scholarly journals LRRC19—A Bridge between Selenium Adjuvant Therapy and Renal Clear Cell Carcinoma: A Study Based on Datamining

Genes ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 440
Author(s):  
Yitong Zhang ◽  
Jiaxing Wang ◽  
Xiqing Liu
Keyword(s):  
Gene Expression ◽  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Independent Risk Factor ◽  
Clear Cell ◽  
Clear Cell Carcinoma ◽  
Renal Clear Cell Carcinoma ◽  
Cell Renal Cell Carcinoma ◽  
Low Expression

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.

scholarly journals Liq_ccRCC: Identification of Clear Cell Renal Cell Carcinoma Based on the Integration of Clinical Liquid Indices

2021 ◽  
Vol 10 ◽  
Author(s):  
Jianhong Zhao ◽  
Jiangpeng Wu ◽  
Jinyan Wei ◽  
Xiaolu Su ◽  
Yanjun Chai ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Test Data ◽  
Renal Cell ◽  
Clear Cell ◽  
Clear Cell Carcinoma ◽  
Renal Clear Cell Carcinoma ◽  
Learning Approaches ◽  
Cell Renal Cell Carcinoma ◽  
Subtype Differentiation

Currently, preoperative diagnosis and differentiation of renal clear cell carcinoma and other subtypes remain a serious challenge for doctors. The liquid biopsy technique and artificial intelligence have inspired the pursuit of distinguishing clear cell renal cell carcinoma using clinically available test data. In this work, a method called liq_ccRCC based on the integration of clinical blood and urine indices through machine learning approaches was successfully designed to achieve this goal. Clinically available biochemical blood data and urine indices were collected from 306 patients with renal cell carcinoma. Finally, the integration of 18 top-ranked clinical liquid indices (13 blood samples and 5 urine samples) was proven to be able to distinguish renal clear cell carcinoma from other subtypes of renal carcinoma by cross-valuation with an AUC of 0.9372. The successful introduction of this identification method suggests that subtype differentiation of renal cell carcinoma can be accomplished based on clinical liquid test data, which is noninvasive and easy to perform. It has huge potential to be developed as a promising innovation strategy for preoperative subtype differentiation of renal cell carcinoma with the advantages of convenience and real-time testing. liq_ccRCC is available online for the free test of readers at http://lishuyan.lzu.edu.cn/liq_ccRCC.

Hypoxia-related lncRNA Signature to Predict the Survival of Patients with Clear Cell Renal Cell Carcinoma

2022 ◽  
Author(s):  
Fu Liu ◽  
Xinyuan Li ◽  
Xiang Zhou ◽  
Hang Tong ◽  
Xin Gou
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Risk Group ◽  
Clear Cell Carcinoma ◽  
Risk Groups ◽  
Renal Clear Cell Carcinoma ◽  
Low Risk ◽  
Cell Renal Cell Carcinoma

Abstract Background: Renal cell carcinoma is the most common aggressive tumor of the genitourinary system. The main pathological subtype is clear cell renal cell carcinoma (ccRCC), and its treatment options are very limited. Therefore, identifying specific markers of renal clear cell carcinoma is of great significance for diagnosis and prognosis.Methods: From the TCGA database, we obtained information on 611 patients with renal clear cell carcinoma to analyze the relationship between hypoxia-related lncRNAs and overall survival. According to the coexpression of hypoxia genes and lncRNAs, genes related to hypoxia were identified. Difference analysis and Cox regression analysis were applied to assess survival-related risk factors. According to the median risk score of hypoxia-related genes, patients were divided into high-risk and low-risk groups. According to these gene characteristics and clinical parameters, a nomogram map was built, and GSEA was used for gene function annotation. RT-qRCR, Western Blot and Flow Cytometry were used to determine the role of SNHG19 in RCC cells.Results: By analyzing the coexpression of hypoxia genes and lncRNAs, 310 hypoxia-related genes were obtained. Six sHRlncRs were significantly correlated with the clinical outcomes of patients with ccRCC. Four sHRlncRs (AC011445.2, PTOV1-AS2, AP004609.3, and SNHG19) with the highest prognostic values were included in the group to construct the HRRS model. The high-risk group had a shorter OS than the low-risk group. HR-lncRNAs were considered to be an independent prognostic factor and associated with OS. The high- and low-risk groups showed different pathways in GSEA. Experiments showed that SNHG19 plays essential roles in autophagy and apoptosis of RCC cells.Conclusion: Our research shows that we established and verified a hypoxia-related lncRNA model that accurately correlates with ccRCC patients. This study also provides novel insights into hypoxia-based mechanisms and provides new biomarkers for the poor prognosis of ccRCC patients.

scholarly journals Patterns of gene expression characterize T1 and T3 clear cell renal cell carcinoma subtypes

PLoS ONE ◽  
2019 ◽  
Vol 14 (5) ◽  
pp. e0216793 ◽  
Author(s):  
Agnieszka M. Borys ◽  
Michał Seweryn ◽  
Tomasz Gołąbek ◽  
Łukasz Bełch ◽  
Agnieszka Klimkowska ◽  
...  
Keyword(s):  
Gene Expression ◽  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Cell Renal Cell Carcinoma

scholarly journals Personalised drug repositioning for Clear Cell Renal Cell Carcinoma using gene expression

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
Keyword(s):  
Gene Expression ◽  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Drug Repositioning ◽  
Cell Renal Cell Carcinoma

scholarly journals Prognostic Value of DNA Methylation-Driven Genes in Clear Cell Renal Cell Carcinoma: A Study Based on Methylation and Transcriptome Analyses

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Maolin Hu ◽  
Jiangling Xie ◽  
Huiming Hou ◽  
Ming Liu ◽  
Jianye Wang
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Risk Score ◽  
Renal Cell ◽  
Methylation Level ◽  
Cox Regression ◽  
Clear Cell ◽  
Cell Renal Cell Carcinoma

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.

Low Expression of ATM Indicates a Poor Prognosis in Clear Cell Renal Cell Carcinoma

2019 ◽  
Vol 17 (3) ◽  
pp. e433-e439 ◽  
Author(s):  
Wenbiao Ren ◽  
Bichen Xue ◽  
Minfeng Chen ◽  
Longfei Liu ◽  
Xiongbing Zu
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Poor Prognosis ◽  
Clear Cell ◽  
Cell Renal Cell Carcinoma ◽  
Low Expression

Validation of Gene Expression Signatures to Identify Low-risk Clear-cell Renal Cell Carcinoma Patients at Higher Risk for Disease-related Death

2016 ◽  
Vol 2 (6) ◽  
pp. 608-615 ◽  
Author(s):  
Mansi Parasramka ◽  
Daniel J. Serie ◽  
Yan W. Asmann ◽  
Jeanette E. Eckel-Passow ◽  
Erik P. Castle ◽  
...  
Keyword(s):  
Gene Expression ◽  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Low Risk ◽  
Cell Renal Cell Carcinoma ◽  
Gene Expression Signatures
Sign in / Sign up

Export Citation Format

Share Document