Anti-Racgap1 polyclonal antibody (CABT-B821)

Specifications


Host Species
Rabbit
Antibody Isotype
IgG
Species Reactivity
Zebrafish
Conjugate
Unconjugated

Applications


Application Notes
IHC-Wmt: 1:100-1:500
*Suggested working dilutions are given as a guide only. It is recommended that the user titrates the product for use in their own experiment using appropriate negative and positive controls.

Target


Alternative Names
wu:fb92c08; zgc:77839; racgap1

Citations


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References


Screening and identification of key biomarkers in hepatocellular carcinoma: Evidence from bioinformatic analysis

ONCOLOGY REPORTS

Authors: Li, Lin; Lei, Qingsong; Zhang, Shujun; Kong, Lingna; Qin, Bo

Hepatocellular carcinoma (HCC) is one of the most common cancers worldwide. Intense efforts have been made to elucidate the pathogeny, but the molecular mechanisms of HCC are still not well understood. To identify the candidate genes in the carcinogenesis and progression of HCC, microarray datasets GSE19665, GSE33006 and GSE41804 were downloaded from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified, and function enrichment analyses were performed. The protein-protein interaction network (PPI) was constructed and the module analysis was performed using STRING and Cytoscape. A total of 273 DEGs were identified, consisting of 189 downregulated genes and 84 upregulated genes. The enriched functions and pathways of the DEGs include protein activation cascade, complement activation, carbohydrate binding, complement and coagulation cascades, mitotic cell cycle and oocyte meiosis. Sixteen hub genes were identified and biological process analysis revealed that these genes were mainly enriched in cell division, cell cycle and nuclear division. Survival analysis showed that BUB1, CDC20, KIF20A, RACGAP1 and CEP55 may be involved in the carcinogenesis, invasion or recurrence of HCC. In conclusion, DEGs and hub genes identified in the present study help us understand the molecular mechanisms underlying the carcinogenesis and progression of HCC, and provide candidate targets for diagnosis and treatment of HCC.

Identification of prognostic biomarkers for breast cancer based on miRNA and mRNA co-expression network

JOURNAL OF CELLULAR BIOCHEMISTRY

Authors: Yao, Yan; Liu, Ruijuan; Gao, Chundi; Zhang, Tingting; Qi, Lingyu; Liu, Gongxi; Zhang, Wenfeng; Wang, Xue; Li, Jie; Li, Jia; Sun, Changgang

Purpose Breast cancer (BC) remains a serious health threat for women due to its high incidence and the trend of rejuvenation. Accumulating evidence has highlighted that microRNAs (miRNAs) and messenger RNAs (mRNAs) could play important roles in various biological processes involved in the pathogenesis of BC. The present study aimed to identify potential prognostic biomarkers associated with BC. Methods Here, original gene expression profiles of patients with BC was downloaded from The Cancer Genome Atlas (TCGA) database. TargetScan, miRDB, and miRTarBase databases were used to predict the target genes of prognostic-related differentially expressed miRNAs (DEMs). Subsequently, functional enrichment analysis and topological analysis were performed on the overlaps of target genes and differentially expressed mRNAs (DEGs), and Kaplan-Meier analysis was used to predict prognosis-related target genes to identify prognostic biomarkers. Results A total of 218 DEMs and 2222 DEGs were extracted in which eight miRNAs were associated with prognosis, and 278 target DEGs were screened out incorporated into functional enrichment analysis and protein-protein interaction network visualization studies. Additionally, five hub genes (CXCL12, IGF1, LEF1, MMP1, and RACGAP1) were observed as potential biomarkers for BC prognosis through survival analysis. Conclusion We performed a distinctive correlation analysis of miRNA-mRNA in BC patients, and identified eight miRNAs and five hub genes may be effective biomarkers for the prognosis of BC patients.

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