Česká společnost pro hmotnostní spektrometrii

(13. ročník České konference hmotnostní spektrometrie a 11. Neformální proteomické setkání - ThS-08)
Towards Biomarker Discovery: Multiomics analysis of Endometriosis

Tomáš Oždian ^1 *, Sára Filipová ¹, Jan Macháň ¹, Richard Masař ¹, Pavlína Javorová ¹, Lukáš Najdekr ¹

1. Univerzita Palackého v Olomouci a Fakultní nemocnice Olomouc

Abstrakt

Endometriosis is a chronic inflammatory disease characterised by the presence of endometrial-like tissue outside the uterus. Affecting approximately 10% of reproductive-age women, its diagnosis is often delayed due to nonspecific symptoms and a lack of reliable biomarkers. In this study, we employ a multiomic approach to investigate plasma-based molecular signatures associated with endometriosis. Plasma samples from patients (Grades I–IV), suspected cases, and matched controls underwent parallel proteomic, non-targeted metabolomic, and lipidomic analysis using extraction protocols optimised for both metabolite and lipid profiling.
Proteomic analysis consisted of DIA analysis and search through the DIA-NN search instrument. Metabolomic and lipidomic data preprocessing included peak picking, quality control-based signal correction (QC-RSC), and statistical filtering using the open-source PySPRESSO pipeline. Principal Component Analysis (PCA) and supervised Partial Least Squares Discriminant Analysis (PLS-DA) revealed clear group separations, particularly between control and diagnosed cases.
In the proteomics branch, cellular oxidant detoxification and the redox-active centre were among the most influenced processes. In the lipidomics branch, lipids such as PC(16:0_20:5) showed differential abundance and fragmentation patterns between groups. These findings underscore the diagnostic potential of untargeted omics approaches in endometriosis. Multi-omic Factor Analysis (MOFA) highlighted latent factors that correlated with disease class and diagnosis. Candidate features were selected based on volcano plots and statistical metrics.
Future work will focus on expanding compound annotation, integrating proteomics, and refining predictive models for clinical translation.

* Korespondující autor: ozdiant@seznam.cz

Poděkování:

The project National Institute for Cancer Research (Programme EXCELES, ID Project No. LX22NPO5102) - Funded by the European Union – Next Generation EU. & JG_2024_026 (UPOL) & IGA LF 2025_006