scholarly journals Identification of aberrant tRNA-halves expression patterns in clear cell renal cell carcinoma

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Malin Nientiedt ◽  
Mario Deng ◽  
Doris Schmidt ◽  
Sven Perner ◽  
Stefan C. Müller ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Expression Patterns ◽  
Cell Renal Cell Carcinoma ◽  
Trna Halves

MicroRNA as novel blood-based biomarkers in clear cell renal cell carcinoma.

2013 ◽  
Vol 31 (6_suppl) ◽  
pp. 375-375 ◽  
Author(s):  
A. Ari Hakimi ◽  
Anders Jacobsen ◽  
Nina Mikkilineni ◽  
Brandon Fiegoli ◽  
Sara Blass ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Mirna Expression ◽  
Renal Cell ◽  
Clear Cell ◽  
Expression Patterns ◽  
Integrative Approach ◽  
Cell Renal Cell Carcinoma ◽  
Potential Biomarker ◽  
Plasma Mirna

375 Background: MicroRNAs (miRNA) are short, non-coding RNAs involved in post-transcriptional gene regulation. Several reports have assessed their role as blood based biomarkers given their tissue and cancer-specific expression. Using an integrative approach we sequenced the miRNA transcriptome of the plasma of several clear cell renal cell carcinoma (ccRCC) patients both before and after surgery as well as several controls. Methods: We performed next generation miRNA sequencing (miRNAseq) on eight pairs (pre- and post-operative plasma samples) and four non-cancer controls to identify potential biomarker candidates. We further integrated our data with the miRNAseq tumor data from the Cancer Genome Atlas (TCGA) study to determine whether plasma miRNA levels are representative of tumor miRNA expression in ccRCC. Results: Overall, 930 unique miRNAs were detected, including 272 at greater than or equal to 10 read counts. There was a global shift of miRNA expression toward the non-cancer controls in the postoperative samples compared to preoperative. We further identified several stably expressed miRNAs across all samples and controls including miR-16, miR-191, and miR-103. We also identified several potential biomarker candidates by looking at differential expression both in terms of preoperative and postoperative status, as well as tumor vs. control including miR-378 and miR-660. Intriguingly, the plasma miRNA expression patterns showed no relationship to the tumor expression patterns using the TCGA samples. Conclusions: Plasma miRNA expression patterns are consistently altered in ccRCC and, following surgery, globally revert to the non-cancerous levels of the controls. Several biomarker candidates have been identified and a panel is undergoing validation in larger cohorts. Plasma miRNA levels do not appear to reflect tumor levels in ccRCC.

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 Clinicopathological and Molecular Prognostic Classifier for Intermediate/High-Risk Clear Cell Renal Cell Carcinoma

Cancers ◽  
2021 ◽  
Vol 13 (24) ◽  
pp. 6338
Author(s):  
Fiorella L. Roldán ◽  
Juan J. Lozano ◽  
Mercedes Ingelmo-Torres ◽  
Raquel Carrasco ◽  
Esther Díaz ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
High Risk ◽  
Tumor Progression ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Predictive Accuracy ◽  
Molecular Model ◽  
Expression Patterns ◽  
Cell Renal Cell Carcinoma

The probability of tumor progression in intermediate/high-risk clear cell renal cell carcinoma (ccRCC) is highly variable, underlining the lack of predictive accuracy of the current clinicopathological factors. To develop an accurate prognostic classifier for these patients, we analyzed global gene expression patterns in 13 tissue samples from progressive and non-progressive ccRCC using Illumina Hi-seq 4000. Expression levels of 22 selected differentially expressed genes (DEG) were assessed by nCounter analysis in an independent series of 71 ccRCCs. A clinicopathological-molecular model for predicting tumor progression was developed and in silico validated in a total of 202 ccRCC patients using the TCGA cohort. A total of 1202 DEGs were found between progressive and non-progressive intermediate/high-risk ccRCC in RNAseq analysis, and seven of the 22 DEGs selected were validated by nCounter. Expression of HS6ST2, pT stage, tumor size, and ISUP grade were found to be independent prognostic factors for tumor progression. A risk score generated using these variables was able to distinguish patients at higher risk of tumor progression (HR 7.27; p < 0.001), consistent with the results obtained from the TCGA cohort (HR 2.74; p < 0.002). In summary, a combined prognostic algorithm was successfully developed and validated. This model may aid physicians to select high-risk patients for adjuvant therapy.

5′-tRNA Halves are Dysregulated in Clear Cell Renal Cell Carcinoma

2018 ◽  
Vol 199 (2) ◽  
pp. 378-383 ◽  
Author(s):  
Chenming Zhao ◽  
Yuri Tolkach ◽  
Doris Schmidt ◽  
Glen Kristiansen ◽  
Stefan C. Müller ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Cell Renal Cell Carcinoma ◽  
Trna Halves
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