Ask about this productRelated genes to: XAGE2 Blocking Peptide
- Gene:
- XAGE2 NIH gene
- Name:
- X antigen family member 2
- Previous symbol:
- GAGED3, XAGE2B
- Synonyms:
- XAGE-2, CT12.2
- Chromosome:
- Xp11.22
- Locus Type:
- gene with protein product
- Date approved:
- 1999-04-15
- Date modifiied:
- 2015-11-18
Related products to: XAGE2 Blocking Peptide
Related articles to: XAGE2 Blocking Peptide
- Bladder cancer (BLCA) is a common malignant tumor whose pathogenesis has not yet been fully elucidated. This study analyzed prognostic genes in BLCA by integrating transcriptomics and proteomics data, and established prognostic models, aiming to offer novel insights for BLCA therapy. Transcriptomic, proteomic, and protein acetylation sequencing were conducted on six BLCA tumor tissues and six paraneoplastic tissue samples. Furthermore, data from TCGA-BLCA, GSE13507, and single-cell RNA sequencing (scRNA-seq) datasets were integrated. Initially, differential expression analysis identified candidate genes regulated by acetylation. These genes were further refined by intersecting with scRNA-DEG obtained from the scRNA-seq dataset, resulting in the identification of key genes. Subsequently, consistency clustering analysis was performed based on these key genes. Prognostic models were then developed utilizing Cox regression analysis and least absolute shrinkage and selection operator (LASSO) Cox regression. Independent prognostic factors were determined through independent prognostic analysis, followed by the establishment of a nomogram model. Additionally, gene set enrichment analysis (GSEA), immune cell infiltration analysis, mutation analysis, and drug sensitivity analysis were conducted between the two risk groups to elucidate underlying mechanisms. A total of 15 key genes were obtained by crossing 284 candidate genes with 510 scRNA-DEGs. Patients in the TCGA-BLCA dataset were categorized into two subtypes based on the 15 key genes. Next, a risk model was developed using five prognostic genes (CTSE, XAGE2, MAP1A, CASQ2, and FXYD6), and a nomogram model was developed using age, pathologic T, pathologic N, and risk score. A total of 1089 GO entries and 49 KEGG pathways, including cytokine-cytokine receptor interactions, ECM receptor interactions, etc., were involved in all genes in both risk groups. The immunization score, matrix score, and ESTIMATE score were significantly higher in the low-risk group than in the high-risk group. CTSE, XAGE2, MAP1A, CASQ2 and FXYD6 were selected as prognostic genes in BLCA, risk model and nomogram model predicting the prognosis of BLCA patients were constructed. These were helpful for prognostic assessment of BLCA. - Source: PubMed
Publication date: 2025/01/27
Tan ZhiyongChen XiaorongHuang YinglongFu ShiLi HaihaoGong ChenLv DihaoYang ChadanfengWang JiansongDing MingxiaWang Haifeng - Single-cell RNA-seq has become a powerful tool to understand tumor cell heterogenicity. This study tried to screen prognosis-related genes in basal-like breast tumors and evaluate their correlations with cellular states at the single-cell level.Bulk RNA-seq data of basal-like tumor cases from The Cancer Genome Atlas-Breast Cancer (TCGA-BRCA) and single-cell RNA-seq from GSE75688 were retrospectively reviewed. Kaplan-Meier survival curves, univariate and multivariate analysis based on Cox regression model were conducted for survival analysis. Gene set enrichment analysis (GSEA) and single-cell cellular functional state analysis were performed.Twenty thousand five hundred thirty genes with bulk RNA-seq data in TCGA were subjected to screening. Preliminary screening identified 10 candidate progression-related genes, including CDH19, AQP5, SDR16C5, NCAN, TTYH1, XAGE2, RIMS2, GZMB, LY6D, and FAM3B. By checking their profiles using single-cell RNA-seq data, only CDH19, SDR16C5, TTYH1, and RIMS2 had expression in primary triple-negative breast cancer (TNBC) cells. Prognostic analysis only confirmed that RIMS2 expression was an independent prognostic indicator of favorable progression free survival (PFS) (HR: 0.78, 95%: 0.64-0.95, P = .015). GSEA analysis showed that low RIMS2 group expression had genes significantly enriched in DNA Repair, and MYC Targets V2. Among the 89 basal-like cells, RIMS2 expression was negatively correlated with DNA repair and epithelial-to-mesenchymal transition (EMT).RIMS2 expression was negatively associated with DNA repair capability of basal-like breast tumor cells and might serve as an independent indicator of favorable PFS. - Source: PubMed
