This study focuses on the systematization and integration of ovarian cancer multi-omics data, revealing patterns in the application of different omics-based approaches and assessing factors that affect the identification of potential biomarkers. An integrative analysis of 51 publications revealed 1649 potential biomarkers. The findings emphasized the molecular diversity of ovarian cancer. They demonstrated the importance of performing the comprehensive integration of molecular and clinical data to search for diagnostic alternatives and molecular patterns underlying ovarian cancer. The heterogeneity of data sources, differences in data acquisition and analysis protocols, and the lack of uniform standards affect the reproducibility of the results of genomic and post-genomic profiling. Multi-omics studies are more promising than mono-omics-based ones. Despite technological advances, researchers continue to focus on results obtained over a decade ago, which may hinder the scientific community from exploring new horizons in ovarian cancer research.

Early detection of ovarian cancer remains challenging because of the lack of sensitive and reproducible blood-based biomarkers. A major challenge in plasma proteomics is the extremely wide dynamic range of protein concentrations, which prevents simultaneous detection of both high- and low abundance proteins and limits the identification of disease-associated signals. In this study, we applied a combined atomic force microscopy and mass spectrometry (AFM/MS) approach to investigate how sample dilution affects plasma proteome coverage and the detection of differences between healthy donors and patients with stage I and stage III ovarian cancer. Plasma samples were analyzed at two dilution levels (1:100 and 1:10,000). At 1:100 dilution, a total of 235 proteins were identified across all samples, representing the union of all replicates and groups. The reproducible CORE proteome comprised 169 proteins in the Healthy group, 183 in the Stage I group, and 193 in the Stage III group. Differential analysis revealed distinct, non-overlapping protein sets at each dilution level. At 1:100 dilution, most altered proteins were decreased in patients and corresponded to major plasma components, including complement proteins and protease inhibitors. At 1:10,000 dilution, most altered proteins were increased and were predominantly immunoglobulin-related proteins, along with complement regulatory components. These findings show that sample dilution determines which fraction of the plasma proteome is observable. Here, proteome depth refers to the total number of non-redundant proteins accessible within the analytical workflow. When CORE sets from all groups were combined, 216 proteins were identified at 1:100 and 149 at 1:10,000, with 133 shared between the two dilution conditions. The higher dilution contributed 16 additional CORE proteins not detected in the 1:100 CORE union, increasing the combined CORE set to 232 proteins. Thus, higher dilution alone did not increase proteome depth, but provided complementary protein identifications that increased cumulative proteome depth when both dilution conditions were considered together. This effect reflects dilution-dependent selectivity in the composition of the detectable protein subset.

This article presents the results of computational screening of approved drug compounds to find new inhibitors of acetylcholinesterase (AChE), an enzyme that plays a key role in the regulation of neurotransmission and cognitive functions. Using molecular docking and quantum chemical postprocessing methods, the authors conducted a virtual screening of a library of 2909 drug compounds approved for clinical use from two ZINC database libraries. The screening process employed the SOL docking program with MMFF94 force field and genetic algorithms for global optimization, targeting the human AChE structure (PDB ID: 6O4W). As a result of the docking, 211 of the most promising ligands were selected for calculating their enthalpy of binding to AChE using quantum chemical calculations. Based on the analysis of the free energy of binding estimated by docking score and the enthalpy of binding calculated using the quantum-chemical PM7 method with the COSMO solvent model, 16 of the most promising candidates for the role of AChE inhibitors were identified. Notable candidates include Pixantrone, Guanfacine and Hydroxystilbamidine. These compounds, although not previously known as AChE inhibitors, represent diverse chemical classes including substituted thiophenes, pyridines, and fused nitrogen-containing heterocycles, showing high potential for treating neurodegenerative diseases such as Alzheimer's disease.