Abstract Background Hepatitis C Virus is becoming a major health problem in Asia and across the globe since it is causing serious liver diseases including liver cirrhosis, chronic hepatitis and hepatocarcinoma (HCC). Protein interaction networks presents us innumerable novel insights into functional constitution of proteome and helps us finding potential candidates for targeting the drugs. Methods Here we present a comprehensive protein interaction network of Hepatitis C Virus with its host, constructed by literature curated interactions. The network was constructed and explored using Cytoscape and the results were further analyzed using KEGG pathway, Gene Ontology enrichment analysis and MCODE. Results We found 1325 interactions between 12 HCV proteins and 940 human genes, among which 21 were intraviral and 1304 were HCV-Human. By analyzing the network, we found potential human gene list with their number of interactions with HCV proteins. ANXA2 and NR4A1 were interacting with 6 HCV proteins while we found 11 human genes which were interacting with 5 HCV proteins. Furthermore, the enrichment analysis and Gene Ontology of the top genes to find the pathways and the biological processes enriched with those genes. Among the viral proteins, NS3 was interacting with most number of interactors followed by NS5A and so on. KEGG pathway analysis of three set of most HCV- associated human genes was performed to find out which gene products are involved in certain disease pathways. Top 5, 10 and 20 human genes with most interactions were analyzed which revealed some striking results among which the top 10 host genes came up to be significant because they were more related to Influenza A viral infection previously. This insight provides us with a clue that the set of genes are highly enriched in HCV but are not well studied in its infection pathway. Conclusions We found out a group of proteins which were rich in HCV viral pathway but there were no drugs targeting them according to the drug repurposing hub. It can be concluded that the cluster we obtained from MCODE contains potential targets for HCV treatment and could be implemented for molecular docking and drug designing further by the scientists. Keywords: Hepatitis C virus, Protein-protein interactions, Protein interaction network, Cytoscape, KEGG, Gene ontology Background Hepatitis C Virus or HCV is a small, single-stranded, positive-sense RNA virus belonging to the genus Hepacivirus of Flaviviridae family, discovered in 1989 [[27]1]. It is responsible for severe and chronic liver diseases [[28]2–[29]4] and affects 200 million people globally with 17 million cases in Pakistan alone [[30]5, [31]6]. The HCV pathogen causes acute and persistent diseases such as liver cirrhosis [[32]6], chronic hepatitis [[33]2], and hepatocarcinoma [[34]7]. HCV genome is 9.6 kb in size [[35]8] and is translated into four structural and six non-structural (NS) proteins. The structural HCV proteins are Core, E1, E2 and p7 protein which splits structural proteins from non-structural proteins. The non-structural HCV proteins are NS2, NS3, NS4A, NS4B, NS5A and NS5B [[36]2, [37]9, [38]10]. Another HCV protein named Frameshift protein or F protein was also discovered in the late 90’s [[39]9]. Protein-protein interactions can be mapped into networks and we can gain valuable insights into functional constitution of proteome by analyzing different parameters of the network such as its size and complexity. PPI networks can help us investigate differences between the normal and diseased states. We can obtain fundamental knowledge about a disease by finding novel pathways through analysis of the PPI network, by investigating subnetworks and its potential hubs involved in causing that specific disease [[40]11, [41]12]. Protein interactions are determined by different high throughput experimental and computational methods which yields different types of protein-protein interaction data. The high throughput experimental techniques identify the interactions either directly or infer them indirectly by different approaches [[42]11, [43]13] including Yeast 2 Hybrid (Y2H) and Tandem affinity purification combined with mass spectrometry (TAPMS). Experimental methods for PPI detection have many limitations such as high false positive rate, high cost, time-consuming, and laborious. New computational methods are being practiced successfully to evaluate and analyze the interaction data generated by high-throughput experimental approaches, and to predict novel protein interactions by gaining insights from the already known interactions. Computational methods provide a quick and low-cost alternative to the traditional experimental techniques to predict PPIs. An important advantage of computational methods over experimental is that we can study proteins by mapping the pairwise associations into a comprehensive network according to their distinct functional level [[44]11, [45]14]. The interactome map of HCV with human cellular proteins has been previously constructed by using the traditional yeast 2 hybrid screens, and also using the computational methods as well. Among the studies done on HCV protein-protein interactions, some are small scale studies experimentally-verifying a small number of PPIs, while others are large scale studies involving large number of PPIs either verified experimentally, or predicted computationally [[46]7, [47]15, [48]16]. Although the Hepatitis C virus interactome has been constructed by many scientists, there is still a comprehensive network missing needed for understanding the biological details of HCV and the infection pathway of its proteins. There is a need of a single comprehensive network that encompasses the multiple researches done on Hepatitis C virus interactome. The goal of this study is to get an explicit idea and understanding of hepatitis c viral protein-protein interactions by integrating both large-scale and small-scale studies either conducted experimentally or computationally on HCV interactome. In this way, we will get vast information regarding Hepatitis C virus interactions from both experimentally-determined and computationally-predicted associations. The aim of creating such a comprehensive network is to combine all the previous work done on HCV-Human interactions; to bring forward a new approach to study pathogens and their connections with host organism and to find potential molecules that are involved in causing the disease which can then be knocked down by designing potential drugs. Every disease in a human body is triggered not by a single element but by the mutual interaction of different molecules. The objective of this study was to find the molecules which were highly involved in stimulating HCV and we came up with certain human genes which were highly interacting with HCV partners but were also actively contributing in other disease and hormonal pathways. Methods The first step of the study was to collect data of all the work done on HCV interactome till date and for this purpose, the PubMed advanced search option was used using multiple keywords related to HCV protein-protein interactions. The search yielded 729 studies containing a variety of literature related the diseases caused by HCV, its epidemiology, gene expression data, and other aspects. The focus of our research was to get protein-protein interactions of HCV with its host Homo sapiens and its own proteins. So, we refined the search research results and narrowed down it to 13 studies which exclusively contained PPI data of HCV-Human and HCV-HCV. All the data collected from these research studies was merged and after removing duplicates, a total of 1325 interactions were found among which 21 were HCV-HCV interactions, and 1304 HCV-Human interactions. Table [49]1 lists the selected studies with their respective number of interactions accessed. Table 1. List of 13 papers with accessible interaction data S/No. Paper No. of Interactions References