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Updated: Oct 11, 2025
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
Published on: March 1, 2024
Predictive modeling of gene expression regulation
Chiara Regondi1, Maddalena Fratelli2, Giovanna Damia3
1Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133, Milan, Italy. chiara.regondi@mail.polimi.it.
Background:
In-depth analysis of regulation networks of genes aberrantly expressed in cancer is essential for better understanding tumors and identifying key genes that could be therapeutically targeted.
Results:
We developed a quantitative analysis approach to investigate the main biological relationships among different regulatory elements and target genes; we applied it to Ovarian Serous Cystadenocarcinoma and 177 target genes belonging to three main pathways (DNA REPAIR, STEM CELLS and GLUCOSE METABOLISM) relevant for this tumor. Combining data from ENCODE and TCGA datasets, we built a predictive linear model for the regulation of each target gene, assessing the relationships between its expression, promoter methylation, expression of genes in the same or in the other pathways and of putative transcription factors. We proved the reliability and significance of our approach in a similar tumor type (basal-like Breast cancer) and using a different existing algorithm (ARACNe), and we obtained experimental confirmations on potentially interesting results.
Conclusions:
The analysis of the proposed models allowed disclosing the relations between a gene and its related biological processes, the interconnections between the different gene sets, and the evaluation of the relevant regulatory elements at single gene level. This led to the identification of already known regulators and/or gene correlations and to unveil a set of still unknown and potentially interesting biological relationships for their pharmacological and clinical use.
Insights
This study introduces a quantitative method to analyze gene regulatory networks in ovarian cancer, identifying key relationships for potential therapeutic targets. The approach reveals known and novel biological connections for clinical applications.
Area of Science:
- Genomics
- Systems Biology
- Cancer Research
Background:
- Understanding cancer requires in-depth analysis of gene regulatory networks.
- Identifying key genes in aberrant gene expression is crucial for targeted cancer therapies.
Purpose of the Study:
- To develop and apply a quantitative approach for investigating biological relationships among regulatory elements and target genes.
- To analyze gene regulation in Ovarian Serous Cystadenocarcinoma focusing on DNA REPAIR, STEM CELLS, and GLUCOSE METABOLISM pathways.
Main Methods:
- Developed a predictive linear model using ENCODE and TCGA data.
- Assessed relationships between gene expression, promoter methylation, pathway gene expression, and transcription factors.
- Validated the approach in basal-like Breast cancer and with the ARACNe algorithm.
Main Results:
- Built predictive models for 177 target genes in Ovarian Serous Cystadenocarcinoma.
- Identified known and novel gene correlations and regulatory elements.
- Confirmed the reliability and significance of the quantitative analysis approach.
Conclusions:
- The models revealed gene-process relations and inter-pathway connections.
- Disclosed relevant regulatory elements at the single gene level.
- Unveiled potentially significant unknown biological relationships for pharmacological and clinical use.
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