Zhang LingyunLiu ZhengZhu Jingqiang - Though infective endocarditis (IE) is a life-threatening cardiac infection with a high mortality rate, the effective diagnostic and prognostic biomarkers for IE are still lacking. The aim of this study was to explore the potential applicable proteomic biomarkers for IE through the Immunome™ Protein Array system. The system was employed to profile those autoantibodies in IE patients and control subjects. Our results showed that interleukin-1 alpha (IL1A), nucleolar protein 4 (NOL4), tudor and KH domain-containing protein (TDRKH), G antigen 2B/2C (GAGE2), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), and X antigen family member 2 (XAGE2) are highly differentially-expressed among IE and non-IE control. Furthermore, bactericidal permeability-increasing protein (BPI), drebrin-like protein (DBNL), signal transducing adapter molecule 2 (STAM2), cyclin-dependent kinase 16 (CDK16), BAG family molecular chaperone regulator 4 (BAG4), and nuclear receptor-interacting protein 3 (NRIP3) are differentially-expressed among IE and healthy controls. On the other hand, those previously identified biomarkers for IE, including erythrocyte sedimentation rate, C-reactive protein, rheumatoid factor, procalcitonin, and N-terminal-pro-B-type natriuretic peptide demonstrated only minor significance. With scientific rationalities for those highly differentially-expressed proteins, they could serve as potential candidates for diagnostic biomarkers of IE for further analysis. - Source: PubMed
Publication date: 2020/08/05
Chen Chang-HuaChen Ying-ChengHuang Ching-HuiWang Shu-HuiLin Jen-ShiouLo Shou-ChenHuang Chieh-Chen - Early development of the human placenta remains poorly understood due to the lack of proper model systems. Previous reports have demonstrated that human induced pluripotent stem cells (hiPSCs) treated with bone morphogenetic protein 4 (BMP4) can differentiate into extraembryonic tissues as useful models of the early stage of trophoblast (TB) differentiation. In our previous study, we optimized the culture conditions of hiPSC-derived TB lineages, but the differentiated cells were heterogeneous. - Source: PubMed
Publication date: 2019/10/09
Tsuchida NanaeKojima JunyaFukuda AtsushiOda MayumiKawasaki TomoyukiIto HiroeKuji NaoakiIsaka KeiichiNishi HirotakaUmezawa AkihiroAkutsu Hidenori - Pancreaticobiliary maljunction (PBM) is associated with high risk of epithelial atypical growth and malignant transformation of the bile duct or gallbladder. However, overall changes in genetic expression have not been examined in children with PBM. Genome-wide expression was analyzed using peripheral blood samples from 10 children with PBM and 15 pediatric controls. Differentially expressed genes (DEGs) were identified using microarray. Bioinformatics analysis was conducted using Gene Ontology and KEGG analyses. The top 5 in the up-regulated genes in PBM were verified with qRT-PCR. Receiver operator characteristic curve analysis was conducted to evaluate the predictive accuracy of selected genes for PBM. The microarray experiments identified a total of 876 DEGs in PBM, among which 530 were up-regulated and the remaining 346 were down-regulated. Verification of the top 5 up-regulated genes (TYMS, MYBPC1, FUT1, XAGE2, and GREB1L) by qRT-PCR confirmed the up-regulation of MYBPC1 and FUT1. Receiver operating characteristic curve analysis suggested that FUT1 and MYBPC1 up-regulation could be used to predict PBM, with the area under the curve of 0.873 (95%CI=0.735-1.000) and 0.960 (95%CI=0.891-1.000), respectively. FUT1 and MYBPC1 were up-regulated in children with PBM, and could be used as potential biomarkers for PBM. - Source: PubMed
Publication date: 2019/07/29
Guo Wan-LiangGeng JiaZhao Jun-GangFang FangHuang Shun-GenWang Jian