Identification of potential endometriosis biomarkers in peritoneal fluid and blood plasma via shotgun lipidomics

Integrating Proteomics and Lipidomics for Evaluating the Risk of Breast Cancer Progression: A Pilot Study

Metastasis is a serious and often life-threatening condition, representing the leading cause of death among women with breast cancer (BC). Although the current clinical classification of BC is well-established, the addition of minimally invasive laboratory tests based on peripheral blood biomarkers that reflect pathological changes in the body is of utmost importance. In the current study, the serum proteome and lipidome profiles for 50 BC patients with (25) and without (25) metastasis were studied. Targeted proteomic analysis for concertation measurements of 125 proteins in the serum was performed via liquid chromatography-multiple reaction monitoring mass spectrometry (LC-MRM MS) using the BAK 125 kit (MRM Proteomics Inc., Victoria, BC, Canada). Untargeted label-free lipidomic analysis was performed using liquid chromatography coupled to tandem mass-spectrometry (LC-MS/MS), in both positive and negative ion modes. Finally, 87 serum proteins and 295 lipids were quantified and showed a moderate correlation with tumor grade, histological and biological subtypes, and the number of lymph node metastases. Two highly accurate classifiers that enabled distinguishing between metastatic and non-metastatic BC were developed based on proteomic (accuracy 90%) and lipidomic (accuracy 80%) features. The best classifier (91% sensitivity, 89% specificity, AUC = 0.92) for BC metastasis diagnostics was based on logistic regression and the serum levels of 11 proteins: alpha-2-macroglobulin, coagulation factor XII, adiponectin, leucine-rich alpha-2-glycoprotein, alpha-2-HS-glycoprotein, Ig mu chain C region, apolipoprotein C-IV, carbonic anhydrase 1, apolipoprotein A-II, apolipoprotein C-II and alpha-1-acid glycoprotein 1.

The dynamic inflammatory profile of pregnancy can be monitored using a novel lipid-based mass spectrometry technique

The lipid environment changes throughout pregnancy both physiologically with emergent insulin resistance and pathologically e.g., gestational diabetes mellitus (GDM). Novel mass spectrometry (MS) techniques applied to minimally processed blood might lend themselves to monitoring changing lipid profiles to inform care decisions across pregnancy. In this study we use an intact-sandwich, MALDI-ToF MS method to identify phosphatidylcholine (PC) and lysophosphatidylcholine (LPC) species and calculate their ratio as an indicator of inflammation. Plasma and sera were prepared from venous blood of non-pregnant women (aged 18-40) and pregnant women at 16 weeks, 28 weeks (including GDM-positive women), and 37+ weeks (term) of gestation alongside umbilical cord blood (UCB). Women with a normal menstrual cycle and age-matched men provided finger-prick derived capillary sera at 6 time-points over a month. Serum rather than plasma was preferable for PC/LPC measurement. As pregnancy progresses, an anti-inflammatory phenotype dominates the maternal circulation, evidenced by increasing PC/LPC ratio. In contrast, the PC/LPC ratio of UCB was aligned to that of non-pregnant donors. BMI had no significant effect on the PC/LPC ratio, but GDM-complicated pregnancies had significantly lower PC/LPC at 16 weeks of gestation. To further translate the use of the PC/LPC ratio clinically, the utility of finger-prick blood was evaluated; no significant difference between capillary versus venous serum was found and we revealed the PC/LPC ratio oscillates with the menstrual cycle. Overall, we show that the PC/LPC ratio can be measured simply in human serum and has the potential to be used as a time-efficient and less invasive biomarker of (mal)adaptative inflammation.

Current Role of Modern Chromatography with Mass Spectrometry and Nuclear Magnetic Resonance Spectroscopy in the Investigation of Biomarkers of Endometriosis

Endometriosis has a wide range of clinical manifestations, and the disease course is unpredictable, making the diagnosis a challenging task. Despite significant advances in the pathophysiology of endometriosis and various proposed theories, the exact etiology is not fully understood and is still unknown. The most commonly used biomarker of endometriosis is CA-125, however, it is nonspecific and is applied for cancers diagnosis. Therefore, the development of reliable noninvasive diagnostic tests for the early diagnosis of endometriosis remains one of the top priorities. Omics technologies are very promising approaches for constructing diagnostic models and biomarker discovery. Their use can greatly facilitate the study of such a complex disease as endometriosis. Nowadays, powerful analytical platforms commonly used in omics, such as gas and liquid chromatography with mass spectrometry and nuclear magnetic resonance (NMR) spectroscopy, have proven to be a promising tools for biomarker discovery. The aim of this review is to summarize the various features of the analytical approaches, practical challenges and features of gas and liquid chromatography with MS and NMR spectroscopy (including sample processing protocols, technological advancements, and methodology) used for profiling of metabolites, lipids, peptides and proteins in physiological fluids and tissues from patients with endometriosis. In addition, this report devotes special attention to the issue of how comprehensive analyses of these profiles can effectively contribute to the study of endometriosis. The search query included reports published between 2012 and 2022 years in PubMed, Web-of-Science, SCOPUS, Science Direct.

Is there a role for small molecule metabolite biomarkers in the development of a diagnostic test for endometriosis?

