205 GENE EXPRESSION SIGNATURE ASSOCIATED WITH METASTASIS IN CLEAR CELL RENAL CELL CARCINOMA

2010 ◽  
Vol 183 (4S) ◽  
Author(s):  
Jimsgene Sanjmyatav ◽  
Thomas Steiner ◽  
Mieczyslaw Gajda ◽  
Heiko Wunderlich ◽  
Kerstin Junker
Cancers ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 178
Author(s):  
Maria Bassanelli ◽  
Marina Borro ◽  
Michela Roberto ◽  
Diana Giannarelli ◽  
Silvana Giacinti ◽  
...  

The Identification of reliable Biomarkers able to predict the outcome after nephrectomy of patients with clear cell renal cell carcinoma (ccRCC) is an unmet need. The gene expression analysis in tumor tissues represents a promising tool for better stratification of ccRCC subtypes and patients’ evaluation. Methods: In our study we retrospectively analyzed using Next-Generation expression analysis (NanoString), the expression of a gene panel in tumor tissue from 46 consecutive patients treated with nephrectomy for non-metastatic ccRCC at two Italian Oncological Centres. Significant differences in expression levels of selected genes was sought. Additionally, we performed a univariate and a multivariate analysis on overall survival according to Cox regression model. Results: A 17-gene expression signature of patients with a recurrence-free survival (RFS) < 1 year (unfavorable genomic signature (UGS)) and of patients with a RFS > 5 years (favorable genomic signature (FGS)) was identified and resulted in being significantly correlated with overall survival of the patients included in this analysis (HR 51.37, p < 0.0001). Conclusions: The identified Genomic Signatures may serve as potential biomarkers for prognosis prediction of non-metastatic RCC and could drive both follow-up and treatment personalization in RCC management.


2011 ◽  
Vol 186 (1) ◽  
pp. 289-294 ◽  
Author(s):  
Jimsgene Sanjmyatav ◽  
Thomas Steiner ◽  
Heiko Wunderlich ◽  
Julia Diegmann ◽  
Mieczyslaw Gajda ◽  
...  

PLoS ONE ◽  
2019 ◽  
Vol 14 (5) ◽  
pp. e0216793 ◽  
Author(s):  
Agnieszka M. Borys ◽  
Michał Seweryn ◽  
Tomasz Gołąbek ◽  
Łukasz Bełch ◽  
Agnieszka Klimkowska ◽  
...  

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Karel K. M. Koudijs ◽  
Anton G. T. Terwisscha van Scheltinga ◽  
Stefan Böhringer ◽  
Kirsten J. M. Schimmel ◽  
Henk-Jan Guchelaar

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Maolin Hu ◽  
Jiangling Xie ◽  
Huiming Hou ◽  
Ming Liu ◽  
Jianye Wang

Background. Few previous studies have comprehensively explored the level of DNA methylation and gene expression in ccRCC. The purpose of this study was to identify the key clear cell renal cell carcinoma- (ccRCC-) related DNA methylation-driven genes (MDG) and to build a prognostic model based on the level of DNA methylation. Methods. RNA-seq transcriptome data and DNA methylation data were obtained from The Cancer Genome Atlas. Based on the MethylMix algorithm, we obtain ccRCC-related MDG. The univariate and multivariate Cox regression analyses were employed to investigate the correlation between patient overall survival and the methylation level of each MDG. Finally, a prognosis risk score was established based on a linear combination of the regression coefficient derived from the multivariate Cox regression model (β) multiplied with the methylation level of the gene. Results. 19 ccRCC-related MDG were identified. Three MDG (NCKAP1L, EVI2A, and BATF) were further screened and integrated into a prognostic risk score model, risk score=3.710∗methylation level of NCKAP1L+−3.892∗methylation level of EVI2A+−3.907∗methylation level of BATF. The risk model was independent from conventional clinical characteristics as a prognostic factor for ccRCC (HR=1.221, 95% confidence interval: 1.063–1.402, and P=0.005). The joint survival analysis showed that the gene expression and methylation levels of the prognostic genes EVI2A and BATF were significantly related with prognosis. Conclusion. This study provided an important bioinformatics foundation for in-depth studies of ccRCC DNA methylation.


Genes ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 440
Author(s):  
Yitong Zhang ◽  
Jiaxing Wang ◽  
Xiqing Liu

Kidney renal clear cell carcinoma (KIRC) is the most common and fatal subtype of renal cancer. Antagonistic associations between selenium and cancer have been reported in previous studies. Selenium compounds, as anti-cancer agents, have been reported and approved for clinical trials. The main active form of selenium in selenoproteins is selenocysteine (Sec). The process of Sec biosynthesis and incorporation into selenoproteins plays a significant role in biological processes, including anti-carcinogenesis. However, a comprehensive selenoprotein mRNA analysis in KIRC remains absent. In the present study, we examined all 25 selenoproteins and identified key selenoproteins, glutathione peroxidase 3 (GPX3) and type 1 iodothyronine deiodinase (DIO1), with the associated prognostic biomarker leucine-rich repeat containing 19 (LRRC19) in clear cell renal cell carcinoma cases from The Cancer Genome Atlas (TCGA) database. We performed validations for the key gene expression levels by two individual clear cell renal cell carcinoma cohorts, GSE781 and GSE6344, datasets from the Gene Expression Omnibus (GEO) database. Multivariate survival analysis demonstrated that low expression of LRRC19 was an independent risk factor for OS. Gene set enrichment analysis (GSEA) identified tyrosine metabolism, metabolic pathways, peroxisome, and fatty acid degradation as differentially enriched with the high LRRC19 expression in KIRC cases, which are involved in selenium therapy of clear cell renal cell carcinoma. In conclusion, low expression of LRRC19 was identified as an independent risk factor, which will advance our understanding concerning the selenium adjuvant therapy of clear cell renal cell carcinoma.


Sign in / Sign up

Export Citation Format

Share Document