Mots-Clés

Long-read sequencing, circular RNA, melanoma, biomarkers

Description

The emergence of long-read sequencing technology has made it possible to sequence longer fragments of RNA and therefore access the complete sequences of the different isoforms of RNA. In this project, we will use Nanopore long-read sequencing technology to study circular RNAs (circRNAs), a type of non-coding RNA that is not yet well understood. CircRNAs are known to play a role in the regulation of messenger RNAs (mRNAs) by acting as microRNA sponges, preventing the degradation of microRNA target mRNAs. Our lab has developed a bioinformatics tool called cirscan (https://gitlab.com/geobioinfo/cirscan_Rshiny) to identify circRNA sponge candidates and infer RNA regulatory networks in cancer, in particular melanoma. In this project we will take advantage of Nanopore long-read sequencing technology to extend our tool and gain a comprehensive understanding of the circRNA landscape in melanoma and its potential role in treatment resistance.
The thesis project will be divided into three parts: 1) The identification and cataloguing of the different isoforms of circRNA in melanoma cell lines, sensitive or resistant to a standard targeted therapy (long-read sequencing data currently generated). 2) The identification and characterization of chemical modifications (such as methylation or acetylation) in circRNA molecules in melanoma. The goal is to understand the role of these modifications in regulating circRNA function and how they may be involved in melanoma treatment resistance. 3) In the final part of the project, the focus will shift to the identification and characterization of circulating circRNA in an in vivo model of melanoma (xenograft model). The aim is to identify circRNA molecules that could serve as potential biomarkers for predicting treatment response in melanoma.
Overall, the proposed PhD project has the potential to significantly advance our understanding of the role of circRNA in melanoma and could lead to the development of novel treatments based on RNA targeting and predictive circulating biomarkers for treatment response.

Expected profile
Strong interest in bioinformatics and oncogenomics