Introduction: Hepatocellular carcinoma (HCC) is the most prevalent malignancy of the liver with limited clinical efficacy of currently used drugs such as sorafenib. Hence in this study we assessed the network proteins of HCC targets to identify the target/s which can achieve optimal clinical efficacy. Materials and Methods: The reported HCC targets and their network proteins were identified in the string database. The interactions of the network proteins based on the number of hydrogen bonds formed were evaluated using the chimera software and used to merit the network protein interactions. The merit of network protein interactions in clinical efficacy was assessed based on the expression pattern of the network proteins and corelating their targeting by sorafenib. Results: 22 potential HCC targets were identified along with their 152 unique network proteins. The following HCC targets; PDGFRB, IFNA2, VEGFR2, PD1, C-MET, RAR and IGF1R were observed to be among the top networks with the most number of hydrogen bond interactions between them. Among these, C-MET, RAR and IGF1R were significantly expressed in hepatocytes, making them relevant HCC targets. PD-1 and PD-L1, which are immune checkpoint regulators and hence used as part of immune therapy, were observed to form higher numbers of hydrogen bonds with HCC network proteins. Conclusion: Our analysis suggest that selectively targeting IGF1R, C-MET and RAR in hepatocytes together with immunotherapy will result in optimal clinical efficacy in the management of HCC.

Introduction: Hepatocellular Carcinoma (HCC) is the most prevalent malignancy of the liver with limited clinical efficacy of currently used drugs such as sorafenib. Hence in this study we assessed the network proteins of HCC targets to identify the target/s which can achieve optimal clinical efficacy. Materials and Methods: The reported HCC targets and their network proteins were identified in the string database. The interactions of the network proteins based on the number of hydrogen bonds formed were evaluated using the chimera software and used to merit the network protein interactions. The merit of network protein interactions in clinical efficacy was assessed based on the expression pattern of the network proteins and corelating their targeting by sorafenib. Results: 22 potential HCC targets were identified along with their 152 unique network proteins. The following HCC targets; PDGFRB, IFNA2, VEGFR2, PD1, C-MET, RAR and IGF1R were observed to be among the top networks with the most number of hydrogen bond interactions between them. Among these, C-MET, RAR and IGF1R were significantly expressed in hepatocytes, making them relevant HCC targets. PD-1 and PD-L1, which are immune checkpoint regulators and hence used as part of immune therapy, were observed to form higher numbers of hydrogen bonds with HCC network proteins. Conclusion: Our analysis suggest that selectively targeting IGF1R, C-MET and RAR in hepatocytes together with immunotherapy will result in optimal clinical efficacy in the management of HCC.