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 ◽  
...  
2019 ◽  
Vol 9 ◽  
Author(s):  
Corina N. A. M. van den Heuvel ◽  
Anne van Ewijk ◽  
Carolien Zeelen ◽  
Tessa de Bitter ◽  
Martijn Huynen ◽  
...  

2020 ◽  
Author(s):  
Yun Peng ◽  
Shangrong Wu ◽  
Zihan Xu ◽  
Dingkun Hou ◽  
Nan Li ◽  
...  

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.


2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e16070-e16070
Author(s):  
Jamal Zekri ◽  
Abdelrazak Meliti ◽  
Mohammed Abhas Baghdadi ◽  
Turki Sobahy ◽  
Saba Imtiaz

e16070 Background: The management of the clear cell renal cell carcinoma (cc-RCC) has evolved over the past decade. Clinical patients’ and tumor characteristics have a limited role in predicting the outcome of these patients. The search for reliable prognostic and predictive biomarkers should be pursued in particular during the current era of next generation sequencing (NGS) technology. Methods: Formalin-Fixed Paraffin-Embedded (FFPE) tissue specimens of cc-RCC were sequenced using NGS and a customized gene panel testing for 72 tumor-related genes. High potential variants were defined by mutation effect (stop-loss, stop-gain, frame-shift substitutions or non-synonymous SNV) and class (pathogenic or likely pathogenic). Cases with identical variants were identified. Results: In total, all 47 cases had 69,052 variants, of which 20,453 were classified as high potential variants. Identical alterations in 15 genes were present in all samples. These genes are: MUC3A, MUC12, MUC7, SRRT, MUC2, MUC5AC, MUC5B, MUC22, MUC6, CR1, MUC4, MUC16, MUC19, MUC17 and MERTK. The numbers of identical and non-identical variants in these 15 genes were counted for each sample. Median number of variants was 377 and was selected as a cut off to define cases with high ( > 377) and low (≤377) variants number (HVN and LVN respectively). For the whole cohort, HVN was associated with shorter overall survival compared to LVN (Median 50 months vs. not reached; Log Rank P = 0.041). In the 11 patients who received anti-angiogenic tyrosine kinase inhibitors (TKIs), HVN was associated with a trend of shorter progression free survival (Median 5 vs. 10 months; Log Rank P = not significant). Conclusions: Alterations in SRRT, CR1, MERTK and MUCIN family genes are very common in RCC. HVN ( > 377) is associated with worse prognosis and may predict decreased benefit from anti-angiogenic TKIs.


2014 ◽  
Vol 9 (1) ◽  
pp. 32-43 ◽  
Author(s):  
Gabriel G. Malouf ◽  
Jianping Zhang ◽  
Ying Yuan ◽  
Eva Compérat ◽  
Morgan Rouprêt ◽  
...  

PLoS ONE ◽  
2012 ◽  
Vol 7 (6) ◽  
pp. e38298 ◽  
Author(s):  
Susanne Osanto ◽  
Yongjun Qin ◽  
Henk P. Buermans ◽  
Johannes Berkers ◽  
Evelyne Lerut ◽  
...  

2021 ◽  
Vol 11 ◽  
Author(s):  
Hao Huang ◽  
Ling Zhu ◽  
Chao Huang ◽  
Yi Dong ◽  
Liangliang Fan ◽  
...  

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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