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Computational Identification of Circulating MicroRNAs Associated with the Early Stage of Ovarian Cancer
Background: Ovarian cancer is the leading cause of death among gynaecological malignancies. Its high fatality rate is due to the difficulty of early detection. Current screening methods are inadequate, and therefore, a new less-invasive early detection method is urgently needed. MicroRNAs are promis...
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AUC Knowledge Fountain
2023
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| Summary: | Background: Ovarian cancer is the leading cause of death among gynaecological malignancies. Its high fatality rate is due to the difficulty of early detection. Current screening methods are inadequate, and therefore, a new less-invasive early detection method is urgently needed. MicroRNAs are promising biomarker candidates for cancer detection, however, their potential for early detection use is understudied. Aim of the work: We sought to identify sensitive and selective microRNA markers for early diagnosis of ovarian cancer from blood serum samples through computational analysis. Methods: In this investigation, we used bioinformatic methods to analyze MicroRNA-Seq data obtained from public databases for a total of 899 samples from patients diagnosed with different stages of ovarian cancer, other cancer types, other non-cancer diseases, as well as healthy controls. The analysis targeted computational identification of circulating microRNAs associated with the early stage of ovarian cancer. We performed differential gene expression analysis, enrichment analysis, and functional annotation analysis. Finally, we built a classifier model to measure the specificity and sensitivity of the diagnostic potential of our selected microRNAs using cross-validation techniques. Results: We identified 9 and 27 microRNA sets with distinct expression patterns for the early and late stages of ovarian cancer, respectively. We conducted a functional annotation analysis of the microRNA sets to select the most statistically significant microRNAs with downstream target genes associated with Ovarian Cancer. Among the early 9 microRNAs, we selected miR-29b-3p, miR-19b-3p, and miR-30e-5p as the most significant. Our classifier model was able to distinguish between early-stage from late-stage ovarian cancer patients from healthy controls (area under the receiver operating characteristic curve, 0.991; true positive rate, 0.917; true negative rate, 0.959). Conclusion: The circulating microRNAs discovered using this procedure have the potential to function as quick, affordable, and non-invasive diagnostic biomarkers for ovarian cancer in its early stages. This approach was effective in differentiating early-stage from late-stage ovarian cancer patients from healthy controls. Early detection of ovarian cancer is critical for devising an effective treatment plan, which leads to improved patient survival outcomes. |
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