Yang Qingling, Sun Qingrong, Zhang Yunuo, Gong Renjie, Wang Majie, Li Jiankang, Lai Maode, Lai Chong
School of Basic Medical Sciences and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China.
College of Information Technology, ZheJiang Shuren University, Hangzhou, China.
Metabolomics. 2025 Jun 22;21(4):89. doi: 10.1007/s11306-025-02284-6.
The development of adrenocortical adenoma (ACA) affects the endocrine homeostasis of the patient and causes various pathophysiological abnormalities. At this stage, the diagnosis of the disease and the distinguishing of adenoma subtypes in patients with ACA remains an unresolved clinical issue. Our study aimed to identify biomarkers for adenoma subtypes and their altered metabolic profiles. We also explored the metabolic differences between non-functional adenomas (NFA) of different sizes.
In this study, we employed untargeted metabolomic analysis on a discovery set of 246 subjects and a validation set of 275 subjects. Following the construction of a biomarker diagnostic model, we proceeded to validate the model through targeted metabolomic analysis in an independent cohort of 631 participants.
In adenoma subtypes, the disturbed pathways in aldosterone-producing adenoma (APA), cortisol-producing adenoma (Cushing's syndrome, CS) and NFA were all mainly focused on the tricarboxylic acid cycle, purine metabolism, and lipid metabolism pathways. In NFA of different sizes, the metabolic profiles did not change significantly as the tumor increased in size. Furthermore, we successfully identified uric acid, isocitric acid, and proline as diagnostic biomarkers for ACA, 4-hydroxyestrone as a reliable marker for NFA, and LysoPC(P-16:0/0:0) for distinguishing APA from CS.
The plasma of patients with ACA shows significant metabolic alterations, with a similar pattern of metabolic disturbances between the different adenoma subtypes. In addition, uric acid, isocitric acid and proline combinations, 4-hydroxyestrone and LysoPC (P-16:0/0:0) could serve as potential biomarkers to complement and improve the diagnosis of ACA.
肾上腺皮质腺瘤(ACA)的发展会影响患者的内分泌稳态,并导致各种病理生理异常。现阶段,ACA患者的疾病诊断及腺瘤亚型鉴别仍是未解决的临床问题。我们的研究旨在识别腺瘤亚型的生物标志物及其改变的代谢谱。我们还探讨了不同大小的无功能腺瘤(NFA)之间的代谢差异。
在本研究中,我们对246名受试者的发现集和275名受试者的验证集进行了非靶向代谢组学分析。构建生物标志物诊断模型后,我们通过对631名参与者的独立队列进行靶向代谢组学分析来验证该模型。
在腺瘤亚型中,醛固酮生成腺瘤(APA)、皮质醇生成腺瘤(库欣综合征,CS)和NFA中受干扰的途径均主要集中在三羧酸循环、嘌呤代谢和脂质代谢途径。在不同大小的NFA中,随着肿瘤大小增加,代谢谱没有显著变化。此外,我们成功鉴定出尿酸、异柠檬酸和脯氨酸作为ACA的诊断生物标志物,4-羟基雌酮作为NFA的可靠标志物,以及溶血磷脂酰胆碱(LysoPC,P-16:0/0:0)用于区分APA和CS。
ACA患者的血浆显示出显著的代谢改变,不同腺瘤亚型之间的代谢紊乱模式相似。此外,尿酸、异柠檬酸和脯氨酸组合、4-羟基雌酮和LysoPC(P-16:0/0:0)可作为潜在生物标志物,以补充和改善ACA的诊断。