Genome-wide gene expression profiles of clear cell renal cell carcinoma: Identification of molecular targets for treatment of 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.

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

Medical Science Monitor ◽

10.12659/msm.919965 ◽

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

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MP47-10 MICRORNA EXPRESSION PROFILES FOR RENAL MASS BIOPSY: A NOVEL TOOL TO AID IN THE STRATIFICATION OF PATIENTS WITH CLEAR CELL RENAL CELL CARCINOMA

The Journal of Urology ◽

10.1016/j.juro.2015.02.1530 ◽

2015 ◽

Vol 193 (4S) ◽

Author(s):

Drew Palmer ◽

Casey Kowalik ◽

Patrick Teebagy ◽

Kari Bailey ◽

Shiv Patel ◽

...

Keyword(s):

Renal Cell Carcinoma ◽

Cell Carcinoma ◽

Renal Cell ◽

Renal Mass ◽

Clear Cell ◽

Expression Profiles ◽

Microrna Expression ◽

Cell Renal Cell Carcinoma ◽

Renal Mass Biopsy

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

BioMed Research International ◽

10.1155/2018/9303486 ◽

2018 ◽

Vol 2018 ◽

pp. 1-13 ◽

Cited By ~ 10

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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Development and Interpretation of a Genomic Instability Derived lncRNAs Based Risk Signature as a Predictor of Prognosis for Clear Cell Renal Cell Carcinoma Patients

Frontiers in Oncology ◽

10.3389/fonc.2021.678253 ◽

2021 ◽

Vol 11 ◽

Author(s):

Huiying Yang ◽

Xiaoling Xiong ◽

Hua Li

Keyword(s):

Renal Cell Carcinoma ◽

High Risk ◽

Cell Carcinoma ◽

Genomic Instability ◽

Renal Cell ◽

Clear Cell ◽

Expression Profiles ◽

Enrichment Analysis ◽

Cell Renal Cell Carcinoma ◽

Prognosis Prediction

BackgroundClear cell renal cell carcinoma (ccRCC) is a kind of frequently diagnosed cancer, leading to high death rate in patients. Genomic instability (GI) is regarded as playing indispensable roles in tumorigenesis and impacting the prognosis of patients. The aberrant regulation of long non-coding RNAs (lncRNAs) is a main cause of GI. We combined the somatic mutation profiles and expression profiles to identify GI derived lncRNAs (GID-lncRNAs) in ccRCC and developed a GID-lncRNAs based risk signature for prognosis prediction and medication guidance.MethodsWe decided cases with top 25% cumulative number of somatic mutations as genomically unstable (GU) group and last 25% as genomically stable (GS) group, and identified differentially expressed lncRNAs (GID-lncRNAs) between two groups. Then we developed the risk signature with all overall survival related GID-lncRNAs with least absolute shrinkage and selection operator (LASSO) Cox regression. The functions of the GID-lncRNAs were partly interpreted by enrichment analysis. We finally validated the effectiveness of the risk signature in prognosis prediction and medication guidance.ResultsWe developed a seven-lncRNAs (LINC00460, AL139351.1, AC156455.1, AL035446.1, LINC02471, AC022509.2, and LINC01606) risk signature and divided all samples into high-risk and low-risk groups. Patients in high-risk group were in more severe clinicopathologic status (higher tumor grade, pathological stage, T stage, and more metastasis) and were deemed to have less survival time and lower survival rate. The efficacy of prognosis prediction was validated by receiver operating characteristic analysis. Enrichment analysis revealed that the lncRNAs in the risk signature mainly participate in regulation of cell cycle, DNA replication, material metabolism, and other vital biological processes in the tumorigenesis of ccRCC. Moreover, the risk signature could help assess the possibility of response to precise treatments.ConclusionOur study combined the somatic mutation profiles and the expression profiles of ccRCC for the first time and developed a GID-lncRNAs based risk signature for prognosis predicting and therapeutic scheme deciding. We validated the efficacy of the risk signature and partly interpreted the roles of the seven lncRNAs composing the risk signature in ccRCC. Our study provides novel insights into the roles of genomic instability derived lncRNAs in ccRCC.

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