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One of the main problems we face with PPGL is the lack of molecular markers capable of predicting the development of metastases in patients. Telomere-related genes, such as TERT and ATRX, have been recently described in PPGL, supporting the association between the activation of immortalization mechanisms and disease progression. However, the contribution of other genes involving telomere preservation machinery has not been previously investigated. In this work, we aimed to analyze the prognostic value of a comprehensive set of genes involved in telomere maintenance. For this study, we collected 165 PPGL samples (97 non-metastatic/63 metastatic), genetically characterized, in which the expression of 29 genes of interest was studied by NGS. Three of the 29 genes studied, TERT, ATRX and NOP10, showed differential expression between metastatic and non-metastatic cases, and alterations in these genes were associated with a shorter time to progression, independent of SDHB-status. We studied telomere length by Q-FISH in patient samples and in an in vitro model. NOP10 overexpressing tumors displayed an intermediate-length telomere phenotype without ALT, and in vitro results suggest that NOP10 has a role in telomerase-dependent telomere maintenance. We also propose the implementation of NOP10 IHC to better stratify PPGL patients.
Despite considerable morbidity and mortality, numerous cases of endocrine hypertension (EHT) forms, including primary aldosteronism (PA), pheochromocytoma and functional paraganglioma (PPGL), and Cushing’s syndrome (CS), remain undetected. We aimed to establish signatures for the different forms of EHT, investigate potentially confounding effects and establish unbiased disease biomarkers. Plasma samples were obtained from 13 biobanks across seven countries and analyzed using untargeted NMR metabolomics. We compared unstratified samples of 106 PHT patients to 231 EHT patients, including 104 PA, 94 PPGL and 33 CS patients. Spectra were subjected to a multivariate statistical comparison of PHT to EHT forms and the associated signatures were obtained. Three approaches were applied to investigate and correct confounding effects. Though we found signatures that could separate PHT from EHT forms, there were also key similarities with the signatures of sample center of origin and sample age. The study design restricted the applicability of the corrections employed. With the samples that were available, no biomarkers for PHT vs. EHT could be identified. The complexity of the confounding effects, evidenced by their robustness to correction approaches, highlighted the need for a consensus on how to deal with variabilities probably attributed to preanalytical factors in retrospective, multicenter metabolomics studies.
Predicting hypertension subtypes with machine learning using targeted metabolites and their ratios
(2022)
Hypertension is a major global health problem with high prevalence and complex associated health risks. Primary hypertension (PHT) is most common and the reasons behind primary hypertension are largely unknown. Endocrine hypertension (EHT) is another complex form of hypertension with an estimated prevalence varying from 3 to 20% depending on the population studied. It occurs due to underlying conditions associated with hormonal excess mainly related to adrenal tumours and sub-categorised: primary aldosteronism (PA), Cushing’s syndrome (CS), pheochromocytoma or functional paraganglioma (PPGL). Endocrine hypertension is often misdiagnosed as primary hypertension, causing delays in treatment for the underlying condition, reduced quality of life, and costly antihypertensive treatment that is often ineffective. This study systematically used targeted metabolomics and high-throughput machine learning methods to predict the key biomarkers in classifying and distinguishing the various subtypes of endocrine and primary hypertension. The trained models successfully classified CS from PHT and EHT from PHT with 92% specificity on the test set. The most prominent targeted metabolites and metabolite ratios for hypertension identification for different disease comparisons were C18:1, C18:2, and Orn/Arg. Sex was identified as an important feature in CS vs. PHT classification.