scholarly journals Integrated Analysis of Three Publicly Available Gene Expression Profiles Identified Genes and Pathways Associated with Clear Cell Renal Cell Carcinoma

2020 ◽  
Vol 26 ◽  
Author(s):  
YuPing Han ◽  
LinLin Wang ◽  
Ye Wang
Keyword(s):  
Gene Expression ◽  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Integrated Analysis ◽  
Cell Renal Cell Carcinoma

Categorization of gene expression subtypes in clear cell renal cell carcinoma with sarcomatoid features.

2017 ◽  
Vol 35 (6_suppl) ◽  
pp. 519-519
Author(s):  
Shreyas Joshi ◽  
Suraj Peri ◽  
Eric A. Ross ◽  
Robert G. Uzzo ◽  
Alexander Kutikov ◽  
...  
Keyword(s):  
Gene Expression ◽  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Gene Expression Patterns ◽  
Cell Renal Cell Carcinoma

519 Background: Presence of sarcomatoid features in Renal Cell Carcinoma (sRCC) tumors signals aggressive clinical behavior and poor prognosis compared to Clear Cell Renal Cell Carcinoma (ccRCC). However, the underlying gene expression patterns of sRCC are poorly understood. We sought to categorize ccRCC and sRCC gene expression subtypes and compare survival outcomes, as well as evaluate whether sRCC gene expression patterns are similar to non-renal sarcomas. Methods: We identified 511 ccRCC cases, of which 36 had a sarcomatoid component from The Cancer Genome Atlas. Enrichment analysis was used to measure associations between gene expression signatures for soft tissue sarcomas and expression profiles of sRCC and ccRCC cases measured by RNA-Seq. The resulting scores were used to identify distinct patient groups using K-means clustering. Overall survival (OS) was evaluated by Kaplan-Meier, log rank, and Cox regression methods. Results: Our analysis identified 4 distinct clusters that differ in enrichment for soft-tissue sarcoma gene expression profiles. The clusters showed significantly different OS distributions (p-value<0.001 log rank). Most sRCC cases (69%) segregated into a single cluster with the worst prognosis. Among ccRCC cases, 57% of patients with higher levels of sarcoma signature enrichment were associated with a shorter OS, which is independent of tumor stage. 5-year/median OS survival estimates for ccRCC cases in the 4 clusters, by increasing levels of sarcoma profile enrichment, were 83%/NR, 75%/NR, 67%/90.9 mo, and 49%/56.7 mo. We also validated existence of these clusters in another sRCC cohort (Sircar 2015). Conclusions: We identified strong associations between sarcoma expression signatures and gene expression profiles of sRCC. We also found that 57% of morphologically non-sRCC cases demonstrate enrichment for sarcoma expression signatures, and these patients have worse OS than their non-sarcoma enriched ccRCC counterparts. The presence of sarcoma expression signatures has not been previously evaluated in RCC. These signatures portend poor survival and may be clinically actionable, as they describe unique subtypes of RCC that may not correspond to histologic characterization.

scholarly journals Transcriptomic characterization and innovative molecular classification of clear cell renal cell carcinoma in the Chinese population

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Qiang Zhao ◽  
Jia Xue ◽  
Baoan Hong ◽  
Wubin Qian ◽  
Tiezhu Liu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Chinese Population ◽  
Clear Cell ◽  
Expression Profiles ◽  
Molecular Classification ◽  
The Cancer Genome Atlas ◽  
Cell Renal Cell Carcinoma

Abstract Background Large-scale initiatives like The Cancer Genome Atlas (TCGA) performed genomics studies on predominantly Caucasian kidney cancer. In this study, we aimed to investigate genomics of Chinese clear cell renal cell carcinoma (ccRCC). Methods We performed whole-transcriptomic sequencing on 55 tumor tissues and 11 matched normal tissues from Chinese ccRCC patients. We systematically analyzed the data from our cohort and comprehensively compared with the TCGA ccRCC cohort. Results It found that PBRM1 mutates with a frequency of 11% in our cohort, much lower than that in TCGA Caucasians (33%). Besides, 31 gene fusions including 5 recurrent ones, that associated with apoptosis, tumor suppression and metastasis were identified. We classified our cohort into three classes by gene expression. Class 1 shows significantly elevated gene expression in the VEGF pathway, while Class 3 has comparably suppressed expression of this pathway. Class 2 is characterized by increased expression of extracellular matrix organization genes and is associated with high-grade tumors. Applying the classification to TCGA ccRCC patients revealed better distinction of tumor prognosis than reported classifications. Class 2 shows worst survival and Class 3 is a rare subtype ccRCC in the TCGA cohort. Furthermore, computational analysis on the immune microenvironment of ccRCC identified immune-active and tolerant tumors with significant increased macrophages and depleted CD4 positive T-cells, thus some patients may benefit from immunotherapies. Conclusion In summary, results presented in this study shed light into distinct genomic expression profiles in Chinese population, modified the stratification patterns by new molecular classification, and gave practical guidelines on clinical treatment of ccRCC patients.

