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Published on: March 1, 2024
Modelling TERT regulation across 19 different cancer types based on the MIPRIP 2.0 gene regulatory network approach
Alexandra M Poos1,2,3, Theresa Kordaß3,4, Amol Kolte1
1Integrated Research and Treatment Center, Center for Sepsis Control and Care (CSCC), Jena University Hospital, Am Klinikum 1, 07747, Jena, Germany.
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.
Insights
Researchers identified common and cancer-specific regulators of the telomerase reverse transcriptase (TERT) gene using an advanced computational approach. This tool aids in understanding cancer proliferation and predicting gene regulation across various diseases.
Area of Science:
- Oncology
- Bioinformatics
- Systems Biology
Background:
- Reactivation of the telomerase reverse transcriptase (TERT) gene is crucial for cancer cell proliferation.
- The regulatory mechanisms governing TERT reactivation are not fully understood.
Purpose of the Study:
- To identify common and cancer-type specific regulators of the TERT gene.
- To develop and validate a computational tool for predicting gene regulation.
Main Methods:
- Assembled regulator binding information to construct human and mouse gene regulatory networks.
- Advanced the Mixed Integer linear Programming based Regulatory Interaction Predictor (MIPRIP) approach.
- Utilized MIPRIP2 R-package to analyze TERT regulation across 19 human cancers.
Main Results:
- Identified common and cancer-type specific regulators of TERT.
- Validated findings using ETS1 transcription factor regulation in melanoma.
- Developed an improved MIPRIP2 R-package and generic regulatory networks.
Conclusions:
- MIPRIP 2.0 successfully identified key TERT regulators.
- The software is applicable to transcriptome data for predicting gene regulation in any disease context.
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