scholarly journals 465 RNA SEQUENCING REVEALS UP-REGULATION OF RUNX1-RUNX1T1 GENE SIGNATURES IN CLEAR CELL RENAL CELL CARCINOMA

2013 ◽  
Vol 189 (4S) ◽  
Author(s):  
Zuquan Xiong ◽  
Qiang Ding ◽  
Jianfeng Xu ◽  
Zujun Fang
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Rna Sequencing ◽  
Renal Cell ◽  
Clear Cell ◽  
Cell Renal Cell Carcinoma ◽  
Gene Signatures

scholarly journals Molecular Profiling of Druggable Targets in Clear Cell Renal Cell Carcinoma Through Targeted RNA Sequencing

2019 ◽  
Vol 9 ◽  
Author(s):  
Corina N. A. M. van den Heuvel ◽  
Anne van Ewijk ◽  
Carolien Zeelen ◽  
Tessa de Bitter ◽  
Martijn Huynen ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Rna Sequencing ◽  
Renal Cell ◽  
Clear Cell ◽  
Molecular Profiling ◽  
Cell Renal Cell Carcinoma ◽  
Druggable Targets

A prognostic nomogram based on competing endogenous RNA network for clear-cell renal cell carcinoma

2020 ◽  
Author(s):  
Yun Peng ◽  
Shangrong Wu ◽  
Zihan Xu ◽  
Dingkun Hou ◽  
Nan Li ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Rna Sequencing ◽  
Renal Cell ◽  
Cox Regression ◽  
Clear Cell ◽  
Gene Signature ◽  
Expression Data ◽  
Competing Endogenous Rna ◽  
Cell Renal Cell Carcinoma

Abstract Backgroud Clear-cell renal cell carcinoma (ccRCC) is stubborn to traditional chemotherapy and radiation treatment, which makes its clinical management a major challenge. Recently, we have made efforts to understand the etiology of ccRCC. Increasing evidence revealed that the competing endogenous RNA (ceRNA) were involved in the development of various tumor. However, it’s scant for studying on ccRCC, and a comprehensive analysis of prognostic model based on lncRNA-miRNA-mRNA ceRNA regulatory network of ccRCC with large-scale sample size and RNA‐sequencing expression data is still limited. Methods RNA‐sequencing expression data were taken out from GTEx database and TCGA database, A total of 354 samples with ccRCC and 157 normal controlled samples were included in our study. The ccRCC-specific genes were obtained from WGCNA and differential expression analysis. Following, the communication between mRNAs and lncRNAs and target miRNAs were predicted by MiRcode, starBase, miRTarBase, and TargetScan. A gene signature of eight genes was constructed by univariate Cox regression, lasso methods and multivariate Cox regression analysis. Results A total of 2191 mRNAs and 1377 lncRNAs was identified, and a dys-regulated ceRNA network for ccRCC was established using 7 mRNAs, 363 lncRNAs, and 3 miRNAs. Further, a gene signature in cluding 8 genes based on this ceRNA was constructed, meanwhile, a nomogram predicting 1-, 3-, 5-year survival probability containing both clinical characteristics and ccRCC-specific gene signatures was developed. Conclusion It could contribute to a better understanding of ccRCC tumorigenesis mechanism and guide clinicians to make a more accurate treatment decision.

Development and confirmation of potential gene classifiers of human clear cell renal cell carcinoma using next-generation RNA sequencing

2016 ◽  
Vol 50 (6) ◽  
pp. 452-462 ◽  
Author(s):  
Oystein S. Eikrem ◽  
Philipp Strauss ◽  
Christian Beisland ◽  
Andreas Scherer ◽  
Lea Landolt ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Rna Sequencing ◽  
Renal Cell ◽  
Clear Cell ◽  
Next Generation ◽  
Cell Renal Cell Carcinoma

scholarly journals Eosinophilic features in clear cell renal cell carcinoma correlate with outcomes of immune checkpoint and angiogenesis blockade

2021 ◽  
Vol 9 (9) ◽  
pp. e002922
Author(s):  
Takashi Yoshida ◽  
Chisato Ohe ◽  
Junichi Ikeda ◽  
Naho Atsumi ◽  
Haruyuki Ohsugi ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Immune Checkpoint ◽  
Clinical Benefit ◽  
Renal Cell ◽  
Clear Cell ◽  
Therapy Group ◽  
Cell Renal Cell Carcinoma ◽  
Gene Signatures ◽  
Type Gene

BackgroundClear cell renal cell carcinoma (ccRCC) displays heterogeneity in appearance—a distinctive pale clear to eosinophilic cytoplasm; however, little is known about the underlying mechanisms and clinical implications. We investigated the role of these eosinophilic features in ccRCC on oncological outcomes and response to tyrosine kinase inhibitors (TKIs) and immune checkpoint inhibitors (ICIs).MethodsOne-hundred and thirty-eight ccRCC cases undergoing radical surgery (cohort 1) and 54 metastatic ccRCC cases receiving either TKIs or ICIs (cohort 2) were included. After histological evaluation, all cases were divided into three phenotypes based on the eosinophilic features at the highest-grade area: clear, mixed, or eosinophilic type. Gene expression and immunohistochemical analyses were performed to explore the potential mechanisms of these phenotypes in cohort 1. Further, the association of the three phenotypes with the best objective response to TKI or ICI, clinical benefit (complete/partial response or stable disease), and overall survival (OS) was assessed in cohort 2.ResultsThe clear type was significantly associated with increased hypoxia as well as angiogenesis gene signatures compared with the eosinophilic type. Gene signatures and protein expression related to effector T cell and immune checkpoint molecules were elevated to a greater extent in the eosinophilic type, followed by the mixed and clear types. The mixed and eosinophilic types exhibited greater PBRM1-negativity and increased prevalence of the epithelial-mesenchymal transition gene signature than the clear type. In the mixed/eosinophilic types of cohort 2, significant clinical benefit was observed in the ICI therapy group versus the TKI therapy group (p=0.035), and TKI therapy vs ICI therapy was an independent factor for worse prognosis of OS (HR 3.236; p=0.012).ConclusionThe histological phenotype based on the eosinophilic features, which are linked to major immunological mechanisms of ccRCC, was significantly correlated with therapeutic efficacy.

