Liver cancer mortality rate and incidence are increasing worldwide. It is the second most common cancerrelated cause of death in men and the third by total deaths worldwide. Liver cancer has a very low fiveyear survival, of 18% worldwide, and its recurrence rate is as high as 70%. It is a deadly disease mainly

because there are limited diagnostic, prognostic, and therapeutic approaches. The most common liver

cancer is hepatocellular carcinoma (HCC). The liver cancer patients and their doctors face two problems

in everyday clinical practice, lack of non-invasive molecular biomarkers, diagnostic or prognostic, and

limited therapeutic approaches. Circular RNA (circRNA) represent a new class of covalently closed, singlestranded RNA, which have several potential applications in oncology. They are a promising new class of

diagnostic and prognostic biomarkers, and could also represent a new class of RNA-based therapies.

In this project we the aim to evaluate biomarker and therapeutic potential of circRNA in liver cancer using

an original systems medicine approach. This has not yet been attempted and we expect that the use of

systems medicine tools will enable us to identify key circRNA driving the malignant phenotype in HCC,

making them ideal targets of new therapies and potential molecular biomarkers in HCC. We believe that

a breakthrough in the diagnosis and treatment of patients with liver cancer requires a different and

original approach to studies of liver cancer pathology. The project brings together the knowledge of

experts from various fields, clinics, computer scientists, molecular biologists and international

collaborators, which will ensure the success of the project.

We will exploit the state-of-the-art long-read sequencing to sequence full length circRNA in tumour and

non-tumour liver samples in parallel with plasma from the same HCC patients. By this approach we will

be the first to generate a unique long-read sequencing dataset from trio of samples from the same patient,

which will enable us to directly align the expression of circRNA in plasma and liver samples to polyA RNA

expression in the liver, which we already have sequenced. Furthermore, by applying the original systems

medicine approach to data integration of transcriptome datasets and gene prioritization, we aim to

identify key circRNAs in HCC and test their biomarker and cancerogenic potential. Finally, we will test the

circRNA potential as the target of RNA-based therapeutics in preclinical models.

Project expected results are: (1) generation of a unique HCC dataset from trio of samples, which will

enable data analyses that were not possible so far. (2) Long-read sequences of circRNA, which will enable

precise definition of their length and sequence and the discovery of new circRNAs. (3) Identification of

key circRNAs using original system medicine tools on our HCC datasets. (4) Validation of key circRNAs

biomarker and cancerogenic potential in HCC. (5) Advancement in the field of RNA-based therapeutics by

evaluating the circRNA potential in preclinical models.

The project will contribute to our understanding of how circRNAs are involved in malignant liver cancer

pathology. It is a unique opportunity to perform a comprehensive study to tackle the two problems in

liver cancer: the lack of non-invasive molecular biomarkers and the lack of new therapeutic strategies