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

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.