The emergence of clinical databases has greatly influenced bioinformatic approaches surrounding identification of new drug targets, yet use of clinical databases alone neglects the functional relationships among various signaling pathways that contribute to a specific disease's tumorigenicity. A new method was developed that uses the GeneGo network analysis environment with The Cancer Genome Atlas (TCGA) to identify relationships between essential genes and upstream non‐essential regulatory proteins. The strategy uses an essential gene (protein product as a hub), and constructs a interaction network using the GeneGo. Upstream nonessential regulatory proteins were queried in TCGA to integrate disease‐specific data into the network. One example of how this approach identified novel molecular therapies is by using proliferating cell nuclear antigen (PCNA) as a hub protein in glioblastoma. This approach identified several regulatory proteins showing 2‐fold change in gene expression in glioblastoma tumors compared to normal tissue in greater than 90 percent of study subjects. Identifying hub interactants and upstream regulators that contribute to overall tumorigenicity creates a disease focus for discovering interactions worth modulating PCNA function and predicted small molecule synthetic lethal relationships.