Screening and identification of novel promoter signatures in clear cell renal cell carcinoma metastasis.

2020 ◽  
Vol 38 (6_suppl) ◽  
pp. 737-737
Author(s):  
Yuan-Yuan Qu ◽  
Xi Tian ◽  
Wenhao Xu ◽  
Aihemutaijiang Anwaier ◽  
Dingwei Ye ◽  
...  
Keyword(s):  
Gene Expression ◽  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Cox Regression ◽  
Clear Cell ◽  
Expression Profiles ◽  
Functional Enrichment ◽  
Hub Genes ◽  
Cell Renal Cell Carcinoma

737 Background: Clear cell renal cell carcinoma (ccRCC) patient usually face aggressive progression when metastasis occurs. Therefore, in-depth investigation is needed to elucidate underlying mechanisms behind the metastasis of ccRCC to promote therapeutic benefits.This study aims to explore and investigate prognostic gene expression profiles based on multi-cohorts. Methods: Three microarray datasets were obtained from the Gene Expression Omnibus (GEO) database to screen and identify differentially expressed genes (DEGs) according to normalization annotation information. A total of 112 DEGs with functional enrichment were identified as candidate prognostic biomarkers. A protein–protein interaction network (PPI) of DEGs was developed, and the modules were analyzed using STRING and Cytoscape. Results: LASSO Cox regression suggested 31 significant involved genes, and 10 hub genes were identified as independent oncogenes in ccRCC patients. Distinct integrated scores of the hub genes mRNA expression showed statistical significance in predicting disease-free survival (DFS; p<0.001) and overall survival (OS; p<0.001) in TCGA and real-world cohorts. Meanwhile, ROC curves were constructed to validate specificity and sensitivity of the Cox regression penal to predict prognosis. The AUC index for the integrated genes scores was 0.758 for OS and 0.772 for DFS. Conclusions: In conclusion,the present study identifies DEGs and hub genes that may be involved in earlier recurrence and poor prognosis of ccRCC. The expression levels of ADAMTS9, C1S, DPYSL3, H2AFX, MINA, PLOD2, RUNX1, SLC19A1, TPX2 and TRIB3 are of high prognostic value, and may help us understand better the underlying carcinogenesis or progression of ccRCC.

scholarly journals Full-Length Enrich c-DNA Libraries-Clear Cell-Renal Cell Carcinoma

2012 ◽  
Vol 2012 ◽  
pp. 1-15 ◽  
Author(s):  
Sai-Wen Tang ◽  
Jung-Yaw Lin
Keyword(s):  
Gene Expression ◽  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Molecular Mechanisms ◽  
Clear Cell ◽  
Expression Profiles ◽  
Full Length ◽  
Cdna Libraries ◽  
Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC), the most common subtype of RCC, is characterized by high metastasis potential and strong resistance to traditional therapies, resulting in a poor five-year survival rate of patients. Several therapies targeted to VEGF pathway for advanced RCC have been developed, however, it still needs to discover new therapeutic targets for treating RCC. Genome-wide gene expression analyses have been broadly used to identify unknown molecular mechanisms of cancer progression. Recently, we applied the oligo-capping method to construct the full-length cDNA libraries of ccRCC and adjacent normal kidney, and analyzed the gene expression profiles by high-throughput sequencing. This paper presents a review for recent findings on therapeutic potential of MYC pathway and nicotinamide N-methyltransferase for the treatment of RCC.

scholarly journals A Novel mRNA-miRNA Regulatory Sub-Network Associated With Prognosis of Metastatic Clear Cell Renal Cell Carcinoma

2021 ◽  
Vol 10 ◽  
Author(s):  
Tianyu Yang ◽  
Xiaofen Miao ◽  
Zhanxiang Bai ◽  
Jian Tu ◽  
Shanshan Shen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Expression Profile ◽  
Renal Cell ◽  
Bioinformatics Analysis ◽  
Clear Cell ◽  
Integrated Analysis ◽  
Cell Renal Cell Carcinoma ◽  
High Incidence

BackgroundClear cell renal cell carcinoma (ccRCC) is a urinary disease with high incidence. The high incidence of metastasis is the leading cause of death in patients with ccRCC. This study was aimed to identify the gene signatures during the metastasis of ccRCC.MethodsTwo datasets, including one gene expression profile dataset and one microRNA (miRNA) expression profile dataset, were downloaded from Gene Expression Omnibus (GEO) database. The integrated bioinformatics analysis was performed using the (limma) R package, miRWalk, DAVID, STRING, Kaplan-Meier plotter databases. Quantitative real-time polymerase chain reaction (qPCR) was conducted to validate the expression of differentially expressed genes (DEGs) and DE-miRNAs.ResultsIn total, 84 DEGs (68 up-regulated and 16 down-regulated) and 41 DE-miRNAs (24 up-regulated and 17 down-regulated) were screened from GSE22541 and GSE37989 datasets, respectively. Furthermore, 11 hub genes and 3 key miRNAs were identified from the PPI network, including FBLN1, THBS2, SCGB1A1, NKX2-1, COL11A1, DCN, LUM, COL1A1, COL6A3, SFTPC, SFTPB, miR-328, miR-502, and miR-504. The qPCR data showed that most of the selected genes and miRNAs were consistent with that in our integrated analysis. A novel mRNA-miRNA network, SFTPB-miR-328-miR-502-miR-504-NKX2-1 was found in metastatic ccRCC after the combination of data from expression, survival analysis, and experiment validation.ConclusionIn conclusion, key candidate genes and miRNAs were identified and a novel mRNA-miRNA network was constructed in ccRCC metastasis using integrated bioinformatics analysis and qPCR validation, which might be utilized as diagnostic biomarkers and molecular targets of metastatic ccRCC.

