Publications by authors named "Natalia V Potoldykova"

7 Publications

  • Page 1 of 1

Micro-Raman Characterization of Structural Features of High-k Stack Layer of SOI Nanowire Chip, Designed to Detect Circular RNA Associated with the Development of Glioma.

Molecules 2021 Jun 18;26(12). Epub 2021 Jun 18.

Laboratory of Nanobiotechnology, Institute of Biomedical Chemistry, 119121 Moscow, Russia.

The application of micro-Raman spectroscopy was used for characterization of structural features of the high-k stack (h-k) layer of "silicon-on-insulator" (SOI) nanowire (NW) chip (h-k-SOI-NW chip), including AlO and HfO in various combinations after heat treatment from 425 to 1000 °C. After that, the NW structures h-k-SOI-NW chip was created using gas plasma etching optical lithography. The stability of the signals from the monocrine phase of HfO was shown. Significant differences were found in the elastic stresses of the silicon layers for very thick (>200 nm) AlO layers. In the UV spectra of SOI layers of a silicon substrate with HfO, shoulders in the Raman spectrum were observed at 480-490 cm of single-phonon scattering. The h-k-SOI-NW chip created in this way has been used for the detection of DNA-oligonucleotide sequences (oDNA), that became a synthetic analog of circular RNA-circ-SHKBP1 associated with the development of glioma at a concentration of 1.1 × 10 M. The possibility of using such h-k-SOI NW chips for the detection of circ-SHKBP1 in blood plasma of patients diagnosed with neoplasm of uncertain nature of the brain and central nervous system was shown.
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http://dx.doi.org/10.3390/molecules26123715DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8234461PMC
June 2021

: R-Based Software Toolbox for Untargeted Metabolomics with Bladder Cancer Biomarkers Discovery Case Study.

J Proteome Res 2021 Jun 23. Epub 2021 Jun 23.

Chemistry Department, Lomonosov Moscow State University, 119991 Moscow, Russia.

Large-scale untargeted LC-MS-based metabolomic profiling is a valuable source for systems biology and biomarker discovery. Data analysis and processing are major tasks due to the high complexity of generated signals and the presence of unwanted variations. In the present study, we introduce an R-based open-source collection of scripts called (), which provides comprehensive data processing. is developed by integrating various R packages and metabolomics software tools and can be easily set up and prepared to create a custom pipeline. Novel computational features are related to quality control samples-based signal processing and are implemented by gradient boosting, tree-based, and other nonlinear regression algorithms. Bladder cancer biomarkers discovery study which is based on untargeted LC-MS profiling of urine samples is performed to demonstrate exhaustive functionality of the developed software tool. Unique examination among dozens of metabolomics-specific data curation methods was carried out at each processing step. As a result, potential biomarkers were identified, statistically validated, and described by metabolism disorders. Our study demonstrates that helps to make untargeted LC-MS metabolomic profiling with the latest computational features readily accessible in a ready-to-use unified manner to a research community.
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http://dx.doi.org/10.1021/acs.jproteome.1c00392DOI Listing
June 2021

Pharmacogenetic Testing: A Tool for Personalized Drug Therapy Optimization.

Pharmaceutics 2020 Dec 19;12(12). Epub 2020 Dec 19.

Biobanking Group, Branch of Institute of Biomedical Chemistry "Scientific and Education Center", 109028 Moscow, Russia.

Pharmacogenomics is a study of how the genome background is associated with drug resistance and how therapy strategy can be modified for a certain person to achieve benefit. The pharmacogenomics (PGx) testing becomes of great opportunity for physicians to make the proper decision regarding each non-trivial patient that does not respond to therapy. Although pharmacogenomics has become of growing interest to the healthcare market during the past five to ten years the exact mechanisms linking the genetic polymorphisms and observable responses to drug therapy are not always clear. Therefore, the success of PGx testing depends on the physician's ability to understand the obtained results in a standardized way for each particular patient. The review aims to lead the reader through the general conception of PGx and related issues of PGx testing efficiency, personal data security, and health safety at a current clinical level.
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http://dx.doi.org/10.3390/pharmaceutics12121240DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7765968PMC
December 2020

Identification of Clinically Significant Prostate Cancer by Combined and mRNA Detection in Urine Samples.

Res Rep Urol 2020 17;12:403-413. Epub 2020 Sep 17.

Department of Molecular Biology and Genetics, Federal Research and Clinical Center of Physical-Chemical Medicine of Federal Medical Biological Agency, Moscow, Russia.

Purpose: Preclinical evaluation of and transcript simultaneous detection in urine to diagnose clinical significant prostate cancer (prostate cancer with Gleason score ≥7) in a Russian cohort.

