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Project information
Molecular classification of renal cell carcinoma based on long noncoding RNAs expression and its implication for diagnosis, prognosis and therapy

Information

This project doesn't include Institute of Computer Science. It includes Central European Institute of Technology. Official project website can be found on muni.cz.
Project Identification
NV18-03-00554
Project Period
5/2018 - 12/2022
Investor / Pogramme / Project type
Ministry of Health of the CR
  • Ministry of Health Research Programme 2015 - 2022
MU Faculty or unit
Central European Institute of Technology
Cooperating Organization
Masaryk Memorial Cancer Institute Brno
  • Responsible person doc. MUDr. Alexandr Poprach, Ph.D.

Renal cell carcinoma (RCC) is the most common cancer of adult kidney. The incidence of RCC varies geographically; interestingly the highest incidence rates are found in the Czech Republic. Despite advances in diagnosis, especially improved imaging techniques, about 20–30% of all patients are diagnosed with metastatic disease. In addition, another 20% of patients undergoing nephrectomy will have a relapse and develop metastatic disease during follow-up. Except for prognostic normograms based on clinic-pathological features of RCC with limited predictive power, there are no sensitive molecular biomarkers enabling prediction of early relapse after nephrectomy. LncRNAs play key roles in cell physiology from nuclear organization and epigenetic remodeling to post-transcriptional regulation. Characteristic changes in the expression of lncRNAs were observed in tumor tissue of wide range of cancers, including RCC. These global expression profiles can be not only organ-specific but can be also correlated with clinical and pathological features of RCC such as grading, staging, etc. We hypothesize, that also specific lncRNA pattern exists in RCC as well, and could be associated with prognosis and enable prediction of relapse-free and overall survival in RCC patients. Recently, gene expression based-taxonomy has been developed in RCC to identify distinct subtypes of the disease and predict patient’s outcome. We aim to develop similar molecular taxonomy on the basis of lncRNA expression profiles leading to identification of RCC molecular subtypes associated with specific clinical phenotypes. Understanding of lncRNAs functions in RCC will lead to better understanding of molecular basis of this malignancy and uncover novel targeted therapeutic approach in RCC.

Publications

Total number of publications: 8

2023

2022

2021

2020

2019

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