Iskanje novih biokemijskih označevalcev endometrioze in raka endometrija s pristopi proteomike in metabolomike

Endometriosis and endometrial cancer are hormone-dependent gynecological diseases. Endometriosis is one of the most common chronic benign gynecological diseases; it is predominantly prevalent in pre-menopausal women, and is the most common cause of infertility. Endometrial cancer is particularly prevalent in post-menopausal women, and it is the fifth most common malignant disease in Slovenia, and the sixth in the world. As hormone-dependent diseases, increased estrogen levels and/or decreased progesterone levels are a risk factor for the emergence of both endometriosis and endometrial cancer. Diagnosis of endometriosis is currently performed through an invasive surgical approach, as to date there are no biomarkers known that can provide reliable non-invasive diagnosis. One aim of our studies on endometriosis and endometrial cancer is thus the identification of biomarkers that can be combined with clinical data of patients as the starting point for statistical analysis and construction of diagnostic models that can be translated into clinical practice. Diagnostic biomarkers of endometriosis and endometrial cancer will enable their earlier detection, allowing earlier treatment to prevent progression of these diseases. Similarly, we aim to identify prognostic biomarkers of endometrial cancer, through which it will be possible to better define the aggressive nature of these cancers, and hence to adjust the treatment approaches for these patients accordingly. We searched for biomarkers of endometriosis and endometrial cancer among individual proteins (i. e. CA-125, HE4, ARX) as well as in a panel of proteins (i. e. from 40 to 900 different proteins) and metabolites (i. e. from 163 to 188 different metabolites). In the first part of this project for my doctoral thesis, and on the basis of a previous transcriptomic study, we investigated the Aristaless-related homeobox (ARX) protein as a biomarker of endometriosis. However, this was not the case, as ARX originates from the ovarian stroma rather than the endometriotic epithelial or stromal cells. Therefore, as ARX is found in the ovarian stroma and cells derived from the ovarian stroma, and also in all types of sex-cord stromal tumors of the ovary, we hypothesize that it represents a marker for sex-cord stromal differentiation in ovarian tumours. We collected blood samples from patients with endometriosis and endometrial cancer, and also from control groups of patients, in collaboration with the Department of Obstetrics and Gynaecology at the University Medical Centre Ljubljana (Ljubljana, Slovenia) and with the Medical University (Vienna, Austria). On the basis of the serum concentrations of two tumor markers, CA-125 and HE4, and the collected patient clinical data and our logistic regression analysis, several diagnostic models were constructed for patients with endometriosis, and a diagnostic model was constructed for patients with endometrial cancer. Here, we showed that serum HE4 levels have superior prognostic value compared to serum CA-125 levels, although we did not succeed in establishing a suitable prognostic model. In a further collaboration with Helmholtz Zentrum München (Institute of Experimental Genetics, Genome Analysis Centre, Nueherberg, Germany), we used commercially available analytical kits and liquid chromatography–tandem mass spectrometry to define the plasma concentrations of 188 and 163 different lipid metabolites in patients with endometriosis and endometrial cancer, respectively. Our preliminary analysis of these metabolites as biomarkers of endometriosis indicated several different statistically significant metabolic variables between patients with different types of endometriosis and the control group. This provided the starting point for the construction of a diagnostic model. In the metabolic study for patients with endometrial cancer, three individual metabolites and 341 metabolite ratios were identified as potential diagnostic biomarkers. We thus constructed a diagnostic model using three metabolite ratios and with the addition of smoking status. For these patients with endometrial cancer, we also constructed four different prognostic models for the presence of deep myometrial invasion, plus a model for the presence of lymphovascular invasion. Using plasma samples collected from patients with peritoneal endometriosis and a control group of patients, we also used proteomics aporoch with antibody microarrays to define 24 potential biochemical markers from a set of 900 different proteins. Although this was carried out on a relatively small number of plasma samples, one of these biochemical markers was also confirmed for a larger number of samples from patients with different types of endometriosis. We also used a high-performance immunological method known as ‘Luminex’ to determine the concentrations of 40 different cytokines and chemokines in plasma samples from patients with endometriosis and from a control group of patients. Through the appropriate statistical approaches, the aim was to define a diagnostic model here to separate the patients with endometriosis from the control group of patients; however, such a model could not be established on the basis of the plasma concentrations of these inflammatory factors. These studies have contributed to the identification of biomarkers of endometriosis and endometrial cancer. We have demonstrated that on the basis of the levels of certain proteins and/or metabolites, the collected clinical data of the patients, and the appropriate statistical approaches, models with good diagnostic and/or prognostic characteristics can be defined. Following additional validation studies, the aim is to transfer these to clinical practice. |