scholarly journals Comprehensive assessment gene signatures for clear cell renal cell carcinoma prognosis

Medicine ◽  
2018 ◽  
Vol 97 (44) ◽  
pp. e12679 ◽  
Author(s):  
Peng Chang ◽  
Zhitong Bing ◽  
Jinhui Tian ◽  
Jingyun Zhang ◽  
Xiuxia Li ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Comprehensive Assessment ◽  
Cell Renal Cell Carcinoma ◽  
Gene Signatures

scholarly journals Gene signatures and prognostic values of m6A regulators in clear cell renal cell carcinoma – a retrospective study using TCGA database

Aging ◽  
2019 ◽  
Vol 11 (6) ◽  
pp. 1633-1647 ◽  
Author(s):  
Jingcheng Zhou ◽  
Jiangyi Wang ◽  
Baoan Hong ◽  
Kaifang Ma ◽  
Haibiao Xie ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Retrospective Study ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Cell Renal Cell Carcinoma ◽  
Gene Signatures

scholarly journals Identification of Novel Glycolysis-Related Gene Signatures Associated With Prognosis of Patients With Clear Cell Renal Cell Carcinoma Based on TCGA

2020 ◽  
Vol 11 ◽  
Author(s):  
Chengjiang Wu ◽  
Xiaojie Cai ◽  
Jie Yan ◽  
Anyu Deng ◽  
Yun Cao ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Programming Language ◽  
Renal Cell ◽  
Clear Cell ◽  
Risk Group ◽  
Related Gene ◽  
Cell Renal Cell Carcinoma ◽  
Gene Signatures ◽  
Gene Sets

ObjectiveThe purpose of the present study was to detect novel glycolysis-related gene signatures of prognostic values for patients with clear cell renal cell carcinoma (ccRCC).MethodsGlycolysis-related gene sets were acquired from the Molecular Signatures Database (V7.0). Gene Set Enrichment Analysis (GSEA) software (4.0.3) was applied to analyze glycolysis-related gene sets. The Perl programming language (5.32.0) was used to extract glycolysis-related genes and clinical information of patients with ccRCC. The receiver operating characteristic curve (ROC) and Kaplan–Meier curve were drawn by the R programming language (3.6.3).ResultsThe four glycolysis-related genes (B3GAT3, CENPA, AGL, and ALDH3A2) associated with prognosis were identified using Cox proportional regression analysis. A risk score staging system was established to predict the outcomes of patients with ccRCC. The patients with ccRCC were classified into the low-risk group and high-risk group.ConclusionsWe have successfully constructed a risk staging model for ccRCC. The model has a better performance in predicting the prognosis of patients, which may have positive reference value for the treatment and curative effect evaluation of ccRCC.

scholarly journals Identification of Hub Genes Associated With Clear Cell Renal Cell Carcinoma by Integrated Bioinformatics Analysis

2021 ◽  
Vol 11 ◽  
Author(s):  
Hao Huang ◽  
Ling Zhu ◽  
Chao Huang ◽  
Yi Dong ◽  
Liangliang Fan ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Rna Sequencing ◽  
Renal Cell ◽  
Clear Cell ◽  
Single Gene ◽  
Gene Set Enrichment Analysis ◽  
Cancer Type ◽  
Hub Genes ◽  
Cell Renal Cell Carcinoma

BackgroundClear cell renal cell carcinoma (ccRCC) is a common genitourinary cancer type with a high mortality rate. Due to a diverse range of biochemical alterations and a high level of tumor heterogeneity, it is crucial to select highly validated prognostic biomarkers to be able to identify subtypes of ccRCC early and apply precision medicine approaches.MethodsTranscriptome data of ccRCC and clinical traits of patients were obtained from the GSE126964 dataset of Gene Expression Omnibus and The Cancer Genome Atlas Kidney Renal Clear Cell Carcinoma (TCGA-KIRC) database. Weighted gene co-expression network analysis (WGCNA) and differentially expressed gene (DEG) screening were applied to detect common differentially co-expressed genes. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes analysis, survival analysis, prognostic model establishment, and gene set enrichment analysis were also performed. Immunohistochemical analysis results of the expression levels of prognostic genes were obtained from The Human Protein Atlas. Single-gene RNA sequencing data were obtained from the GSE131685 and GSE171306 datasets.ResultsIn the present study, a total of 2,492 DEGs identified between ccRCC and healthy controls were filtered, revealing 1,300 upregulated genes and 1,192 downregulated genes. Using WGCNA, the turquoise module was identified to be closely associated with ccRCC. Hub genes were identified using the maximal clique centrality algorithm. After having intersected the hub genes and the DEGs in GSE126964 and TCGA-KIRC dataset, and after performing univariate, least absolute shrinkage and selection operator, and multivariate Cox regression analyses, ALDOB, EFHD1, and ESRRG were identified as significant prognostic factors in patients diagnosed with ccRCC. Single-gene RNA sequencing analysis revealed the expression profile of ALDOB, EFHD1, and ESRRG in different cell types of ccRCC.ConclusionsThe present results demonstrated that ALDOB, EFHD1, and ESRRG may act as potential targets for medical therapy and could serve as diagnostic biomarkers for ccRCC.

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