6 research outputs found
8Cl-cAMP modifies the balance between PKAR1 and PKAR2 and modulates the cell cycle, growth and apoptosis in human adrenocortical H295R cells
Link between steroidogenesis, the cell cycle, and PKA in adrenocortical tumor cells
International audienceAdrenocortical tumors (ACTs) frequently cause steroid excess and present cell-cycle dysregulation. cAMP/PKA signaling is involved in steroid synthesis and play a role in cell-cycle regulation. We investigated, by cell synchronization in the different phases of the cell-cycle, the control of steroidogenesis and the contribution of PKA in adrenocortical cells (H295R and culture of primary pigmented nodular adrenocortical disease cells). Cells showed increased steroidogenesis and a maximal PKA activity at G2 phase, and a reduction at G1 phase. PRKACA overexpression, or cAMP stimulation, enhanced PKA activity and induced steroidogenesis in all synchronized groups but is not sufficient to drive cell-cycle progression. PRKAR1A inactivation enhanced PKA activity and induced STAR gene expression, only in cells in G1, and triggered cell-cycle progression in all groups.These findings provide evidence for a tight association between steroidogenesis and cell-cycle in ACTs. Moreover, PRKAR1A is essential for mediating the function of PKA activity on both steroidogenesis and cell-cycle progression in adrenocortical cells
The Gene of the Ubiquitin-Specific Protease 8 Is Frequently Mutated in Adenomas Causing Cushing's Disease
Clinical Outcome, Hormonal Status, Gonadotrope Axis, and Testicular Function in 219 Adult Men Born With Classic 21-Hydroxylase Deficiency. A French National Survey
Urine Steroid Metabolomics as a Biomarker Tool for Detecting Malignancy in Adrenal Tumors
Context: Adrenal tumors have a prevalence of around 2% in the general population. Adrenocortical carcinoma (ACC) is rare but accounts for 2–11% of incidentally discovered adrenal masses. Differentiating ACC from adrenocortical adenoma (ACA) represents a diagnostic challenge in patients with adrenal incidentalomas, with tumor size, imaging, and even histology all providing unsatisfactory predictive values.
Objective: Here we developed a novel steroid metabolomic approach, mass spectrometry-based steroid profiling followed by machine learning analysis, and examined its diagnostic value for the detection of adrenal malignancy.
Design: Quantification of 32 distinct adrenal derived steroids was carried out by gas chromatography/mass spectrometry in 24-h urine samples from 102 ACA patients (age range 19–84 yr) and 45 ACC patients (20–80 yr). Underlying diagnosis was ascertained by histology and metastasis in ACC and by clinical follow-up [median duration 52 (range 26–201) months] without evidence of metastasis in ACA. Steroid excretion data were subjected to generalized matrix learning vector quantization (GMLVQ) to identify the most discriminative steroids.
Results: Steroid profiling revealed a pattern of predominantly immature, early-stage steroidogenesis in ACC. GMLVQ analysis identified a subset of nine steroids that performed best in differentiating ACA from ACC. Receiver-operating characteristics analysis of GMLVQ results demonstrated sensitivity = specificity = 90% (area under the curve = 0.97) employing all 32 steroids and sensitivity = specificity = 88% (area under the curve = 0.96) when using only the nine most differentiating markers.
Conclusions: Urine steroid metabolomics is a novel, highly sensitive, and specific biomarker tool for discriminating benign from malignant adrenal tumors, with obvious promise for the diagnostic work-up of patients with adrenal incidentalomas.