Types of alternative splice variants of transcription factor 19 and their expression patterns in clear cell renal cell carcinoma tissues

2010 ◽  
Vol 30 (7) ◽  
pp. 706-710
Author(s):  
Tao QIAN ◽  
Li-ping LIN ◽  
Wen-jun CHANG ◽  
Ting-hu CAO ◽  
Xing-xing XU ◽  
...  
2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Malin Nientiedt ◽  
Mario Deng ◽  
Doris Schmidt ◽  
Sven Perner ◽  
Stefan C. Müller ◽  
...  

2013 ◽  
Vol 31 (6_suppl) ◽  
pp. 375-375 ◽  
Author(s):  
A. Ari Hakimi ◽  
Anders Jacobsen ◽  
Nina Mikkilineni ◽  
Brandon Fiegoli ◽  
Sara Blass ◽  
...  

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.


2019 ◽  
Author(s):  
Yanyan Zhu ◽  
Shundong Cang ◽  
Bowang Chen ◽  
Yue Gu ◽  
Miaomiao Jiang ◽  
...  

AbstractClear cell renal cell carcinoma represents the most common type of kidney cancer. Precision medicine approach to ccRCC requires an accurate stratification of patients that can predict prognosis and guide therapeutic decision. Transcription factors are implicated in the initiation and progression of human carcinogenesis. However, no comprehensive analysis of transcription factor activity has been proposed so far to realize patient stratification. Here we propose a novel approach to determine the subtypes of ccRCC patients based on global transcription factor activity landscape. Using the TCGA cohort dataset, we identified different subtypes that have distinct upregulated biomarkers and altered biological pathways. More important, this subtype information can be used to predict the overall survival of ccRCC patients. Our results suggest that transcription factor activity can be harnessed to perform patient stratification.


2017 ◽  
Vol 35 (6_suppl) ◽  
pp. 519-519
Author(s):  
Shreyas Joshi ◽  
Suraj Peri ◽  
Eric A. Ross ◽  
Robert G. Uzzo ◽  
Alexander Kutikov ◽  
...  

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.


Sign in / Sign up

Export Citation Format

Share Document