Endometriosis is a common gynaecological condition characterized by severe pelvic pain and/or infertility. The combination of nonspecific symptoms and invasive laparoscopic diagnostics have prompted researchers to evaluate potential biomarkers that would enable a non-invasive diagnosis of endometriosis. Endometriosis is an inflammatory disease thus different cytokines represent potential diagnostic biomarkers. As panels of biomarkers are expected to enable better separation between patients and controls we evaluated 40 different cytokines in plasma samples of 210 patients (116 patients with endometriosis; 94 controls) from two medical centres (Slovenian, Austrian). Results of the univariate statistical analysis showed no differences in concentrations of the measured cytokines between patients and controls, confirmed by principal component analysis showing no clear separation amongst these two groups. In order to validate the hypothesis of a more profound (non-linear) differentiating dependency between features, machine learning methods were used. We trained four common machine learning algorithms (decision tree, linear model, k-nearest neighbour, random forest) on data from plasma levels of proteins and patients' clinical data. The constructed models, however, did not separate patients with endometriosis from the controls with sufficient sensitivity and specificity. This study thus indicates that plasma levels of the selected cytokines have limited potential for diagnosis of...
COBISS.SI-ID: 34551001
Irreproducibility of biomedical research is an overarching problem, where several key reasons have been identified, including: i) problems with study design, ii) variability in biological tools (such as antibodies, cell lines), iii) data quality, iv) biased data analysis and interpretation of results, and iv) inadequate documentation and reporting of protocols and further often a lack of publicly available raw data. In post-genomic era biomedical research often depends on collection of biological materials including body fluids and tissue samples from animal models or humans followed by state-of-the-art transcriptomic, proteomic or metabolomics profiling. Omics studies of body fluids (i.e. blood, urine, saliva, cerebrospinal fluid, follicular fluid, peritoneal fluid) are crucial for biomarker discovery, as well as for understanding systemic disease mechanisms, while molecular profiling of tissue samples have essential roles for deciphering disease pathophysiology and for identification of tissue biomarkers and novel drug targets.
COBISS.SI-ID: 33951449
P.I. of the project had an invited lecture at the Pre-Congress Course Diagnosing endometriosis and adenomyosis: Today and tomorrow, ESHRE 2019, Vienna, Austria, 2019. Tea Lanišnik Rižner: Proteomics and metabolomics – Discovery of diagnostic biomarkers of endometriosis.
COBISS.SI-ID: 34666201