Abstract
BACKGROUND: Reactivation of the telomerase reverse transcriptase gene TERT is a central feature for unlimited proliferation of the majority of cancers. However, the underlying regulatory processes are only partly understood. RESULTS: We assembled regulator binding information from serveral sources to construct a generic human and mouse gene regulatory network. Advancing our "Mixed Integer linear Programming based Regulatory Interaction Predictor" (MIPRIP) approach, we identified the most common and cancer-type specific regulators of TERT across 19 different human cancers. The results were validated by using the well-known TERT regulation by the ETS1 transcription factor in a subset of melanomas with mutations in the TERT promoter. Our improved MIPRIP2 R-package and the associated generic regulatory networks are freely available at https://github.com/KoenigLabNM/MIPRIP. CONCLUSION: MIPRIP 2.0 identified common as well as tumor type specific regulators of TERT. The software can be easily applied to transcriptome datasets to predict gene regulation for any gene and disease/condition under investigation.