Patients And Methods: We analyzed urine samples of patients with a total serum PSA ≥2 ng/mL: 31 men with prostate cancer scheduled for radical prostatectomy, 128 men scheduled for first diagnostic biopsy (prebiopsy cohort). , , and transcripts were detected by multiplex reverse transcription quantitative polymerase chain reaction, and the results were used for scores for calculation and statistical analysis.

Results: There was no significant difference between clinically significant and nonsignificant prostate cancer PCA3 scores. However, there was a significant difference in the AMACR score (patients scheduled for radical prostatectomy =0.0088, prebiopsy cohort =0.029). We estimated AUCs, optimal cutoffs, sensitivities and specificities for PCa and csPCa detection in the prebiopsy cohort by tPSA, PCA3 score, PCPT Risk Calculator and classification models based on tPSA, PCA3 score and AMACR score. In the clinically significant prostate cancer ROC analysis, the PCA3 score AUC was 0.632 (95%CI: 0.511-0.752), the AMACR score AUC was 0.711 (95%CI: 0.617-0.806) and AUC of classification model based on the score, the AMACR score and total PSA was 0.72 (95%CI: 0.58-0.83). In addition, the correlation of the AMACR score with the ratio of total RNA and RNA of prostate cells in urine was shown (tau=0.347, =6.542e-09). Significant amounts of nonprostate RNA in urine may be a limitation for the AMACR score use.

Conclusion: The AMACR score is a good predictor of clinically significant prostate cancer. Significant amounts of nonprostate RNA in urine may be a limitation for the AMACR score use. Evaluation of the AMACR score and classification models based on it for clinically significant prostate cancer detection with larger samples and a follow-up analysis is promising.
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http://dx.doi.org/10.2147/RRU.S262310DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7505712PMC
September 2020

Plasma metabolomic profile in prostatic intraepithelial neoplasia and prostate cancer and associations with the prostate-specific antigen and the Gleason score.

Metabolomics 2020 06 17;16(7):74. Epub 2020 Jun 17.

Laboratory of Pharmacokinetics and Metabolomic Analysis, Institute of Translational Medicine and Biotechnology, I.M. Sechenov First Moscow State Medical University, 2-4 Bolshaya Pirogovskaya St., Moscow, Russia, 119991.

Introduction: The metabolic alterations reflecting the influence of prostate cancer cells can be captured through metabolomic profiling.

Objective: To characterize the plasma metabolomic profile in prostatic intraepithelial neoplasia (PIN) and prostate cancer (PCa).

Methods: Metabolomics analyses were performed in plasma samples from individuals classified as non-cancerous control (n = 36), with PIN (n = 16), or PCa (n = 27). Untargeted [26 moieties identified after pre-processing by gas chromatography/mass spectrometry (GC/MS)] and targeted [46 amino acids, carbohydrates, organic acids and fatty acids by GC/MS, and 16 nucleosides and amino acids by ultra performance liquid chromatography-triple quadrupole/mass spectrometry (UPLC-TQ/MS)] analyses were performed. Prostate specific antigen (PSA) concentrations were measured in all samples. In PCa patients, the Gleason scores were determined.

Results: The metabolites that were best discriminated (p < 0.05, FDR < 0.2) for the Kruskal-Wallis test with Dunn's post-hoc comparing the control versus the PIN and PCa groups included isoleucine, serine, threonine, cysteine, sarcosine, glyceric acid, among several others. PIN was mainly characterized by alterations on steroidogenesis, glycine and serine metabolism, methionine metabolism and arachidonic acid metabolism, among others. In the case of PCa, the most predominant metabolic alterations were ubiquinone biosynthesis, catecholamine biosynthesis, thyroid hormone synthesis, porphyrin and purine metabolism. In addition, we identified metabolites that were correlated to the PSA [i.e. hypoxanthine (r = - 0.60, p < 0.05; r = - 0.54, p < 0.01) and uridine (r = - 0.58, p < 0.05; r = - 0.50, p < 0.01) in PIN and PCa groups, respectively] and metabolites that were significantly different in PCa patients with Gleason score < 7 and ≥ 7 [i.e. arachidonic acid, median (P25-P75) = 883.0 (619.8-956.4) versus 570.8 (505.6-651.8), respectively (p < 0.01)].

Conclusions: This human plasma metabolomic assessment contributes to the understanding of the unique metabolic features exhibited in PIN and PCa and provides a list of metabolites that can have the potential to be used as biomarkers for early detection of disease progression and management.
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http://dx.doi.org/10.1007/s11306-020-01694-yDOI Listing
June 2020

Plasma Sarcosine Measured by Gas Chromatography-Mass Spectrometry Distinguishes Prostatic Intraepithelial Neoplasia and Prostate Cancer from Benign Prostate Hyperplasia.

