Stratification of ovarian cancer borderline from high-grade serous carcinoma patients by quantitative serum NMR spectroscopy of metabolites, lipoproteins, and inflammatory markers

Quantitative Blood Serum IVDr NMR Spectroscopy in Clinical Metabolomics of Cancer, Neurodegeneration, and Internal Medicine

Despite more than two decades of metabolomics having joined the "omics" scenery, to date only a few novel blood metabolite biomarkers have found their way into the clinic. This is changing now by massive large-scale population metabolic phenotyping for both healthy and disease cohorts. Here, nuclear magnetic resonance (NMR) spectroscopy is a method of choice, as typical blood serum markers can be easily quantified and by knowledge of precise reference concentrations, more and more NMR-amenable biomarkers are established, moving NMR from research to clinical application. Besides customized approaches, to date two major commercial platforms have evolved based on either 600 MHz (14.1 Tesla) or 500 MHz (11.7 Tesla) high-field NMR systems. This chapter provides an introduction into the field of quantitative in vitro diagnostics research (IVDr) NMR at 600 MHz and its application within clinical research of cancer, neurodegeneration, and internal medicine.

The Diversity of Methylation Patterns in Serous Borderline Ovarian Tumors and Serous Ovarian Carcinomas

Background: Changes in DNA methylation patterns are a pivotal mechanism of carcinogenesis. In some tumors, aberrant methylation precedes genetic changes, while gene expression may be more frequently modified due to methylation alterations than by mutations. Methods: Herein, 128 serous ovarian tumors were analyzed, including borderline ovarian tumors (BOTS) with (BOT.V600E) and without (BOT) the BRAF V600E mutation, low-grade (lg), and high-grade (hg) ovarian cancers (OvCa). The methylome of the samples was profiled with Infinium MethylationEPIC microarrays. Results: The biggest number of differentially methylated (DM) CpGs and regions (DMRs) was found between lgOvCa and hgOvCa. By contrast, the BOT.V600E tumors had the lowest number of DM CpGs and DMRs compared to all other groups and, in relation to BOT, their genome was strongly downmethylated. Remarkably, the ten most significant DMRs, discriminating BOT from lgOvCa, encompassed the MHC region on chromosome 6. We also identified hundreds of DMRs, being of potential use as predictive biomarkers in BOTS and hgOvCa. DMRs with the best discriminative capabilities overlapped the following genes: BAIAP3, IL34, WNT10A, NEU1, SLC44A4, and HMOX1, TCN2, PES1, RP1-56J10.8, ABR, NCAM1, RP11-629G13.1, AC006372.4, NPTXR in BOTS and hgOvCa, respectively. Conclusions: The global genome-wide hypomethylation positively correlates with the increasing aggressiveness of ovarian tumors. We also assume that the immune system may play a pivotal role in the transition from BOTS to lgOvCa. Given that the BOT.V600E tumors had the lowest number of DM CpGs and DMRs compared to all other groups, when methylome is considered, such tumors might be placed in-between BOT and OvCa.

The roles of long non-coding RNAs in ovarian cancer: from functions to therapeutic implications

Long non-coding RNAs (lncRNAs) are multifunctional and participate in a variety of biological processes and gene regulatory networks. The deregulation of lncRNAs has been extensively implicated in diverse human diseases, especially in cancers. Overwhelming evidence demonstrates that lncRNAs are essential to the pathophysiological processes of ovarian cancer (OC), acting as regulators involved in metastasis, cell death, chemoresistance, and tumor immunity. In this review, we illustrate the expanded functions of lncRNAs in the initiation and progression of OC and elaborate on the signaling pathways in which they pitch. Additionally, the potential clinical applications of lncRNAs as biomarkers in the diagnosis and treatment of OC were emphasized, cementing the bridge of communication between clinical practice and basic research.

Quantitative Metabolomics and Lipoprotein Analysis of PDAC Patients Suggests Serum Marker Categories for Pancreatic Function, Pancreatectomy, Cancer Metabolism, and Systemic Disturbances

Pancreatic ductal adenocarcinoma (PDAC) is difficult to diagnose in the early stages and lacks reliable biomarkers. The scope of this project was to establish quantitative nuclear magnetic resonance (NMR) spectroscopy to comprehensively study blood serum alterations in PDAC patients. Serum samples from 34 PDAC patients obtained before and after pancreatectomy as well as 83 age- and sex-matched control samples from healthy donors were analyzed with in vitro diagnostics research (IVDr) proton NMR spectroscopy at 600 MHz. Uni- and multivariate statistics were applied to identify significant biofluid alterations. We identified 29 significantly changed metabolites and 98 lipoproteins when comparing serum from healthy controls with those of PDAC patients. The most prominent features were assigned to (i) markers of pancreatic function (e.g., glucose and blood triglycerides), (ii) markers related to surgery (e.g., ketone bodies and blood cholesterols), (iii) PDAC-associated markers (e.g., amino acids and creatine), and (iv) markers for systemic disturbances in PDAC (e.g., gut metabolites DMG, TMAO, DMSO2, and liver lipoproteins). Quantitative serum NMR spectroscopy is suited as a diagnostic tool to investigate PDAC. Remarkably, 2-hydroxybutyrate (2-HB) as a previously suggested marker for insulin resistance was found in extraordinarily high levels only after pancreatectomy, suggesting this metabolite is the strongest marker for pancreatic loss of function.

Urinary phenotyping of SARS-CoV-2 infection connects clinical diagnostics with metabolomics and uncovers impaired NAD+ pathway and SIRT1 activation

OBJECTIVES

The stratification of individuals suffering from acute and post-acute SARS-CoV-2 infection remains a critical challenge. Notably, biomarkers able to specifically monitor viral progression, providing details about patient clinical status, are still not available. Herein, quantitative metabolomics is progressively recognized as a useful tool to describe the consequences of virus-host interactions considering also clinical metadata.

METHODS

The present study characterized the urinary metabolic profile of 243 infected individuals by quantitative nuclear magnetic resonance (NMR) spectroscopy and liquid chromatography mass spectrometry (LC-MS). Results were compared with a historical cohort of noninfected subjects. Moreover, we assessed the concentration of recently identified antiviral nucleosides and their association with other metabolites and clinical data.

RESULTS

Urinary metabolomics can stratify patients into classes of disease severity, with a discrimination ability comparable to that of clinical biomarkers. Kynurenines showed the highest fold change in clinically-deteriorated patients and higher-risk subjects. Unique metabolite clusters were also generated based on age, sex, and body mass index (BMI). Changes in the concentration of antiviral nucleosides were associated with either other metabolites or clinical variables. Increased kynurenines and reduced trigonelline excretion indicated a disrupted nicotinamide adenine nucleotide (NAD+) and sirtuin 1 (SIRT1) pathway.

CONCLUSIONS

Our results confirm the potential of urinary metabolomics for noninvasive diagnostic/prognostic screening and show that the antiviral nucleosides could represent novel biomarkers linking viral load, immune response, and metabolism. Moreover, we established for the first time a casual link between kynurenine accumulation and deranged NAD+/SIRT1, offering a novel mechanism through which SARS-CoV-2 manipulates host physiology.