Liver cirrhosis and its complications are estimated to cause more than 1.4 million of death worldwide being the 5th mortality cause in European Union but, unfortunately, there is no treatment available for patients with advanced chronic liver disease. One characteristic of the clinical trials failure is the unreliable method of diagnosis for determining the degree of liver cirrhosis based on the sinusoid phenotype, which is the most relevant to the development of the disease. Therefore, the diagnostic and prognostic of patients with liver cirrhosis could significantly improve with the development of methods for early detection of disease complications, and with personalized therapies adapted to liver alterations in each individual patient.
Our invention is based in results obtained after reanalyze available single-cell RNAseq and transcriptomic data of liver tissue from healthy or cirrhotic patients using bioinformatic techniques. The sinusoidal subpopulations of endothelial cells (ECs), mesenchymal cells (MES) and hepatic macrophages (MPs) with significant changes in cirrhosis vs healthy were analyzed. The most significantly deregulated genes expressed specifically in each sinusoidal subpopulation (6 genes in ECs, 6 genes in MES and 5 genes in MPs) were validated in an intern cohort (n=12 healthy vs n=11 cirrhosis), and the specificity of these genes was analysed and validated in single-cell RNAseq data (n=10) and in 3 cohorts of patients with different liver disease stage (n=343). Moreover, the expression of these 17 genes was able to classify patients depending on the stage of liver diseases (healthy, early or advanced) (N=271), and to estimate the sinusoidal cells dysfunction (n=216).
The signature of these 17 genes would have the added value, compared to other methods of diagnosis (histology or serum markers), of providing specific information on the most altered cell types in each patient and thus helping to find the right therapy for each case. Therefore, the use of this gene signature could be a useful tool for personalized clinical decision making, possibly aiding in assessment of drug response or in choosing the most relevant cell target for therapy for an individual patient.
Finally, the use of our diagnostic product as a starting point for personalized medicine implies that the possible effects of the patient's sex, age or genetic background on the development of liver disease would be reflected in the initial diagnosis and could be addressed specifically.
