Differential expression analysis of clear cell renal cell carcinomas in The Cancer Genome Atlas distinguishes an aggressive subset enriched with chromosomes 7 and 12 gains

2020 ◽  
Vol 76 (7) ◽  
pp. 950-958
Author(s):  
Chin‐Chen Pan ◽  
Yi‐Chen Yeh ◽  
Yu‐Chao Wang ◽  
Yen‐Hwa Chang
Keyword(s):  
Differential Expression ◽  
Expression Analysis ◽  
Renal Cell ◽  
Clear Cell ◽  
Differential Expression Analysis ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Renal Cell Carcinomas ◽  
Cancer Genome Atlas ◽  
Genome Atlas

scholarly journals Renal cell tumors with clear cell histology and intact VHL and chromosome 3p: a histological review of tumors from the Cancer Genome Atlas database

2017 ◽  
Vol 30 (11) ◽  
pp. 1603-1612 ◽  
Author(s):  
Laura Favazza ◽  
Dhananjay A Chitale ◽  
Ravi Barod ◽  
Craig G Rogers ◽  
Shanker Kalyana-Sundaram ◽  
...  
Keyword(s):  
Renal Cell ◽  
Clear Cell ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Chromosome 3P ◽  
Renal Cell Tumors ◽  
Cancer Genome Atlas ◽  
Genome Atlas

Association of mutations in chromatin modifiers with poor survival in clear cell renal cell carcinoma: Analysis of the Cancer Genome Atlas Project.

2013 ◽  
Vol 31 (6_suppl) ◽  
pp. 360-360 ◽  
Author(s):  
A. Ari Hakimi ◽  
Irina Ostrovnaya ◽  
Martin Henner Voss ◽  
Robert John Motzer ◽  
Paul Russo ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Competing Risk ◽  
The Cancer Genome Atlas ◽  
Cell Renal Cell Carcinoma ◽  
Cancer Specific Survival ◽  
Epigenetic Modifiers ◽  
Cancer Genome Atlas ◽  
Genome Atlas

360 Background: We have previously shown that mutations in the epigenetic modifiers PBRM1, BAP1, SETD2 and KDM5C are associated with adverse tumor characteristics and, in some cases, worse cancer specific survival in clear cell renal cell carcinoma (ccRCC). We analyzed publically available data from the Cancer Genome Atlas Project (TCGA), to assess the impact of mutations in these genes on cancer-specific survival. Methods: We analayzed the genomic and clinical data from the TCGA cohort of 424 patients with primary ccRCC. The Kaplan-Meier method was used to estimate the survival probabilities, and log-rank test was used to test the univariate association between mutation status and overall survival. Cancer specific survival (CSS) was analyzed using the competing risk method. Multivariate Cox proportional hazard regression and competing risk models were also fitted to adjust for the validated Mayo Clinic SSIGN prognostic score. Results: Mutations in these epigenetic modifiers are frequent (PBRM1, 33.7%; SETD2, 11.6%; BAP1, 9.7%, KDM5C, 5.7%). BAP1 (p=0.002, HR 2.21 [1.34-3.62]), SETD2 (p=0.036, HR 1.68 [1.03-2.72]) and KDM5C (p=0.016, HR 2.18 [1.16-4.11]) are associated with worse CSS by competing risk. When adjusting for the prognostic SSIGN score, only mutations in KDM5C remain significant (p<0.0001 HR 4.03 [2.1-7.9]). On the contrary, PBRM1 mutations, the second most common gene mutations of ccRCC, have no impact on CSS. Conclusions: BAP1, SETD2 and KDM5C mutations are associated with worse CSS, suggesting their roles in disease progression. PBRM1 mutations do not impact CSS, implicating its principal role in the tumor initiation. Future efforts should focus on therapeutic interventions and further clinical, pathologic and molecular interrogation of this novel class of tumor suppressors.

scholarly journals A Novel Prognostic Model for Clear Cell Renal Cell Carcinoma with Differentially Expressed Immune-related lncRNA Pairs Based on the Cancer Genome Atlas

2021 ◽  
Author(s):  
Chen Zhao ◽  
Kewei Xiong ◽  
Fengming Liu ◽  
Xiangpan Li
Keyword(s):  
Renal Cell Carcinoma ◽  
Renal Cell ◽  
Prognostic Model ◽  
Cox Regression ◽  
Clear Cell ◽  
Risk Group ◽  
The Cancer Genome Atlas ◽  
Cell Renal Cell Carcinoma ◽  
Cancer Genome Atlas ◽  
Genome Atlas

Abstract Objective: To construct a novel prognostic model of immune-related lncRNA (irlncRNA) pairs in clear cell renal cell carcinoma (ccRCC). Methods: RNA-seq and clinical data were retrieved from The Cancer Genome Atlas (TCGA). Differentially expressed irlncRNAs (DEirlncRNAs) were obtained by co-expression strategy with immune genes. A 0-1 matrix was constructed according to DEirlncRNAs relevant expression levels. Univariate cox regression was used to select potential target pairs. Lasso regression with cross validation and multivariate cox regression were carried out to extract the final biomarker pairs for risk score calculation. Through calculating the optimal cutoff of AUCs, patients were divided into high and low risk group. Model validation was conducted by independent prognostic analysis, survival analysis, tumor-infiltrating and chemosensitivity analysis. Results: A total of 42 DEirlncRNAs were identified and 12 target pairs were included to construct the final model. The risk score were both significantly different according to univariate (p<0.001, HR=1.391, 95%CI [1.313–1.475]) and multivariate cox regression (p<0.001, HR=1.3104, 95%CI [1.227-1.399]). The AUC reached 0.765 at 1-year, 0.724 at 3-year and 0.785 at 5-year. Patients in the high-risk group had significantly poor survival, higher level of CD8+T infiltration, lower drug sensitivity of sunitinib and temsirolimus but higher sensitivity of lapatinib and pazopanib.Conclusion: The novel prognostic model constructed by paring irlncRNAs showed an effective clinical prediction in ccRCC patients.

scholarly journals The Cancer Genome Atlas Comprehensive Molecular Characterization of Renal Cell Carcinoma

Cell Reports ◽  
2018 ◽  
Vol 23 (12) ◽  
pp. 3698 ◽  
Author(s):  
Christopher J. Ricketts ◽  
Aguirre A. De Cubas ◽  
Huihui Fan ◽  
Christof C. Smith ◽  
Martin Lang ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Molecular Characterization ◽  
Renal Cell ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Cancer Genome Atlas ◽  
Genome Atlas
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