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Keywords
- Candida albicans (1)
- adrenal cortex hormones (1)
- adrenal cortex neoplasms (1)
- mass spectrometry (1)
- metabolomics (1)
- urine (1)
EU-Project number / Contract (GA) number
- 259735 (1)
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.
The pathogenic yeast Candida albicans can develop resistance to the widely used antifungal agent fluconazole, which inhibits ergosterol biosynthesis, by the overexpression of genes encoding multidrug efflux pumps or ergosterol biosynthesis enzymes. Zinc cluster transcription factors play a central role in the transcriptional regulation of drug resistance. Mrr1 regulates the expression of the major facilitator MDR1, Tac1 controls the expression of the ABC transporters CDR1 and CDR2, and Upc2 regulates ergosterol biosynthesis (ERG) genes. Gain-of-function mutations in these transcription factors result in constitutive overexpression of their target genes and are responsible for fluconazole resistance in many clinical C. albicans isolates. The transcription factor Ndt80 contributes to the drug-induced upregulation of CDR1 and ERG genes and also binds to the MDR1 and CDR2 promoters, suggesting that it is an important component of all major transcriptional mechanisms of fluconazole resistance. However, we found that Ndt80 is not required for the induction of MDR1 and CDR2 expression by inducing chemicals. CDR2 was even partially derepressed in ndt80D mutants, indicating that Ndt80 is a repressor of CDR2 expression. Hyperactive forms of Mrr1, Tac1, and Upc2 promoted overexpression of MDR1, CDR1/CDR2, and ERG11, respectively, with the same efficiency in the presence and absence of Ndt80. Mrr1- and Tac1-mediated fluconazole resistance was even slightly enhanced in ndt80D mutants compared to wild-type cells. These results demonstrate that Ndt80 is dispensable for the constitutive overexpression of Mrr1, Tac1, and Upc2 target genes and the increased fluconazole resistance of strains that have acquired activating mutations in these transcription factors.