Dataset Information

Predicting Diagnostic Potential of Cathepsin in Epithelial Ovarian Cancer: A Design Validated by Computational, Biophysical and Electrochemical Data.

ABSTRACT: Epithelial ovarian cancer remains one of the leading variants of gynecological cancer with a high mortality rate. Feasibility and technical competence for screening and detection of epithelial ovarian cancer remain a major obstacle and the development of point of care diagnostics (POCD) may offer a simple solution for monitoring its progression. Cathepsins have been implicated as biomarkers for cancer progression and metastasis; being a protease, it has an inherent tendency to interact with Cystatin C, a cysteine protease inhibitor. This interaction was assessed for designing a POCD module. A combinatorial approach encompassing computational, biophysical and electron-transfer kinetics has been used to assess this protease-inhibitor interaction. Calculations predicted two cathepsin candidates, Cathepsin K and Cathepsin L based on their binding energies and structural alignment and both predictions were confirmed experimentally. Differential pulse voltammetry was used to verify the potency of Cathepsin K and Cathepsin L interaction with Cystatin C and assess the selectivity and sensitivity of their electrochemical interactions. Electrochemical measurements indicated selectivity for both the ligands, but with increasing concentrations, there was a marked difference in the sensitivity of the detection. This work validated the utility of dry-lab integration in the wet-lab technique to generate leads for the design of electrochemical diagnostics for epithelial ovarian cancer.

SUBMITTER: Ranade H

PROVIDER: S-EPMC8774009 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature

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Predicting Diagnostic Potential of Cathepsin in Epithelial Ovarian Cancer: A Design Validated by Computational, Biophysical and Electrochemical Data.

Ranade Hemangi H Paliwal Priya P Chaudhary Anis Ahmad AA Piplani Sakshi S Rudayni Hassan Ahmed HA Al-Zharani Mohammed M Niraj Ravi Ranjan RR Datta Manali M

Biomolecules 20211230 1

<h4>Background</h4>Epithelial ovarian cancer remains one of the leading variants of gynecological cancer with a high mortality rate. Feasibility and technical competence for screening and detection of epithelial ovarian cancer remain a major obstacle and the development of point of care diagnostics (POCD) may offer a simple solution for monitoring its progression. Cathepsins have been implicated as biomarkers for cancer progression and metastasis; being a protease, it has an inherent tendency to ...[more]

PMID: 35053201

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Project description:BackgroundEpithelial ovarian cancer (EOC) is one of the most lethal gynecological malignancies among women worldwide. Early diagnosis of EOC could help in ovarian cancer management. MicroRNAs, a class of small non-coding RNA molecules, are known to be involved in post-transcriptional regulation of ~60% of human genes. Aberrantly expressed miRNAs associated with disease progression are confined in lipid or lipoprotein and secreted as extracellular miRNA in body fluid such as plasma, serum, and urine. MiRNAs are stably present in the circulation and recently have gained an importance to serve as a minimally invasive biomarker for early detection of epithelial ovarian cancer.MethodsGenome-wide methylation pattern of six EOC and two normal ovarian tissue samples revealed differential methylation regions of miRNA gene promoter through MeDIP-NGS sequencing. Based on log2FC and p-value, three hypomethylated miRNAs (miR-205, miR-200c, and miR-141) known to have a potential role in ovarian cancer progression were selected for expression analysis through qRT-PCR. The expression of selected miRNAs was analyzed in 115 tissue (85 EOC, 30 normal) and 65 matched serum (51 EOC and 14 normal) samples.ResultsAll three miRNAs (miR-205, miR-200c, and miR-141) showed significantly higher expression in both tissue and serum cohorts when compared with normal controls (p < 0.0001). The receiver operating characteristic curve analysis of miR-205, miR-200c, and miR-141 has area under the curve (AUC) values of 87.6 (p < 0.0001), 78.2 (p < 0.0001), and 86.0 (p < 0.0001), respectively; in advance-stage serum samples, however, ROC has AUC values of 88.1 (p < 0.0001), 78.9 (p < 0.0001), and 86.7 (p < 0.0001), respectively, in early-stage serum samples. The combined diagnostic potential of the three miRNAs in advance-stage serum samples and early-stage serum samples has AUC values of 95.9 (95% CI: 0.925-1.012; sensitivity = 96.6% and specificity = 80.0%) and 98.1 (95% CI: 0.941-1.021; sensitivity = 90.5% and specificity = 100%), respectively.ConclusionOur data correlate the epigenetic deregulation of the miRNA genes with their expression. In addition, the miRNA panel (miR-205 + miR-200c + miR-141) has a much higher AUC, sensitivity, and specificity to predict EOC at an early stage in both tissue and serum samples.

Project description:BACKGROUND:Prior studies suggested that glycans were differentially expressed in patients with ovarian cancer and controls. We hypothesized that glycan-based biomarkers might serve as a diagnostic test for ovarian cancer and evaluated the ability of glycans to distinguish ovarian cancer cases from matched controls. METHODS:Serum samples were obtained from the tissue-banking repository of the Gynecologic Oncology Group, and included healthy female controls (n = 100), women diagnosed with low malignant potential (LMP) tumors (n = 52), and epithelial ovarian cancers (EOC) cases (n = 147). Cases and controls were matched on age at enrollment within ±5 years. Serum samples were analyzed by glycomics analysis to detect abundance differences in glycan expression levels. A two-stage procedure was carried out for biomarker discovery and validation. Candidate classifiers of glycans that separated cases from controls were developed using a training set in the discovery phase and the classification performance of the candidate classifiers was assessed using independent test samples that were not used in discovery. RESULTS:The patterns of glycans showed discriminatory power for distinguishing EOC and LMP cases from controls. Candidate glycan-based biomarkers developed on a training set (sensitivity, 86% and specificity, 95.8% for distinguishing EOC from controls through leave-one-out cross-validation) confirmed their potential use as a detection test using an independent test set (sensitivity, 70% and specificity, 86.5%). CONCLUSION:Formal investigations of glycan biomarkers that distinguish cases and controls show great promise for an ovarian cancer diagnostic test. Further validation of a glycan-based test for detection of ovarian cancer is warranted. IMPACT:An emerging diagnostic test based on the knowledge gained from understanding the glycobiology should lead to an assay that improves sensitivity and specificity and allows for early detection of ovarian cancer.

Dataset Information

Predicting Diagnostic Potential of Cathepsin in Epithelial Ovarian Cancer: A Design Validated by Computational, Biophysical and Electrochemical Data.

Publications

Predicting Diagnostic Potential of Cathepsin in Epithelial Ovarian Cancer: A Design Validated by Computational, Biophysical and Electrochemical Data.

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