AMI_1
- Known as:
- AMI_1
- Catalog number:
- 1943-5
- Product Quantity:
- 5 mg
- Category:
- -
- Supplier:
- Biovis
- Gene target:
- AMI_1
Related genes to: AMI_1
- Gene:
- TRX-CAT2-1 NIH gene
- Name:
- transfer RNA-iMet (CAT) 2-1
- Previous symbol:
- TRM1, TRMI1, TRNAMI1, TRNAM2
- Synonyms:
- -
- Chromosome:
- 6p22.1
- Locus Type:
- RNA, transfer
- Date approved:
- 2001-06-22
- Date modifiied:
- 2018-06-15
Related products to: AMI_1
(1,5_dimethylhexyl)amine (1S)_2_oxoborn (1,5_dimethylhexyl)ami(2R)_2_((6_((3_Amino_5_chlorophenyl)ami Aminopurvalanol A(5_Bromobenzo[b]furan_2_yl) methyl ami (5_Bromobenzo[b]furan(R)_((2_Phenyl_4_quinolinyl)carbonyl)ami SB 218795(R)_(5,6,7,8_Tetrahydro_quinolin_8_yl)ami _(R*,S*)_(_)_alpha_[1_[(2_hydroxyethyl)ami (R*,S*)_(_)_alpha_[1_[(2_(S)_methyl2_(bis(tert_butoxycarbonyl)ami (S)_methyl2_(bis(tert_bu(Tyr34)_pTH (7_34) amide (bovine) (Tyr34)_pTH (7_34) ami(Z)_12_hydroxy_N_[2_[(2_hydroxyethyl)ami (Z)_12_hydroxy_N_[2_[(21,3_Propanediol, 2_[(triphenylmethyl)ami _1,4_bis[(2,6_dibromo_4_methylphenyl)ami 1,4_bis[(2,6_dibromo_4_1,4_bis[(2_bromo_4,6_dimethylphenyl)ami 1,4_bis[(2_bromo_4,6_di13_amino_N_[2_[[2_[[2_[(2_aminoethyl)ami 13_amino_N_[2_[[2_[[2_[(1_(3_Furyl)methane amine hydrochloride 1_(3_Furyl)methane ami1_(methylamino)_4_[(3_methylphenyl)ami 1_(methylamino)_4_[(3_ Related articles to: AMI_1
- 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 - Adult brain tumors (glioma) represent a cancer of unmet need where standard-of-care is non-curative; thus, new therapies are urgently needed. It is unclear whether isocitrate dehydrogenases (IDH1/2) when not mutated have any role in gliomagenesis or tumor growth. Nevertheless, IDH1 is overexpressed in glioblastoma (GBM), which could impact upon cellular metabolism and epigenetic reprogramming. This study characterizes IDH1 expression and associated genes and pathways. A novel biomarker discovery pipeline using artificial intelligence (evolutionary algorithms) was employed to analyze IDH-wildtype adult gliomas from the TCGA LGG-GBM cohort. Ninety genes whose expression correlated with IDH1 expression were identified from: (1) All gliomas, (2) primary GBM, and (3) recurrent GBM tumors. Genes were overrepresented in ubiquitin-mediated proteolysis, focal adhesion, mTOR signaling, and pyruvate metabolism pathways. Other non-enriched pathways included O-glycan biosynthesis, notch signaling, and signaling regulating stem cell pluripotency (PCGF3). Potential prognostic (TSPYL2, JAKMIP1, CIT, TMTC1) and two diagnostic (MINK1, PLEKHM3) biomarkers were downregulated in GBM. Their gene expression and methylation were negatively and positively correlated with IDH1 expression, respectively. Two diagnostic biomarkers (BZW1, RCF2) showed the opposite trend. Prognostic genes were not impacted by high frequencies of molecular alterations and only one (TMTC1) could be validated in another cohort. Genes with mechanistic links to IDH1 were involved in brain neuronal development, cell proliferation, cytokinesis, and O-mannosylation as well as tumor suppression and anaplerosis. Results highlight metabolic vulnerabilities and therapeutic targets for use in future clinical trials. - Source: PubMed
Publication date: 2022/07/02
McInerney Caitríona ELynn Joanna AGilmore Alan RFlannery TomPrise Kevin M - Tumor mutational burden (TMB) is considered a potential biomarker for predicting the response and effect of immune checkpoint inhibitors (ICIs). However, there are still inconsistent standards of gene panels using next-generation sequencing and poor correlation between the TMB genes, immune cell infiltrating, and prognosis. We applied text-mining technology to construct specific TMB-associated gene panels cross various cancer types. As a case exploration, Pearson's correlation between TMB genes and immune cell infiltrating was further analyzed in colorectal cancer. We then performed LASSO Cox regression to construct a prognosis predictive model and calculated the risk score of each sample for receiver operating characteristic (ROC) analysis. The results showed that the assessment of TMB gene panels performed well with fewer than 500 genes, highly mutated genes, and the inclusion of synonymous mutations and immune regulatory and drug-target genes. Moreover, the analysis of TMB differentially expressed genes (DEGs) suggested that JAKMIP1 was strongly correlated with the gene expression level of CD8 T cell markers in colorectal cancer. Additionally, the prognosis predictive model based on 19 TMB DEGs reached AUCs of 0.836, 0.818, and 0.787 in 1-, 3-, and 5-year OS models, respectively (C-index: 0.810). In summary, the gene panel performed well and TMB DEGs showed great potential value in immune cell infiltration and in predicting survival. - Source: PubMed
Publication date: 2022/02/17
Wang ShuangkuaiTong YuantaoZong HuiXu XuewenCrabbe M James CWang YingZhang Xiaoyan