IACS9 9th International Adrenal Cancer Symposium 2024 Abstracts (18 abstracts)
Cross-study reconciliation of SF-1 regulatory targets in adrenocortical carcinoma through in silico analysis
Muzzi JCD 1,2 , Colodel ME 1 , Magno JM 1,2 , Resende JSS 1,2 , Ruggiero C 3,4 , Doghman M 3,4 , de Moura JF 5 , Alvarenga LM 5 , Cavalli LR 1,6 , Figueiredo BC 1,7 , Lalli E 3,4,8 & Castro MAA 2
1Oncology Division, Pelé Pequeno Príncipe Research Institute, Curitiba 80250-060, Brazil 2Bioinformatics and Systems Biology Laboratory, Federal University of Paraná (UFPR), Curitiba 81520-260, Brazil 3Institut de Pharmacologie Moleculaire et Cellulaire CNRS UMR 7275, 06560 Valbonne, France 4Universite Cote d’Azur, 06560 Valbonne, France 5Laboratório de Imunoquímica (LIMQ), Department of Basic Pathology, Federal University of Paraná (UFPR), Curitiba 81530-990, Brazil 6Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC 20057, USA 7Molecular Oncology Laboratory, Centro de Genética Molecular e Pesquisa do Câncer em Crianças (CEGEMPAC), Curitiba 80030-110, Brazil 8Inserm, 06560 Valbonne, France
Background: Steroidogenic Factor-1 (SF-1; NR5A1) is a nuclear receptor transcription factor crucial for the development of adrenal glands and gonads, as well as for steroid hormone production. Its overexpression in adrenocortical carcinoma (ACC) is associated with increased proliferation, poor prognosis, modulation of steroid production, and immune suppression.
Objectives: Three independent studies aimed to identify SF-1 regulatory targets in ACC using H295R cells, but comparisons of differentially expressed genes (DEGs) revealed poor overlap, with less than 10% of target genes shared. This study explores the reasons for this divergence and proposes a method to reconcile the findings using an in silico approach.
Methods: We reassessed in vitro raw data from the studies by Ferraz et al. (2011), Doghman et al. (2013), and Ehrlund et al. (2012), applying standardized analytical methods, including normalization, data preprocessing, and statistical tests. An in silico SF-1 regulon was used as an external reference for cross-study comparison. Additionally, we implemented a systematic approach to optimize the threshold for identifying shared differentially expressed genes (DEGs) among the studies.
Results: Our analysis revealed a consistent directional pattern across all phenotypes despite the low initial overlap in targets. We identified similar qualitative transcriptional signatures across all three studies, which led us to conduct a quantitative analysis using a systematic approach to threshold selection. This approach ultimately identified a common set of NR5A1 targets shared among the studies.
Discussion/Conclusion: The findings suggest that the studies complement each other, providing a more comprehensive understanding of SF-1’s regulatory role in ACC. Reassessing and standardizing the comparison between these three studies enhances the identification of SF-1 regulatory targets in ACC. In conclusion, the in silico methodology used here can be considered and assessed for feasibility, where the standard methods pose limitations to finding convergence in DEGs results, despite using the same cell line and similar technical approaches.
Keywords: SF-1, Adrenocortical Carcinoma, Gene Regulation, Bioinformatics, Threshold Optimization
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Endocrine Abstracts
ISSN 1470-3947 (print) | ISSN 1479-6848 (online)
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