Use of meta-analysis of clear cell renal cell carcinoma gene expression to define a variant subgroup and identify gender influences on tumor biology.

2012 ◽  
Vol 30 (5_suppl) ◽  
pp. 412-412 ◽  
Author(s):  
Scott Mattox Haake ◽  
A. Rose Brannon ◽  
Kate Hacker ◽  
Raj Pruthi ◽  
Eric Wallen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Expression Profiles ◽  
Meta Analysis ◽  
Cell Renal Cell Carcinoma ◽  
Tumor Histology ◽  
New Variant

412 Background: Clear cell renal cell carcinoma (ccRCC) displays molecular and histological heterogeneity. Previously described subsets of this disease, ccA and ccB, were defined based on multi-gene expression profiles, but it is unclear if these subgroupings reflect the full spectrum of disease or how these molecular subtypes relate to histological descriptions or gender. We sought to determine whether additional subtypes of ccRCC exist, and whether these subtypes are related to VHL inactivation, HIF1/HIF2 expression, tumor histology, or gender. Methods: Six large publically available ccRCC gene expression databases were identified that cumulatively provided data for 480 tumors for meta-analysis via meta-array compilation. Unsupervised consensus clustering was performed on the meta-arrays. Tumors were examined for the relationship of multigene-defined consensus subtypes and expression signatures of VHL mutation and HIF status, tumor histology, and gender. Results: Two dominant subsets of ccRCC were observed. However, a minor third cluster was revealed which correlated strongly with a wild type VHL expression profile and indications of variant histologies. When variant histologies were removed, ccA tumors naturally divided by gender. This technique is limited by potential for persistent batch effect, tumor sampling bias, and restrictions of annotated information. Conclusions: ccA and ccB subsets of ccRCC are robust in meta-analysis among histologically conventional ccRCC tumors. A third group of tumors was identified, which may represent a new variant of ccRCC. Within definitively clear cell tumors, gender may delineate tumors in such a way that could have implications regarding current treatments and future drug development.

scholarly journals Identification of Six Potentially Long Noncoding RNAs as Biomarkers Involved Competitive Endogenous RNA in Clear Cell Renal Cell Carcinoma

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Kang Yang ◽  
Xiao-fan Lu ◽  
Peng-cheng Luo ◽  
Jie Zhang
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Expression Profiles ◽  
Noncoding Rnas ◽  
Long Noncoding Rnas ◽  
The Cancer Genome Atlas ◽  
Cell Renal Cell Carcinoma ◽  
Cerna Network

Background. Clear cell renal cell carcinoma (ccRCC), the most common subtype of renal cell carcinoma (RCC), usually is representative of metastatic heterogeneous neoplasm that links with poor prognosis, but the pathogenesis of ccRCC remains unclear. Currently, numerous evidences prove that long noncoding RNAs (lncRNAs) are considered as competing endogenous RNA (ceRNA) to participate in cellular processes of tumors. Therefore, to investigate the underlying mechanisms of ccRCC, the expression profiles of lncRNAs, miRNAs, and mRNAs were downloaded from the Cancer Genome Atlas (TCGA) database. A total of 1526 differentially expressed lncRNAs (DElncRNAs), 54 DEmiRNAs, and 2352 DEmRNAs were identified. To determine the connection of them, all DElncRNAs were input to the miRcode database. The results indicated that 85 DElncRNAs could connect with 9 DEmiRNAs in relation to our study. Then, databases of TargetScan and miRDB were used to search for targeted genes with reference to DEmiRNAs. The results showed that 203 out of 2352 targeted genes were identified in our TCGA set. Subsequently, ceRNA network was constructed according to Cytoscape and the targeted genes were functionally analyzed to elucidate the mechanisms of DEmRNAs. The results of survival analysis and regression analysis indicated that 6 DElncRNAs named COL18A1-AS1, WT1-AS, LINC00443, TCL6, AL356356.1, and SLC25A5-AS1 were significantly correlative with the clinical traits of ccRCC patients and could be served as predictors for ccRCC. Finally, these findings were validated by quantitative RT-PCR (qRT-PCR). Based on these discoveries, we believe that this identified ceRNA network will provide a novel perspective to elucidate ccRCC pathogenesis.

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