Diagnostic area

IDENTIFICATION OF PROGNOSIS BIOMARKERS IN HEPATOCELLULAR CARCINOMA

Hepatocellular carcinoma (HCC) accounts for 80-90% of primary malignant liver tumors, is the fifth highest incidence in malignant disease and the third leading cause of cancer deaths with an average of 450,000 new cases diagnosed annually. Although risk factors such as hepatitis B or C, alcohol abuse or steatosis are known, that the population at risk is monitored regularly and the availability of new therapeutic strategies, the survival of patients (about 6 months) has not been able to improve substantially over the past two decades. The main reason is the advanced stage of disease at the time of diagnosis, which highlights the urgent need to develop new methods of early detection in the population at risk (hepatitis and cirrhosis) and to develop innovative therapeutic methods. The partial or total resection followed by transplantation is the only curative therapy but is not always possible to apply and common recurrence of HCC. Consequently, there is an urgent need to identify biomarkers to develop diagnostic strategies to identify those patients with chronic liver disease will develop HCC, which will be its progression and its responsiveness to therapy and their risk of recurrence. This would help to improve patients' expectations.

A biomarker is a characteristic that can be measured and evaluated objectively as an indicator of biological or pathological process, or pharmacologic responses to therapeutic interventions. Complex and multifactorial diseases such as HCC require, probably, of the determination of several parameters to stratify patients effectively. The progress made in recent years in defining the molecular mechanisms underlying diseases, along with the capabilities of the new biotechnologies greatly facilitate the discovery of biomarkers. In recent years, HCC has been analyzed using different experimental approaches, including technology of DNA microarrays and proteomic analysis, but do not yet have a collection of validated markers for early detection.

The objective of this project is to identify biomarkers in studies based on genome-wide gene expression that allow to stratify patients at risk for development of HCC, to identify cases that presented neoplastic evolution and determine the degree of malignancy of the lesion. This could be used to implement a customized therapy for each patient. Given the complexity of large-scale analysis in human samples, largely due to heterogeneity derived from the genetic background and environmental factors, as well as the limited availability makes it difficult to longitudinal studies, the project was planned in the following stages:

1. - Identification of biomarkers in a murine HCC model (MAT1-/ -).

2. - Validation of differential genes by RT PCR in TLDA format (microfluidics).

3. - Comparison of the panel of differentially expressed genes and validated in the murine HCC with gene expression profiles characteristic of human HCC published by other authors. This analysis will select the genes most likely to be validated in studies with human samples.

4. - Differential validation of selected genes in samples from patients with cirrhosis and HCC.

5. - Identification of the minimum number of genes that allows differentiates specifically cirrhosis and HCC.

6. - Development of an algorithm for diagnosis/prognosis that allows a weighted combination of the positive biomarker levels in human samples.

The study of murine MAT1-/ - using the technology of DNA microarrays has established a fingerprint consisting of 351 differentially expressed probes (equivalent to 181 genes). After obtaining the list of candidates they have been validated using low density arrays (TLDAs) in mouse samples MAT1 - / - used as a model of HCC and checked the validation of approximately 70% of them. The panel of validated genes in mice has been compared with the characteristic gene expression profiles of human HCC and have selected 45 genes. We analyzed their changes of expression in cirrhosis and HCC by TLDAs in human specimens. Among the differential genes were searched, using a classifier, the minimum number that enabled the correct grouping of the classes under study. Six genes were selected as markers and we propose an algorithm that relates the basis for the development of a protocol of early diagnosis of HCC.