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.

The detection of CA 125 protein in buffer solution with a silicon-on-insulator (SOI)-based nanoribbon (NR) biosensor was experimentally demonstrated. In the biosensor, sensor chips, bearing an array of 12 nanoribbons (NRs) with n-type conductance, were employed. In the course of the analysis with the NR biosensor, the target protein was biospecifically captured onto the surface of the NRs, which was sensitized with covalently immobilized aptamers against CA 125. Atomic force microscopy (AFM) and mass spectrometry (MS) were employed in order to confirm the formation of the probe–target complexes on the NR surface. Via AFM and MS, the formation of aptamer–antigen complexes on the surface of SOI substrates with covalently immobilized aptamers against CA 125 was revealed, thus confirming the efficient immobilization of the aptamers onto the SOI surface. The biosensor signal, resulting from the biospecific interaction between CA 125 and the NR-immobilized aptamer probes, was shown to increase with an increase in the target protein concentration. The minimum detectable CA 125 concentration was as low as 1.5 × 10⁻¹⁷ M. Moreover, with the biosensor proposed herein, the detection of CA 125 in the plasma of ovarian cancer patients was demonstrated.

This work demonstrates the use of a modified mica to concentrate proteins, which is required for proteomic profiling of blood plasma by mass spectrometry (MS). The surface of mica substrates, which are routinely used in atomic force microscopy (AFM), was modified with a photocrosslinker to allow “irreversible” binding of proteins via covalent bond formation. This modified substrate was called the AFM chip. This study aimed to determine the role of the surface and crosslinker in the efficient concentration of various types of proteins in plasma over a wide concentration range. The substrate surface was modified with a 4-benzoylbenzoic acid N-succinimidyl ester (SuccBB) photocrosslinker, activated by UV irradiation. AFM chips were incubated with plasma samples from a healthy volunteer at various dilution ratios (10²X, 10⁴X, and 10⁶X). Control experiments were performed without UV irradiation to evaluate the contribution of physical protein adsorption to the concentration efficiency. AFM imaging confirmed the presence of protein layers on the chip surface after incubation with the samples. MS analysis of different samples indicated that the proteomic profile of the AFM-visualized layers contained common and unique proteins. In the working series of experiments, 228 proteins were identified on the chip surface for all samples, and 21 proteins were not identified in the control series. In the control series, a total of 220 proteins were identified on the chip surface, seven of which were not found in the working series. In plasma samples at various dilution ratios, a total of 146 proteins were identified without the concentration step, while 17 proteins were not detected in the series using AFM chips. The introduction of a concentration step using AFM chips allowed us to identify more proteins than in plasma samples without this step. We found that AFM chips with a modified surface facilitate the efficient concentration of proteins owing to the adsorption factor and the formation of covalent bonds between the proteins and the chip surface. The results of our study can be applied in the development of highly sensitive analytical systems for determining the complete composition of the plasma proteome.

MicroRNAs, which circulate in blood, are characterized by high diagnostic value; in biomedical research, they can be considered as candidate markers of various diseases. Mature microRNAs of glial cells and neurons can cross the blood–brain barrier and can be detected in the serum of patients with autism spectrum disorders (ASD) as components of macrovesicles, macromolecular protein and low-density lipoprotein particles. In our present study, we have proposed an approach, in which microRNAs in protein complexes can be concentrated on the surface of AFM chips with oligonucleotide molecular probes, specific against the target microRNAs. MicroRNAs, associated with the development of ASD in children, were selected as targets. The chips with immobilized molecular probes were incubated in serum samples of ASD patients and healthy volunteers. By atomic force microscopy (AFM), objects on the AFM chip surface have been revealed after incubation in the serum samples. The height of these objects amounted to 10 nm and 6 nm in the case of samples of ASD patients and healthy volunteers, respectively. MALDI-TOF-MS analysis of protein components on the chip surface allowed us to identify several cell proteins. These proteins are involved in the binding of nucleic acids (GBG10, RT24, RALYL), in the organization of proteasomes and nucleosomes (PSA4, NP1L4), and participate in the functioning of the channel of active potassium transport (KCNE5, KCNV2).

Mass spectrometry (MS) is one of the main techniques for protein identification. Herein, MS has been employed for the identification of bovine serum albumin (BSA), which was covalently immobilized on the surface of a mica chip intended for investigation by atomic force microscopy (AFM). For the immobilization, two different types of crosslinkers have been used: 4-benzoylbenzoic acid N-succinimidyl ester (SuccBB) and dithiobis(succinimidyl propionate) (DSP). According to the data obtained by using an AFM-based molecular detector, the SuccBB crosslinker was more efficient in BSA immobilization than the DSP. The type of crosslinker used for protein capturing has been found to affect the results of MS identification. The results obtained herein can be applied in the development of novel systems intended for the highly sensitive analysis of proteins with molecular detectors.

Integrating proteomic and metabolomic data remains challenging due to the many-to-many relationships between metabolites and proteins and the spatial constraints of cellular compartmentalization. To address this, we developed PMconv, a web-based application for bidirectional, knowledge-based mapping of proteomic and metabolomic datasets. Leveraging curated associations from the Human Metabolome Database (HMDB) and protein interaction data from STRING, PMconv infers potential biochemical connections between experimentally detected molecules and pathway-annotated partners. The tool supports interactive network visualization and exports compartment annotations from the Human Protein Atlas to facilitate spatial contextualization of inferred interactions. PMconv is designed as an exploratory resource for hypothesis generation and feature engineering in multi-omics research, with the explicit understanding that knowledge-derived associations require experimental validation for compartment-specific interpretation.