Predictive modeling of gene expression regulation

• Main Authors: Damia, G. (Author), Fratelli, M. (Author), Ganzinelli, M. (Author), Guffanti, F. (Author), Masseroli, M. (Author), Matteucci, M. (Author), Regondi, C. (Author)
• Format: Article
• Language: English
• Published: BioMed Central Ltd 2021
• Subjects: algorithm; Algorithms; Alkylation; Analysis approach; Biological relationships; Cancer; Diseases; gene expression profiling; Gene Expression Profiling; gene expression regulation; Gene expression regulation; Gene Expression Regulation; Gene Expression Regulation, Neoplastic; gene regulatory network; Gene Regulatory Networks; In-depth analysis; Linear modeling; Machine learning; metabolism; Predictive modeling; Predictive models; Promoter methylation; Regulation networks; Regulatory elements; Regulatory network; reproducibility; Reproducibility of Results; Stem cells; Target genes; Transcription; transcription factor; Transcription Factors; Tumors
• Online Access: View Fulltext in Publisher

 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. © 2021, The Author(s).