JAKMIP1 Antibody (Center) Blocking Peptide
- Known as:
- JAKMIP1 Antibody (Center) Blocking Peptide
- Catalog number:
- BP8685c
- Product Quantity:
- 0.1 mg
- Category:
- -
- Supplier:
- Abgen
- Gene target:
- JAKMIP1 Antibody (Center) Blocking Peptide
Related genes to: JAKMIP1 Antibody (Center) Blocking Peptide
- Gene:
- JAKMIP1 NIH gene
- Name:
- janus kinase and microtubule interacting protein 1
- Previous symbol:
- -
- Synonyms:
- MARLIN1, JAMIP1, Gababrbp, FLJ31564
- Chromosome:
- 4p16.1
- Locus Type:
- gene with protein product
- Date approved:
- 2006-02-23
- Date modifiied:
- 2014-11-18
Related products to: JAKMIP1 Antibody (Center) Blocking Peptide
Related articles to: JAKMIP1 Antibody (Center) Blocking Peptide
- Autism spectrum disorder (ASD) encompasses a group of neurodevelopmental disorders influenced by genetic and environmental factors, although the molecular mechanisms underlying their interactions remain unclear. We previously reported dysregulated cytokine signalling in chromosome 15q-duplication syndrome (Dup(15q)), a common syndromic form of ASD. Dup(15q) induced pluripotent stem cell (iPSC)-derived neurons exhibit an amplified Signal Transducer and Activator of Transcription-3 (STAT3) response to Interleukin-6 (IL-6), a cytokine often upregulated in ASD. To identify candidate genes within the 15q region that may modify cytokine signalling, we investigated (), as dysregulation has been linked to altered expression of genes involved in immunoregulatory pathways. - Source: PubMed
Publication date: 2026/06/22
Martin Emily-RoseMartin Josan GRussell Mark AOguro-Ando Asami - Splicing variants significantly contribute to Mendelian disorders, yet predicting their pathogenicity remains challenging. To address this issue, we developed a framework that simplifies the evaluation of pathogenic splicing single nucleotide variants (SNVs) while following ACMG/AMP guidelines and ClinGen recommendations established in 2023. Our system simplifies the 2023 ClinGen criteria by assigning a priority score (ranging from -10 to 14) to SNVs in open reading frame regions. Validation using pathogenic splicing SNVs from the Human Gene Mutation Database and common SNVs from gnomAD demonstrated superior discrimination compared to SpliceAI alone (area under the receiver operating characteristic 0.991 versus 0.983, = 2.11 × 10⁻). When applied to 1257 patients with unresolved diagnoses after exome sequencing, our framework identified pathogenic splicing variants in , and and suggested potential candidate disease-causing genes, and . This method enhances the detection of splicing variants in exome sequencing. - Source: PubMed
Publication date: 2025/12/19
Utsuno YasuhiroHamanaka KoheiSakamoto MasamuneTsuchida NaomiUchiyama YuriKoshimizu ErikoFujita AtsushiMiyatake SatokoMizuguchi TakeshiMatsumoto Naomichi - This study aims to develop a risk model for the prognostic prediction for colorectal cancer (CRC) patients according to the phenotype related to damage-associated molecular patterns (DAMPs). The data were sourced from the Cancer Genome Atlas (TCGA) and cBioportal databases. The DAMP score was calculated based on the TCGA cohort data using the "ssGSEA" method. Differentially expressed genes (DEGs) identified by the "limma" package were compressed by performing Lasso Cox regression analysis using the "glmnet" package. Subsequently, biomarkers obtained were used to construct a risk model and a nomogram. The CRC subjects were divided by the median RiskScore into low- and high-risk groups. Kaplan-Meier (KM) survival analysis was conducted, and the "timeROC" package was used for model validation. The "estimate" package, "MCP-COUNTER", "ssGSEA" and "TIDE" were employed to perform immune infiltration analyses. Drug sensitivity analysis and pathway analysis were conducted using the "pRRophetic" package and "ssGSEA", respectively. According to the results, cancer-adjacent samples showed higher DAMP score and immune cell infiltration, lower tumor purity, and a better prognosis. Nine biomarkers (PAH, SIGLEC14, MMP1, JAKMIP1, FCGR3B, KCNT1, SLC2A3, SLC11A1, and HOXC4) were determined to build a reliable risk model, which showed a relatively high AUC value. Notably, patients classified by the model into the high-risk group had a worse prognostic outcome. Furthermore, a nomogram was constructed, and both the nomogram and RiskScore demonstrated a strong predictive power. The results of immune infiltration and drug sensitivity analysis showed higher immune infiltration and greater immunotherapy benefit in the low-risk group. Also, the low-risk group was enriched in immune-related pathways. We developed a reliable DAMP signature for CRC, contributing to the diagnosis and treatment of CRC. - Source: PubMed
