Thesis supervisor: Ágnes Tantos
co-supervisor: Éva Schád
Location of studies (in Hungarian): HUN-REN Research Centre for Natural Sciences, Institute of Molecular Life Sciences, Non-Coding Genome Research Group Abbreviation of location of studies: H-REN
Description of the research topic:
"The central goal of the work is to improve the predictive power of RNA biomarkers through a network-based approach, which helps identifying key regulatory players behind malignant processes. The scientific basis is to build Competing Endogenous RNA (ceRNA) networks using transcriptomic data from the available databases combined with our own results in cellular systems and use these ceRNA networks for the identification of the best combinations of biomarkers for specific diagnostic purposes.
ceRNA Networks consist of different RNAs that compete for a shared regulatory RNA, typically a microRNA (miRNA). These can be mRNAs with the same regulatory miRNA, or long non-coding RNAs (lncRNAs) that can interact with several miRNAs, sequestering them and thus modifying the expression of their target mRNAs. Our primary targets are lncRNAs as biomarkers, since these represent a yet untapped source for additional information on disease progression, despite the fact that their diagnostic and prognostic power have been repeatedly shown. The problem with the application of these lncRNAs as biomarkers is their limited specificity (showing altered expression in several tumours) and that they often have ambiguous effects (having tumour suppressor roles in one type of cancer and oncogenic roles in others).
Our ceRNA network-based approach offers a solution to these problems, as we will be able to create unique combinations of lncRNA biomarkers specific to the selected tumours with their effects predicted from the ceRNA network.
The tasks of the PhD student will include bioinformatics analysis of transcriptomic data, building and analysing ceRNA networks and potentially participate in the experimental validation of the results.
"
Required language skills: English Further requirements: Basic programming skills (Perl, Python, R).
Number of students who can be accepted: 1
Deadline for application: 2024-05-31
2024. IV. 17. ODT ülés Az ODT következő ülésére 2024. június 14-én, pénteken 10.00 órakor kerül sor a Semmelweis Egyetem Szenátusi termében (Bp. Üllői út 26. I. emelet).
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