program (replication) on ten cardiometabolic drug response outcomes including lipid response to statins, HbA1c response to metformin and blood pressure response to antihypertensives (N = 932-28,880). Our discovery analyses in participants of European ancestry recover previously reported pharmacogenetic signals at genome-wide significance level (APOE, LPA and SLCO1B1) and a novel rare variant association in GIMAP5 with HbA1c response to metformin. Importantly, these associations are treatment-specific and not associated with biomarker progression in medication-naive individuals. We also found polygenic risk scores to predict drug response, though they explained less than 2% of the variance. In summary, we present an EHR-based framework to study the genetics of drug response and systematically investigated the common and rare pharmacogenetic contribution to cardiometabolic drug response phenotypes in 41,732 UK Biobank and 14,277 All of Us participants. - Source: PubMed
Publication date: 2025/03/25
Sadler Marie CApostolov AlexanderCevallos CaterinaAuwerx ChiaraRibeiro Diogo MAltman Russ BKutalik Zoltán - 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