Publication date: 2025/07/16
Wu YangXu YangjingChen YongtongXu WeiYao MinwuDing Wencong - The use of metabolome genome-wide association studies (mGWAS) has been shown to be effective in identifying functional genes in complex diseases. While mGWAS has been applied to biomedical and pharmaceutical studies, its potential in predicting gastric cancer prognosis has yet to be explored. This study aims to address this gap and provide insights into the genetic basis of GC survival, as well as identify vital regulatory pathways in GC cell progression. Genome-wide association analysis of plasma metabolites related to gastric cancer prognosis was performed based on the Generalized Linear Model (GLM). We used a log-rank test, LASSO regression, multivariate Cox regression, GO enrichment analysis, and the Cytoscape software to visualize the complex regulatory network of genes and metabolites and explored in-depth genetic variation in gastric cancer prognosis based on mGWAS. We found 32 genetic variation loci significantly associated with GC survival-related metabolites, corresponding to seven genes, , , , , , , and . Furthermore, this study identified 722 Single nucleotide polymorphism (SNP) sites, suggesting an association with GC prognosis-related metabolites, corresponding to 206 genes. These 206 possible functional genes for gastric cancer prognosis were mainly involved in cellular signaling molecules related to cellular components, which are mainly involved in the growth and development of the body and neurological regulatory functions related to the body. The expression of 23 of these genes was shown to be associated with survival outcome in gastric cancer patients in The Cancer Genome Atlas (TCGA) database. Based on the genome-wide association analysis of prognosis-related metabolites in gastric cancer, we suggest that gastric cancer survival-related genes may influence the proliferation and infiltration of gastric cancer cells, which provides a new idea to resolve the complex regulatory network of gastric cancer prognosis. - Source: PubMed
Publication date: 2023/10/17
Zhang YulingLyu YanpingChen LiangpingCao KangChen JingwenHe ChenzhouLyu XuejieJiang YuXiang JianjunLiu BaoyingWu Chuancheng - The microtubule-targeting paclitaxel (PTX) and docetaxel (DTX) are widely used chemotherapeutic agents. However, the dysregulation of apoptotic processes, microtubule-binding proteins, and multi-drug resistance efflux and influx proteins can alter the efficacy of taxane drugs. In this review, we have created multi-CpG linear regression models to predict the activities of PTX and DTX drugs through the integration of publicly available pharmacological and genome-wide molecular profiling datasets generated using hundreds of cancer cell lines of diverse tissue of origin. Our findings indicate that linear regression models based on CpG methylation levels can predict PTX and DTX activities (log-fold change in viability relative to DMSO) with high precision. For example, a 287-CpG model predicts PTX activity at R of 0.985 among 399 cell lines. Just as precise (R=0.996) is a 342-CpG model for predicting DTX activity in 390 cell lines. However, our predictive models, which employ a combination of mRNA expression and mutation as input variables, are less accurate compared to the CpG-based models. While a 290 mRNA/mutation model was able to predict PTX activity with R of 0.830 (for 546 cell lines), a 236 mRNA/mutation model could calculate DTX activity at R of 0.751 (for 531 cell lines). The CpG-based models restricted to lung cancer cell lines were also highly predictive (R≥0.980) for PTX (74 CpGs, 88 cell lines) and DTX (58 CpGs, 83 cell lines). The underlying molecular biology behind taxane activity/resistance is evident in these models. Indeed, many of the genes represented in PTX or DTX CpG-based models have functionalities related to apoptosis (e.g., ACIN1, TP73, TNFRSF10B, DNASE1, DFFB, CREB1, BNIP3), and mitosis/microtubules (e.g., MAD1L1, ANAPC2, EML4, PARP3, CCT6A, JAKMIP1). Also represented are genes involved in epigenetic regulation (HDAC4, DNMT3B, and histone demethylases KDM4B, KDM4C, KDM2B, and KDM7A), and those that have never been previously linked to taxane activity (DIP2C, PTPRN2, TTC23, SHANK2). In summary, it is possible to accurately predict taxane activity in cell lines based entirely on methylation at multiple CpG sites. - Source: PubMed
Publication date: 2022/12/29
Bacolod Manny DFisher Paul BBarany Francis