Innovative Therapeutic Target Identification For Hepatocellular Carcinoma: Pathways Simulation And Biomarker Identification

Hepatocellular carcinoma(HCC), the second mortality rate among malignant tumors of male in the world, is one of the most life-threatening cancers with complex pathogenesis and low cure rate. Up to now, Sorafenib is the only approved drug by FDA for the treatment of advanced HCC concomitantly with rash, diarrhea, high blood pressure and other serious side effects. Meanwhile, other clinical targeting preparations also face serious problems such as weak efficacy, side effects and so on. An effective way of overcoming these issues is to select some appropriate drug targets in the early drug discovery. But so far, targets for HCC were extremely rare only including VEGFR, PDGFR, Raf and so on. Thus, it is urgent to identify novel therapeutic targets to promote the discovery of anti-HCC drug.The deregulation of intracellular signal transduction is an important reason for tumor. Current evidence indicates that gene differential expression and mutation in cancer cells which may influence the signal transduction of many cancer-related signaling pathways is closely correlated with HCC. At present, studies on HCC signaling pathways have become the forefront hotspot. Nevertheless, there has not yet been a relatively complete system of HCC integrating ultiple signaling pathways. On the other hand, with the genomics technologies developments, the selection of biomarkers has been used for the target identification, which will makes great significance in diagnose, drug discovery and therapy of diseases especially for the hepatocellular carcinoma with no obvious clinical features in the early level.This research aimed to find novel therapeutic targets for HCC through constructing a simulation model for HCC-related signaling pathways and the biomarkers selection. First, based on the existing experimental protein-protein interactions database, this work established the most complete model of HCC signaling pathways by using a systems biology approach. This model can be used to identify potential therapeutic targets and analyse the dynamic characteristics of HCC signaling pathway on system-level.This simulation model supported by data from several literatures and other experimental data was furtherly verified its reliability. Through this model, we found some potential targets for HCC including VEGFR, PDGFR, EGFR, MEK, PI3 K, PP2 A. Second, using the biomarker seletction method, we analyzed hepatocellular carcinoma microarray data, and found out some differential expressing genes including CYP2B6, PON3, AOX1, ACSM, which were closely related to metabolism.The above targets found based on signaling networks and genomics technology had a high degree consistency with the reported anti-HCC drug targets.The differential results between signaling networks and biomarker identification methods would reflect the different interest aspects by using different methods for drug targets identification which must be considered in new drug discovery. In summary, up to now,a most complete simulation model of HCC signaling pathways was constructed in present study, and several potential drug target candidates were found. Meanwhile, we conclude that the comprehensive consideration of a variety of methods and techniques for anti-HCC drug targets discovery is needed, which can reflect the complex network characteristics from different aspects of cancer pathway.