Background: Conventional diagnostic approaches for adrenal tumors require multi-step processes, including imaging studies and dynamic hormone tests. Therefore, this study aimed to discriminate adrenal tumors from a single blood sample based on the combination of liquid chromatography-mass spectrometry (LC-MS) and machine learning algorithms in serum profiling of adrenal steroids. Methods: The LC-MS-based steroid profiling was applied to serum samples obtained from patients with nonfunctioning adenoma (NFA, n=73), Cushing’s syndrome (CS, n=30), and primary aldosteronism (PA, n=40) in a prospective multicenter study of adrenal disease. The decision tree (DT), random forest (RF), and extreme gradient boost (XGBoost) were performed to categorize the subtypes of adrenal tumors. Results: The CS group showed higher serum levels of 11-deoxycortisol than the NFA group, and increased levels of tetrahydrocortisone (THE), 20α-dihydrocortisol, and 6β-hydroxycortisol were found in the PA group. However, the CS group showed lower levels of dehydroepiandrosterone (DHEA) and its sulfate derivative (DHEA-S) than both the NFA and PA groups. Patients with PA expressed higher serum 18-hydroxycortisol and DHEA but lower THE than NFA patients. The balanced accuracies of DT, RF, and XGBoost for classifying each type were 78%, 96%, and 97%, respectively. In receiver operating characteristics (ROC) analysis for CS, XGBoost, and RF showed a significantly greater diagnostic power than the DT. However, in ROC analysis for PA, only RF exhibited better diagnostic performance than DT. Conclusion The combination of LC-MS-based steroid profiling with machine learning algorithms could be a promising one-step diagnostic approach for the classification of adrenal tumor subtypes.

The protein encoded by the Gene Associated with Retinoid-Interferon-Induced Mortality-19 (GRIM-19) is located in the mitochondrial inner membrane and is homologous to the NADH dehydrogenase 1-alpha subcomplex subunit 13 of the electron transport chain.Multiple sclerosis (MS) is a demyelinating disease that damages the brain and spinal cord.Although both the cause and mechanism of MS progression remain unclear, it is accepted that an immune disorder is involved. We explored whether GRIM-19 ameliorated MS by increasing the levels of inflammatory cytokines and immune cells; we used a mouse model of experimental autoimmune encephalomyelitis (EAE) to this end. Six-to-eight-week-old male C57BL/6, IFNγ-knockout (KO), and GRIM-19 transgenic mice were used; EAE was induced in all strains. A GRIM-19 overexpression vector (GRIM19 OVN) was electrophoretically injected intravenously. The levels of Th1 and Th17 cells were measured via flow cytometry, immunofluorescence, and immunohistochemical analysis. IL-17A and IFNγ expression levels were assessed via ELISA and quantitative PCR. IL-17A expression decreased and IFNγ expression increased in EAE mice that received injections of the GRIM19 OVN. GRIM-19 transgenic mice expressed more IFNγ than did wild-type mice; this inhibited EAE development. However, the effect of GRIM-19 overexpression on the EAE of IFNγ-KO mice did not differ from that of the empty vector. GRIM-19 expression was therapeutic for EAE mice, elevating the IFNγ level. GRIM-19 regulated the Th17/Treg cell balance.