Lab Med 2020 Nov;51(6):566-573

Laboratory of Pharmacokinetics and Metabolomic Analysis, Institute of Translational Medicine and Biotechnology, I.M. Sechenov First Moscow State Medical University, Moscow, Russia.

Objective: Sarcosine was postulated in 2009 as a biomarker for prostate cancer (PCa). Here, we assess plasma sarcosine as a biomarker that is complementary to prostate-specific antigen (PSA).

Methods: Plasma sarcosine was measured using gas chromatography-mass spectrometry (GC-MS) in adults classified as noncancerous controls (with benign prostate hyperplasia [BPH], n = 36), with prostatic intraepithelial neoplasia (PIN, n = 16), or with PCa (n = 27). Diagnostic accuracy was assessed using receiver operating characteristic curve analysis.

Results: Plasma sarcosine levels were higher in the PCa (2.0 µM [1.3-3.3 µM], P <.01) and the PIN (1.9 µM [1.2-6.5 µM], P <.001) groups than in the BPH (0.9 µM [0.6-1.4 µM]) group. Plasma sarcosine had "good" and "very good" discriminative capability to detect PIN (area under the curve [AUC], 0.734) and PCa (AUC, 0.833) versus BPH, respectively. The use of PSA and sarcosine together improved the overall diagnostic accuracy to detect PIN and PCa versus BPH.

Conclusion: Plasma sarcosine measured by GC-MS had "good" and "very good" classification performance for distinguishing PIN and PCa, respectively, relative to noncancerous patients diagnosed with BPH.
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http://dx.doi.org/10.1093/labmed/lmaa008DOI Listing
November 2020

Revelation of Proteomic Indicators for Colorectal Cancer in Initial Stages of Development.

Molecules 2020 Jan 31;25(3). Epub 2020 Jan 31.

V.N. Orekhovich Institute of Biomedical Chemistry, 119121 Moscow, Russia.

Colorectal cancer (CRC) at a current clinical level is still hardly diagnosed, especially with regard to nascent tumors, which are typically asymptotic. Searching for reliable biomarkers of early diagnosis is an extremely essential task. Identification of specific post-translational modifications (PTM) may also significantly improve net benefits and tailor the process of CRC recognition. We examined depleted plasma samples obtained from 41 healthy volunteers and 28 patients with CRC at different stages to conduct comparative proteome-scaled analysis. The main goal of the study was to establish a constellation of protein markers in combination with their PTMs and semi-quantitative ratios that may support and realize the distinction of CRC until the disease has a poor clinical manifestation. Proteomic analysis revealed 119 and 166 proteins for patients in stages I-II and III-IV, correspondingly. Plenty of proteins (44 proteins) reflected conditions of the immune response, lipid metabolism, and response to stress, but only a small portion of them were significant ( < 0.01) for distinguishing stages I-II of CRC. Among them, some cytokines (Clusterin (CLU), C4b-binding protein (C4BP), and CD59 glycoprotein (CD59), etc.) were the most prominent and the lectin pathway was specifically enhanced in patients with CRC. Significant alterations in Inter-alpha-trypsin inhibitor heavy chains (ITIH1, ITIH2, ITIH3, and ITIH4) levels were also observed due to their implication in tumor growth and the malignancy process. Other markers (Alpha-1-acid glycoprotein 2 (ORM2), Alpha-1B-glycoprotein (A1BG), Haptoglobin (HP), and Leucine-rich alpha-2-glycoprotein (LRG1), etc.) were found to create an ambiguous core involved in cancer development but also to exactly promote tumor progression in the early stages. Additionally, we identified post-translational modifications, which according to the literature are associated with the development of colorectal cancer, including kininogen 1 protein (T327-p), alpha-2-HS-glycoprotein (S138-p) and newly identified PTMs, i.e., vitamin D-binding protein (K75-ac and K370-ac) and plasma protease C1 inhibitor (Y294-p), which may also contribute and negatively impact on CRC progression. The contribution of cytokines and proteins of the extracellular matrix is the most significant factor in CRC development in the early stages. This can be concluded since tumor growth is tightly associated with chronic aseptic inflammation and concatenated malignancy related to loss of extracellular matrix stability. Due attention should be paid to Apolipoprotein E (APOE), Apolipoprotein C1 (APOC1), and Apolipoprotein B-100 (APOB) because of their impact on the malfunction of DNA repair and their capability to regulate mTOR and PI3K pathways. The contribution of the observed PTMs is still equivocal, but a significant decrease in the likelihood between modified and native proteins was not detected confidently.
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http://dx.doi.org/10.3390/molecules25030619DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7036866PMC
January 2020
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