ECE2022 Poster Presentations Adrenal and Cardiovascular Endocrinology (87 abstracts)
Whole blood transcriptomic profile of Cushing’s syndrome
Roberta Armignacco 1 , De Murat Daniel 1 , Anne Jouinot 1 , Lucas Bouys 1 , Karine Perlemoine 1 , Franck Letourneur 1 , Lucie Adoux 1 , Maria-Christina Zennaro 2,3 , Jerome Bertherat 1,4 & Guillaume Assié 1,4
1Université de Paris, Institut Cochin, INSERM U1016, CNRS UMR8104, Paris, France; 2Université de Paris, PARCC, INSERM, Paris, France; 3Assistance Publique-Hôpitaux de Paris, Hôpital Européen Georges Pompidou, Service de Génétique, Paris, France; 4Assistance Publique-Hôpitaux de Paris, Hôpital Cochin, Service d’Endocrinologie, Center for Rare Adrenal Diseases, Paris, France
Background: Cushing’s syndrome, caused by an excess of circulating glucocorticoids, is associated with high morbidity and presents high inter-individual variability. The earlier the diagnosis, the better the treatment effectiveness and the prognosis. Hormone assays, routinely used, contribute to identify Cushing’s syndrome. However, no biomarker is currently available to directly quantify the biological action of glucocorticoids. Blood samples represent an easily obtainable source for profiling individuals on a molecular level. In this study, we analysed the transcriptomic profile in 59 blood samples from patients with different glucocorticoid states (overt or mild Cushing’s syndrome, eucortisolism, adrenal insufficiency).
Materials and methods: Total RNA was extracted from whole blood samples collected into PAXgene tubes. Transcriptome was determined by RNA sequencing performed on NovaSeq 6000 platform (Illumina). Blood cell proportions in each sample were estimated from expression profiles by using the CIBERSORT method. Unsupervised samples classification (PCA) was used to explore the transcriptomic profiles. A preliminary differential expression analysis was performed by using a linear model-based method (Limma).
Results: Unsupervised classification showed a discrimination of overt Cushing’s syndrome samples (accuracy: 0.81), presenting a specific profile compared to the other glucocorticoid states. This variability also associated with blood cell proportions, particularly with a higher neutrophils percentage. The most differentially expressed genes in the group of overt Cushing’s syndrome (n=3173 genes, with adjusted p-value <0.001) were enriched in pathways related to immunity, particularly to neutrophils activation and activity.
Conclusions: These preliminary results show that glucocorticoid excess associates with a specific whole blood transcriptome profile. Further analyses will allow to identify a set of genes representing a specific molecular signature of glucocorticoid excess, which will take into account biological factors potentially involved, such as blood cell composition.
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Endocrine Abstracts
ISSN 1470-3947 (print) | ISSN 1479-6848 (online)
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