Endometriosis is a disease defined by the presence of benign lesions of endometrial-like glands and stroma outside the endometrial cavity. Affecting an estimated 11.4% of Australian women, symptoms include chronic pelvic pain, dysmenorrhea and infertility. The current gold standard of diagnosis requires an expensive and invasive laparoscopic surgery, resulting in delayed time to treatment. The identification of a non-invasive endometriosis biomarker - a measurable factor correlating with disease presence or activity - has therefore become a priority in endometriosis research, although no biomarker has yet been validated. As small molecule metabolites and lipids have emerged as a potential focus, this review with systematic approach, aims to summarize studies examining metabolomic biomarkers of endometriosis in order to guide future research. EMBASE, PubMed and Web of Science were searched using keywords: lipidomics OR metabolomics OR metabolome AND diagnostic tests OR biomarkers AND endometriosis, and only studies written in English from August 2000 to August 2020 were included. Twenty-nine studies met inclusion and exclusion criteria and were included. These studies identified potential biomarkers in serum, ectopic tissue, eutopic endometrium, peritoneal fluid, follicular fluid, urine, cervical swabs and endometrial fluid. Glycerophospholipids were identified as potential biomarkers in all specimens, except urine and cervical swab specimens. However, no individual molecule or metabolite combination has reached clinical diagnostic utility. Further research using large study populations with robust patient phenotype and specimen characterisation is required if we are to make progress in identifying and validating a non-invasive diagnostic test for endometriosis.

Alterations in lipid profile upon uterine fibroids and its recurrence

Uterine fibroids (UF) is the most common (about 70% cases) type of gynecological disease, with the recurrence rate varying from 11 to 40%. Because UF has no distinct symptomatology and is often asymptomatic, the specific and sensitive diagnosis of UF as well as the assessment for the probability of UF recurrence pose considerable challenge. The aim of this study was to characterize alterations in the lipid profile of tissues associated with the first-time diagnosed UF and recurrent uterine fibroids (RUF) and to explore the potential of mass spectrometry (MS) lipidomics analysis of blood plasma samples for the sensitive and specific determination of UF and RUF with low invasiveness of analysis. MS analysis of lipid levels in the myometrium tissues, fibroids tissues and blood plasma samples was carried out on 66 patients, including 35 patients with first-time diagnosed UF and 31 patients with RUF. The control group consisted of 15 patients who underwent surgical treatment for the intrauterine septum. Fibroids and myometrium tissue samples were analyzed using direct MS approach. Blood plasma samples were analyzed using high performance liquid chromatography hyphened with mass spectrometry (HPLC/MS). MS data were processed by discriminant analysis with projection into latent structures (OPLS-DA). Significant differences were found between the first-time UF, RUF and control group in the levels of lipids involved in the metabolism of glycerophospholipids, sphingolipids, lipids with an ether bond, triglycerides and fatty acids. Significant differences between the control group and the groups with UF and RUF were found in the blood plasma levels of cholesterol esters, triacylglycerols, (lyso) phosphatidylcholines and sphingomyelins. Significant differences between the UF and RUF groups were found in the blood plasma levels of cholesterol esters, phosphotidylcholines, sphingomyelins and triacylglycerols. Diagnostic models based on the selected differential lipids using logistic regression showed sensitivity and specificity of 88% and 86% for the diagnosis of first-time UF and 95% and 79% for RUF, accordingly. This study confirms the involvement of lipids in the pathogenesis of uterine fibroids. A diagnostically significant panel of differential lipid species has been identified for the diagnosis of UF and RUF by low-invasive blood plasma analysis. The developed diagnostic models demonstrated high potential for clinical use and further research in this direction.

Normalization methods for reducing interbatch effect without quality control samples in liquid chromatography-mass spectrometry-based studies

Data normalization is an essential part of a large-scale untargeted mass spectrometry metabolomics analysis. Autoscaling, Pareto scaling, range scaling, and level scaling methods for liquid chromatography-mass spectrometry data processing were compared with the most common normalization methods, including quantile normalization, probabilistic quotient normalization, and variance stabilizing normalization. These methods were tested on eight datasets from various clinical studies. The efficiency of the data normalization was assessed by the distance between clusters corresponding to batches and the distance between clusters corresponding to clinical groups in the space of principal components, as well as by the number of features with a pairwise statistically significant difference between the batches and the number of features with a pairwise statistically significant difference between clinical groups. Autoscaling demonstrated the most effective reduction in interbatch variation and can be preferable to probabilistic quotient or quantile normalization in liquid chromatography-mass spectrometry data.

Metabolomics for Diagnosis and Prognosis of Uterine Diseases? A Systematic Review

This systematic review analyses the contribution of metabolomics to the identification of diagnostic and prognostic biomarkers for uterine diseases. These diseases are diagnosed invasively, which entails delayed treatment and a worse clinical outcome. New options for diagnosis and prognosis are needed. PubMed, OVID, and Scopus were searched for research papers on metabolomics in physiological fluids and tissues from patients with uterine diseases. The search identified 484 records. Based on inclusion and exclusion criteria, 44 studies were included into the review. Relevant data were extracted following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) checklist and quality was assessed using the QUADOMICS tool. The selected metabolomics studies analysed plasma, serum, urine, peritoneal, endometrial, and cervico-vaginal fluid, ectopic/eutopic endometrium, and cervical tissue. In endometriosis, diagnostic models discriminated patients from healthy and infertile controls. In cervical cancer, diagnostic algorithms discriminated patients from controls, patients with good/bad prognosis, and with/without response to chemotherapy. In endometrial cancer, several models stratified patients from controls and recurrent from non-recurrent patients. Metabolomics is valuable for constructing diagnostic models. However, the majority of studies were in the discovery phase and require additional research to select reliable biomarkers for validation and translation into clinical practice. This review identifies bottlenecks that currently prevent the translation of these findings into clinical practice.