Tags

Type your tag names separated by a space and hit enter

Multiplex analysis of 40 cytokines do not allow separation between endometriosis patients and controls.
Sci Rep. 2019 11 13; 9(1):16738.SR

Abstract

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 endometriosis.

Authors+Show Affiliations

Institute of Biochemistry, Faculty of Medicine, University of Ljubljana, 1000, Ljubljana, Slovenia.Institute of Computer Science, University of Tartu, Liivi 2, 50409, Tartu, Estonia. Quretec Ltd., Ülikooli 6A, Tartu, 51003, Estonia.Department of Obstetrics and Gynaecology, University Medical Centre Ljubljana, 1000, Ljubljana, Slovenia.Department of Obstetrics and Gynecology, Medical University Vienna, 1090, Vienna, Austria.Department of Obstetrics and Gynecology, Medical University Vienna, 1090, Vienna, Austria.Institute of Computer Science, University of Tartu, Liivi 2, 50409, Tartu, Estonia. Quretec Ltd., Ülikooli 6A, Tartu, 51003, Estonia.Institute of Biochemistry, Faculty of Medicine, University of Ljubljana, 1000, Ljubljana, Slovenia. Tea.Lanisnik-Rizner@mf.uni-lj.si.

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

31723213

Citation

Knific, Tamara, et al. "Multiplex Analysis of 40 Cytokines Do Not Allow Separation Between Endometriosis Patients and Controls." Scientific Reports, vol. 9, no. 1, 2019, p. 16738.
Knific T, Fishman D, Vogler A, et al. Multiplex analysis of 40 cytokines do not allow separation between endometriosis patients and controls. Sci Rep. 2019;9(1):16738.
Knific, T., Fishman, D., Vogler, A., Gstöttner, M., Wenzl, R., Peterson, H., & Rižner, T. L. (2019). Multiplex analysis of 40 cytokines do not allow separation between endometriosis patients and controls. Scientific Reports, 9(1), 16738. https://doi.org/10.1038/s41598-019-52899-8
Knific T, et al. Multiplex Analysis of 40 Cytokines Do Not Allow Separation Between Endometriosis Patients and Controls. Sci Rep. 2019 11 13;9(1):16738. PubMed PMID: 31723213.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Multiplex analysis of 40 cytokines do not allow separation between endometriosis patients and controls. AU - Knific,Tamara, AU - Fishman,Dmytro, AU - Vogler,Andrej, AU - Gstöttner,Manuela, AU - Wenzl,René, AU - Peterson,Hedi, AU - Rižner,Tea Lanišnik, Y1 - 2019/11/13/ PY - 2019/07/24/received PY - 2019/10/24/accepted PY - 2019/11/15/entrez PY - 2019/11/15/pubmed PY - 2019/11/15/medline SP - 16738 EP - 16738 JF - Scientific reports JO - Sci Rep VL - 9 IS - 1 N2 - 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 endometriosis. SN - 2045-2322 UR - https://www.unboundmedicine.com/medline/citation/31723213/Multiplex_analysis_of_40_cytokines_do_not_allow_separation_between_endometriosis_patients_and_controls_ L2 - http://dx.doi.org/10.1038/s41598-019-52899-8 DB - PRIME DP - Unbound Medicine ER -
Try the Free App:
Prime PubMed app for iOS iPhone iPad
Prime PubMed app for Android
Prime PubMed is provided
free to individuals by:
Unbound Medicine.